Machine Learning Engineer With Python

Discover the machine learning fundamentals and explore how machine learning is changing the world. Join the ML revolution today! The advance course will usher you into the cutting-edge field of machine learning engineering with the comprehensive track designed for aspiring professionals.

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Machine Learning Engineer With Python Course

Who this course is for:

If you want to become a machine learning engineer then this course is for you.
If you want to learn the basics of Python then this courses is for.

If you want something beyond the typical lecture style course then this course is for you.

What you will Learn:

You'll learn everything you need to know about Python for authoring basic machine learning models.
You'll work through hands on labs that will test the skills you learned in the lessons.

You'll learn all the Python vernacular you need to take you skills to the next level.
You'll build a basic Deep Neural Network in Python line by line.

You'll use Scikit-Learn to Build a Traditional Machine Learning Model.
You'll understand why Python has become the Gold Standard in the Machine Learning Space. And much more.

Course Benefits & Key Features

Machine Learning Engineer With Python’s benefits and key features.

Modules

30+ Modules.

Lessons

80+ Lessons

Practical

40+ Hands-On Labs

Life Projects

5+ Projects

Resume

CV Preparations

Job

Jobs Reference

Recording

Session Recording

Interviews

Mock Interviews

Support

On Job Supports

Membership

Membership Access

Networks

Networking

Certification

Certificate of Completion

INSTRUCTOR-LED LIVE ONLINE CLASSES

Our learn-by-building-project method enables you to build

practical or coding experience that sticks. 95% of our

learners say they have confidence and remember more

when they learn by building real world projects which is

required to work in your real life.

Get step-by-step guidance to practice your skills without getting stuck
Validate your technical problem-solving skills in a real environment
Troubleshoot complex scenarios to practice what you learned
Develop production experience that translates into real-world

.

Python Developer Program Job Outlook

.

Ranked #1 Programming
Language

TIOBE and PYPL ranks Python as the most popular global programming language.

Python Salary
Trend

The average salary for a Python Developer is $114,489 per year in the United States.

44.8% Compound annual growth rate (CAGR)

The global python market size is expected to reach USD 100.6 million in 2030.

Machine Learning Engineer With Python?

Machine Learning Jobs Are Lucrative

They earn average salary of $148,485 in the U.S.. INDEED's numbers also show that one could earn up to $200,000 in one of the country’s larger markets. etc.

Demand for Machine Learning Engineering Skills Is High

Robert Half looking at the future of work revealed that 30 percent of surveyed U.S. managers said their company was currently using AI and ML, and 53 percent expected to adopt those tools within the next three to five years.

Opportunities for Continual Learning

Similar to Software Developers, ML Engineers by nature must value learning. And using courses, blogs, tutorials, and podcasts to stay on top of a young and rapidly changing field is essential.

They Live on the Cutting-Edge of Technology

It’s a great career for those who like finding practical applications for math. As a Machine Learning Engineer, you would likely be able to use linear algebra, calculus, probability, and statistics in your daily work.

Machine Learning Careers Offer Variety

If you’re the type to get bored, a Machine Learning career would feature plenty of diversity. You could join a team that makes the next great breakthrough in healthcare, marketing, or self-driving cars etc.

Become a Leader

Being a central part of an organization’s decision-making processes, analytics experts often pick up strong leadership skills as well.

GreaterHeight Certificates holders are prepared to work at companies like these.

Some Alumni Testimonies

Investing in the course "Become a Data Analyst" with GreaterHeight Academy is great value for the money and I highly recommend. The trainer is very knowledgeable, very engaging, provided us with quality training sessions on all courses and was easily acessible for queries. We also had access to the course materials and also the timely availability of the recorded videos made it easy and aided the learning process..

QUEEN OBIWULU

Team Lead, Customer Success

The training was fantastic, the instructor is an awesome lecturer, relentless and not tired in his delivery. He obviously enjoys teaching, it comes natural to him. We got more than we expected. He extended my knowledge of Excel beyond what I knew, and the courses were brilliantly delivered. They reach out, follow up, ask questions, and in fact the support has been great. They are highly recommended and I would definitely subscribe to other training programs from them.

BISOLA OGUNRO

Fraud Analytics Risk Oversight Manager

It's one thing to look for just a Data Analysis training, and it's another to get the knowledge transferred through certified professional trainers. No matter your initial level of proficiency in any of the Data Analysis tools, GreaterHeight Academy would meet you there and take you up to a highly proficienct and confident level in a short time at a reasonable pace. I learnt a lot of Data Analysis tools and skills at GreaterHeight from patient and resourceful teachers.

TUNDE MEREDITH

Operation Director - Abbfem Technology

The Data Analysis training program was one of the best I have attended. The way GreaterHeight took off with Excel and concluded the four courses with Excel was a mind blowing - it was WOW!! I concluded that I'm on the right path with the right mentor to take me from a novice to professional. GreaterHeight is the best as far as impacting Data Analysis knowledge is concern. I would shout it at the rooftop to recommend GreaterHeight to any trainee that really wants to learn.

JOHN OSI PETER

Greaterheight

I wanted to take a moment to express my deepest gratitude for the opportunity to study data analytics at GreaterHeight Academy. I am truly impressed by the level of dedication and support that the sponsor and CEO have put into this program. GreaterHeight Academy is without a doubt the best tech institution out there, providing top-notch education and resources for its students. One of the advantages of studying at GreaterHeight Academy is the access to the best tools and technologies in the field.

AYODELE PAYNE

Sales/Data Analyst

It is an unforgettable experience that will surely stand the test of time learning to become a Data Analyst with GreaterHeights Academy. The Lecture delivery was so impactful and the Trainer is vast and well knowledgeable in using the applicable tools for the Sessions. Always ready to go extra mile with you. The supports you get during and after the lectures are top notch with materials and resources available to build your confidence on and off the job.

ADEBAYO OLADEJO

Customer Service Advisor (Special Operations)

Machine Learning Fundamentals with SQL

Discover the machine learning fundamentals and explore how machine learning is changing the world. Join the ML revolution today! If you’re new to the discipline, this is an ideal place to start. You’ll cover the machine learning basics with Python, starting with supervised learning with the scikit-learn library. You’ll also learn how to cluster, transform, visualize, and extra insights from data using unsupervised learning and scipy. As you progress, you’ll explore the fundamentals of neural networks and deep learning models using PyTorch. You’ll finish the track by covering reinforcement learning, solving a myriad of problems as you go! By the time you’re finished, you’ll understand the essential machine learning concepts and be able to apply the fundamentals of machine learning with Python!

Supervised Learning with Scikit-learn

Grow your machine learning skills with scikit-learn and discover how to use this popular Python library to train models using labeled data. In this course, you'll learn how to make powerful predictions, such as whether a customer is will churn from your business, whether an individual has diabetes, and even how to tell classify the genre of a song. Using real-world datasets, you'll find out how to build predictive models, tune their parameters, and determine how well they will perform with unseen data.

4 Modules | 5+ Hours | 4+ Skills

Course Modules

In this module, you'll be introduced to classification problems and learn how to solve them using supervised learning techniques. You'll learn how to split data into training and test sets, fit a model, make predictions, and evaluate accuracy. You’ll discover the relationship between model complexity and performance, applying what you learn to a churn dataset, where you will classify the churn status of a telecom company's customers.

Machine learning with scikit-learn
Binary classification
The supervised learning workflow
The classification challenge
k-Nearest Neighbors: Fit
k-Nearest Neighbors: Predict
Measuring model performance
Train/test split + computing accuracy
Overfitting and underfitting
Visualizing model complexity

In this module, you will be introduced to regression, and build models to predict sales values using a dataset on advertising expenditure. You will learn about the mechanics of linear regression and common performance metrics such as R-squared and root mean squared error. You will perform k-fold cross-validation, and apply regularization to regression models to reduce the risk of overfitting.

Introduction to regression
Creating features
Building a linear regression model
Visualizing a linear regression model
The basics of linear regression
Fit and predict for regression
Regression performance
Cross-validation
Cross-validation for R-squared
Analyzing cross-validation metrics
Regularized regression
Regularized regression: Ridge
Lasso regression for feature importance

Having trained models, now you will learn how to evaluate them. In this module, you will be introduced to several metrics along with a visualization technique for analyzing classification model performance using scikit-learn. You will also learn how to optimize classification and regression models through the use of hyperparameter tuning.

How good is your model?
Deciding on a primary metric
Assessing a diabetes prediction classifier
Logistic regression and the ROC curve
Building a logistic regression model
The ROC curve
ROC AUC
Hyperparameter tuning
Hyperparameter tuning with GridSearchCV
Hyperparameter tuning with RandomizedSearchCV

Learn how to impute missing values, convert categorical data to numeric values, scale data, evaluate multiple supervised learning models simultaneously, and build pipelines to streamline your workflow!

Preprocessing data
Creating dummy variables
Regression with categorical features
Handling missing data
Dropping missing data
Pipeline for song genre prediction: I
Pipeline for song genre prediction: II
Centering and scaling
Centering and scaling for regression
Centering and scaling for classification
Evaluating multiple models
Visualizing regression model performance
Predicting on the test set
Visualizing classification model performance
Pipeline for predicting song popularity
Wrap-up!

Unsupervised Learning in Python

Say you have a collection of customers with a variety of characteristics such as age, location, and financial history, and you wish to discover patterns and sort them into clusters. Or perhaps you have a set of texts, such as Wikipedia pages, and you wish to segment them into categories based on their content. This is the world of unsupervised learning, called as such because you are not guiding, or supervising, the pattern discovery by some prediction task, but instead uncovering hidden structure from unlabeled data. Unsupervised learning encompasses a variety of techniques in machine learning, from clustering to dimension reduction to matrix factorization. In this course, you'll learn the fundamentals of unsupervised learning and implement the essential algorithms using scikit-learn and SciPy. You will learn how to cluster, transform, visualize, and extract insights from unlabeled datasets, and end the course by building a recommender system to recommend popular musical artists.

4 Modules | 5+ Hours | 4 Skills

Course Modules

Learn how to discover the underlying groups (or "clusters") in a dataset. By the end of this module, you'll be clustering companies using their stock market prices, and distinguishing different species by clustering their measurements.

Unsupervised Learning
How many clusters?
Clustering 2D points
Inspect your clustering
Evaluating a clustering
How many clusters of grain?
Evaluating the grain clustering
Transforming features for better clusterings
Scaling fish data for clustering
Clustering the fish data
Clustering stocks using KMeans
Which stocks move together?

In this module, you'll learn about two unsupervised learning techniques for data visualization, hierarchical clustering and t-SNE. Hierarchical clustering merges the data samples into ever-coarser clusters, yielding a tree visualization of the resulting cluster hierarchy. t-SNE maps the data samples into 2d space so that the proximity of the samples to one another can be visualized.

Visualizing hierarchies
How many merges?
Hierarchical clustering of the grain data
Hierarchies of stocks
Cluster labels in hierarchical clustering
Which clusters are closest?
Different linkage, different hierarchical clustering!
Intermediate clusterings
Extracting the cluster labels
t-SNE for 2-dimensional maps
t-SNE visualization of grain dataset
A t-SNE map of the stock market

Dimension reduction summarizes a dataset using its common occuring patterns. In this module, you'll learn about the most fundamental of dimension reduction techniques, "Principal Component Analysis" ("PCA"). PCA is often used before supervised learning to improve model performance and generalization. It can also be useful for unsupervised learning. For example, you'll employ a variant of PCA will allow you to cluster Wikipedia articles by their content!

Visualizing the PCA transformation
Correlated data in nature
Decorrelating the grain measurements with PCA
Principal components
Intrinsic dimension
The first principal component
Variance of the PCA features
Intrinsic dimension of the fish data
Dimension reduction with PCA
Dimension reduction of the fish measurements
A tf-idf word-frequency array
Clustering Wikipedia part I
Clustering Wikipedia part II

In this module, you'll learn about a dimension reduction technique called "Non-negative matrix factorization" ("NMF") that expresses samples as combinations of interpretable parts. For example, it expresses documents as combinations of topics, and images in terms of commonly occurring visual patterns. You'll also learn to use NMF to build recommender systems that can find you similar articles to read, or musical artists that match your listening history!

Non-negative matrix factorization (NMF)
Non-negative data
NMF applied to Wikipedia articles
NMF features of the Wikipedia articles
NMF reconstructs samples
NMF learns interpretable parts
NMF learns topics of documents
Explore the LED digits dataset
NMF learns the parts of images
PCA doesn't learn parts
Building recommender systems using NMF
Which articles are similar to 'Cristiano Ronaldo'?
Recommend musical artists part I
Recommend musical artists part II
Final thoughts!

Introduction to Deep Learning with PyTorch

Understanding the power of Deep Learning

Deep learning is everywhere: in smartphone cameras, voice assistants, and self-driving cars. It has even helped discover protein structures and beat humans at the game of Go. Discover this powerful technology and learn how to leverage it using PyTorch, one of the most popular deep learning libraries.

Train your first neural network

First, tackle the difference between deep learning and "classic" machine learning. You will learn about the training process of a neural network and how to write a training loop. To do so, you will create loss functions for regression and classification problems and leverage PyTorch to calculate their derivatives.

Evaluate and improve your model

In the second half, learn the different hyperparameters you can adjust to improve your model. After learning about the different components of a neural network, you will be able to create larger and more complex architectures. To measure your model performances, you will leverage TorchMetrics, a PyTorch library for model evaluation.

Upon completion, you will be able to leverage PyTorch to solve classification and regression problems on both tabular and image data using deep learning. A vital capability for experienced data professionals looking to advance their careers!

4 Modules | 5+ Hours | 4 Skills

Course Modules

Self-driving cars, smartphones, search engines... Deep learning is now everywhere. Before you begin building complex models, you will become familiar with PyTorch, a deep learning framework. You will learn how to manipulate tensors, create PyTorch data structures, and build your first neural network in PyTorch.

Introduction to deep learning with PyTorch
Getting started with PyTorch tensors
Checking and adding tensors
Creating our first neural network
Your first neural network
Stacking linear layers
Discovering activation functions
Activate your understanding!
The sigmoid and softmax functions

To train a neural network in PyTorch, you will first need to understand the job of a loss function. You will then realize that training a network requires minimizing that loss function, which is done by calculating gradients. You will learn how to use these gradients to update your model's parameters, and finally, you will write your first training loop.

Running a forward pass
Building a binary classifier in PyTorch
From regression to multi-class classification
Using loss functions to assess model predictions
Creating one-hot encoded labels
Calculating cross entropy loss
Using derivatives to update model parameters
Accessing the model parameters
Updating the weights manually
Using the PyTorch optimizer
Writing our first training loop
Using the MSELoss
Writing a training loop

Hyperparameters are parameters, often chosen by the user, that control model training. The type of activation function, the number of layers in the model, and the learning rate are all hyperparameters of neural network training. Together, we will discover the most critical hyperparameters of a neural network and how to modify them.

Discovering activation functions between layers
Implementing ReLU
Implementing leaky ReLU
Understanding activation functions
A deeper dive into neural network architecture
Counting the number of parameters
Manipulating the capacity of a network
Learning rate and momentum
Experimenting with learning rate
Experimenting with momentum
Layer initialization and transfer learning
Fine-tuning process
Freeze layers of a model
Layer initialization

Training a deep learning model is an art, and to make sure our model is trained correctly, we need to keep track of certain metrics during training, such as the loss or the accuracy. We will learn how to calculate such metrics and how to reduce overfitting using an image dataset as an example.

A deeper dive into loading data
Using the TensorDataset class
From data loading to running a forward pass
Evaluating model performance
Writing the evaluation loop
Calculating accuracy using torchmetrics
Fighting overfitting
Experimenting with dropout
Understanding overfitting
Improving model performance
Implementing random search
Wrap-up!

Reinforcement Learning with Gymnasium in Python

Discover the World of Reinforcement Learning

Embark on an exhilarating exploration of Reinforcement Learning (RL), a pivotal branch of machine learning. This interactive course takes you on a comprehensive journey through the core principles of RL where you'll master the art of training intelligent agents, teaching them to make strategic decisions and maximize rewards.

Master Essential Concepts and Tools

Your adventure starts with a deep dive into the unique aspects of RL. You'll not only learn foundational RL concepts but also apply key RL algorithms to practical scenarios using the renowned OpenAI Gym toolkit. This hands-on approach ensures a thorough grasp of RL essentials.

Navigate Through Advanced Strategies and Applications

As your journey unfolds, you'll venture into the realms of advanced RL strategies to discover the intricacies of Monte Carlo methods, Temporal Difference Learning, and Q-Learning. By mastering these techniques in Python, you'll be adept at training agents for a variety of complex tasks.

Transform Your Learning into Real-World Impact

Concluding this course, you'll emerge with a profound understanding of RL theory, equipped with the skills to apply it creatively in real-world contexts. You'll be ready to build RL models in Python, unlocking a world of possibilities in your projects and professional endeavors.

4 Modules | 5+ Hours | 4 Skills

Course Modules

Dive into the exciting world of Reinforcement Learning (RL) by exploring its foundational concepts, roles, and applications. Navigate through the RL framework, uncovering the agent-environment interaction. You'll also learn how to use the Gymnasium library to create environments, visualize states, and perform actions, thus gaining a practical foundation in RL concepts and applications.

Fundamentals of reinforcement learning
What is Reinforcement Learning?
RL vs. other ML sub-domains
Scenarios for applying RL
Navigating the RL framework
RL interaction loop
Episodic and continuous RL tasks
Calculating discounted returns for agent strategies
Interacting with Gymnasium environments
Setting up a Mountain Car environment
Visualizing the Mountain Car Environment
Interacting with the Frozen Lake environment

Delve deeper into the world of RL focusing on model-based learning. Unravel the complexities of Markov Decision Processes (MDPs), understanding their essential components. Enhance your skill set by learning about policies and value functions. Gain expertise in policy optimization with policy iteration and value Iteration techniques.

Markov Decision Processes
Custom Frozen Lake MDP components
Exploring state and action spaces
Transition probabilities and rewards
Policies and state-value functions
Defining a deterministic policy
Computing state-values for a policy
Comparing policies
Action-value functions
Computing Q-values
Improving a policy
Policy iteration and value iteration
Applying policy iteration for optimal policy
Implementing value iteration

Embark on a journey through the dynamic realm of Model-Free Learning in RL. Get introduced to to the foundational Monte Carlo methods, and apply first-visit and every-visit Monte Carlo prediction algorithms. Transition into the world of Temporal Difference Learning, exploring the SARSA algorithm. Finally, dive into the depths of Q-Learning, and analyze its convergence in challenging environments.

Monte Carlo methods
Episode generation for Monte Carlo methods
Implementing first-visit Monte Carlo
Implementing every-visit Monte Carlo
Temporal difference learning
Implementing the SARSA update rule
Solving 8x8 Frozen Lake with SARSA
Q-learning
Implementing Q-learning update rule
Solving 8x8 Frozen Lake with Q-learning
Evaluating policy on a slippery Frozen Lake

Dive into advanced strategies in Model-Free RL, focusing on enhancing decision-making algorithms. Learn about Expected SARSA for more accurate policy updates and Double Q-learning to mitigate overestimation bias. Explore the Exploration-Exploitation Tradeoff, mastering epsilon-greedy and epsilon-decay strategies for optimal action selection. Tackle the Multi-Armed Bandit Problem, applying strategies to solve decision-making challenges under uncertainty.

Expected SARSA
Expected SARSA update rule
Applying Expected SARSA
Double Q-learning
Implementing double Q-learning update rule
Applying double Q-learning
Balancing exploration and exploitation
Defining epsilon-greedy function
Solving CliffWalking with epsilon greedy strategy
Solving CliffWalking with decayed epsilon-greedy strategy
Multi-armed bandits
Creating a multi-armed bandit
Solving a multi-armed bandit
Assessing convergence in a multi-armed bandit
Wrap-up!

Data Engineer with Python Course

Advance your journey to becoming a Data Engineer with our Python-focused track, which is ideal for those with foundational SQL knowledge from our Associate Data Engineer track. This track dives deeper into the world of data engineering, emphasizing Python's role in automating and optimizing data processes. Starting with an understanding of cloud computing, you'll progress through Python programming from basics to advanced topics, including data manipulation, cleaning, and analysis. Engage in hands-on projects to apply what you've learned in real-world scenarios. You'll explore efficient coding practices, software engineering principles, and version control with Git, preparing you for professional data engineering challenges. Introduction to data pipelines and Airflow will equip you with the skills to design, schedule, and monitor complex data workflows!

Understanding Cloud Computing

Learn About Cloud Computing

Every day, we interact with the cloud—whether it’s using Google Drive, apps like Salesforce, or accessing our favorite websites. Cloud computing has become the norm for many companies, but what exactly is the cloud, and why is everyone rushing to adopt it?

Designed for complete novices, this cloud computing course breaks down what the cloud is and explains terminology such as scalability, latency, and high-availability.

Understand the Cloud Computing Basics

You’ll start by looking at the very basics of cloud computing, learning why it’s growing in popularity, and what makes it such a powerful option. You’ll explore the different service models businesses can choose from and how they're implemented in different situations.

As this is a no-code course, you can learn about cloud computing at a more conceptual level, exploring ideas of data protection, the various cloud providers, and how organizations can use cloud deployment.

Discover the Advantages of Cloud Computing

This course will demonstrate the many advantages of cloud computing, including ease of remote collaboration, how there are no hardware limitations, and reliable disaster recovery.

As you progress, you'll also discover the range of tools provided by major cloud providers and look at cloud computing examples from Amazon Web Services (AWS), Microsoft Azure, and Google Cloud. By the end of this course, you'll be able to confidently explain how cloud tools can increase productivity and save money, as well as ask the right questions about how to optimize your use of cloud tools.

3 Modules | 4+ Hours | 3+ Skills

Course Modules

In this module, you’ll learn why cloud computing is growing in popularity, how it compares to an on-premise solution, and what makes it so powerful. Next, you’ll learn about the three different service models—IaaS, PaaS, and SaaS—and how they each satisfy a unique set of business requirements.

What is cloud computing?
Understanding the cloud
Cloud vs. on-premise
Cloud computing services
The power of the cloud
Primary cloud services
Key characteristics
Cloud service models
Outsourcing IT services
IaaS, PaaS, or SaaS?
Level of abstraction

Now that you understand the power of cloud computing, it’s time to discover how it’s implemented using one of three deployment methods—private, public, and hybrid. You'll then find out how data protection regulations can affect cloud infrastructure. Lastly, you’ll meet the important roles within an organization that can make your cloud deployment a reality.

Cloud deployment models
Private or public?
Pick the best model
Regulations on the cloud
Time limits on storing data
Personal data
Cloud computing roles
Microsoft cloud skills report
Cloud roles
In other tracks

In the final module, you’ll be introduced to the major cloud infrastructure players, including AWS, Microsoft Azure, and Google Cloud. You’ll become more familiar with their market positioning, the products they offer, and who their main customers are and how they use cloud computing.

An overview of providers
The big three
The risk of vendor lock-in
Amazon Web Services
AWS or not AWS!
NerdWallet
Microsoft Azure
Which service to pick?
The Ottawa hospital
Google Cloud
Lush migration
True or false?
Cloud providers and their services
Wrap-up!

Introduction to Python for Developers

What is Python and why use it?

Learn all about Python a versatile and powerful language, perfect for software development. No prior experience required!

Learn the fundamentals

Perform calculations, store and manipulate information in variables using various data structures, and write descriptive comments describing your code to others.

Build your workflow

Use comparison operators in combination with for and while loops to execute code based on conditions being met, enabling a fully customizable workflow.

3 Modules | 4+ Hours | 3+ Skills

Course Modules

Discover the wonders of Python - why it is popular and how to use it. No prior knowledge required!

What is Python?
The benefits of Python
Use-cases for Python
How to run Python code
Working with Python files
Python as a calculator
Advanced calculations
Variables and data types
Naming conventions
Checking data types
Working with variables
Checking and updating conditions

Learn how and when to use Python's built-in data structures, including lists, dictionaries, sets, and tuples!

Working with strings
Multi-line strings
Modifying string variables
Lists
Building a party playlist
Subsetting lists
Dictionaries
Building a playlist dictionary
Working with dictionaries
Sets and tuples
Last quarter's revenue
DJ Sets
Choosing a data structure

Conditional statements and operators, for and while loops all combine to enable customized workflows for your needs!

Conditional statements and operators
Conditional statements
Checking inflation
On the rental market
For loops
Looping through a list
Updating a variable with for loops
Conditional looping with a dictionary
While loops
Breaking a while loop
Converting to a while loop
Conditional while loops
Building a workflow
Appending to a list
Book genre popularity
Working with keywords
Recap!

Intermediate Python for Developers

Elevate your Python skills to the next level

This course will delve deeper into Python's rich ecosystem, focusing on essential aspects such as built-in functions, modules, and packages. You'll learn how to harness the power of Python's built-in functions effectively, enabling you to streamline your code. The course will introduce you to the power of Python's modules, empowering you to develop quicker by reusing existing code rather than writing your own from scratch every time! You'll see how people have extended modules to create their own open-source software, known as packages, discovering how to download, import, and work with packages in your programs.

Master custom functions

You'll learn best practices for defining functions, including comprehensive knowledge of how to write user-friendly docstrings to ensure clarity and maintainability. You'll dive into advanced concepts such as default arguments, enabling you to create versatile functions with predefined values. The course will equip you with the knowledge and skills to handle arbitrary positional and keyword arguments effectively, enhancing the flexibility and usability of your functions. By understanding how to work with these arguments, you'll be able to create more robust and adaptable solutions to various programming challenges.

Debug your code and use error handling techniques

You'll learn to interpret error messages, including tracebacks from incorrectly using functions from packages. You'll use keywords and techniques to adapt your custom functions, effectively handling errors and providing bespoke feedback messages to developers who misuse your code!

3 Modules | 3+ Hours | 3+ Skills

Course Modules

Discover Python's rich ecosystem of built-in functions and modules, plus how to download and work with packages.

Built-in functions
Get some assistance
Counting the elements
Performing calculations
Modules
What is a module?
Working with the string module
Importing from a module
Packages
Package or module?
Working with pandas
Performing calculations with pandas

Learn the fundamentals of functions, from Python's built-in functions to creating your own from scratch!

Defining a custom function
Custom function syntax
Cleaning text data
Building a password checker
Default and keyword arguments
Positional versus keyword arguments
Adding a keyword argument
Data structure converter function
Docstrings
Single-line docstrings
Multi-line docstrings
Arbitrary arguments
Adding arbitrary arguments
Arbitrary keyword arguments

Build lambda functions on the fly, and discover how to error-proof your code!

Lambda functions
Adding tax
Calling lambda in-line
Lambda functions with iterables
Introduction to errors
Debugging code
Module and package tracebacks
Fixing an issue
Error handling
Avoiding errors
Returning errors
Recap!

Introduction to Importing Data in Python

As a data scientist, you will need to clean data, wrangle and munge it, visualize it, build predictive models, and interpret these models. Before you can do so, however, you will need to know how to get data into Python. In this course, you'll learn the many ways to import data into Python: from flat files such as .txt and .csv; from files native to other software such as Excel spreadsheets, Stata, SAS, and MATLAB files; and from relational databases such as SQLite and PostgreSQL.

3 Modules | 4+ Hours | 3+ Skills

Course Modules

In this module, you'll learn how to import data into Python from all types of flat files, which are a simple and prevalent form of data storage. You've previously learned how to use NumPy and pandas—you will learn how to use these packages to import flat files and customize your imports.

Welcome to the course!
Importing entire text files
Importing text files line by line
The importance of flat files in data science
Pop quiz: what exactly are flat files?
Why we like flat files and the Zen of Python
Importing flat files using NumPy
Using NumPy to import flat files
Customizing your NumPy import
Importing different datatypes
Importing flat files using pandas
Using pandas to import flat files as DataFrames (1)
Using pandas to import flat files as DataFrames (2)
Customizing your pandas import
Final thoughts on data import

You've learned how to import flat files, but there are many other file types you will potentially have to work with as a data scientist. In this module, you'll learn how to import data into Python from a wide array of important file types. These include pickled files, Excel spreadsheets, SAS and Stata files, HDF5 files, a file type for storing large quantities of numerical data, and MATLAB files.

Introduction to other file types
Not so flat any more
Loading a pickled file
Listing sheets in Excel files
Importing sheets from Excel files
Customizing your spreadsheet import
Importing SAS/Stata files using pandas
How to import SAS7BDAT
Importing SAS files
Using read_stata to import Stata files
Importing Stata files
Importing HDF5 files
Using File to import HDF5 files
Using h5py to import HDF5 files
Extracting data from your HDF5 file
Importing MATLAB files
Loading .mat files
The structure of .mat in Python

In this module, you'll learn how to extract meaningful data from relational databases, an essential skill for any data scientist. You will learn about relational models, how to create SQL queries, how to filter and order your SQL records, and how to perform advanced queries by joining database tables.

Introduction to relational databases
Pop quiz: The relational model
Creating a database engine in Python
Creating a database engine
What are the tables in the database?
Querying relational databases in Python
The Hello World of SQL Queries!
Customizing the Hello World of SQL Queries
Filtering your database records using SQL's WHERE
Ordering your SQL records with ORDER BY
Querying relational databases directly with pandas
Pandas and The Hello World of SQL Queries!
Pandas for more complex querying
Advanced querying: exploiting table relationships
The power of SQL lies in relationships between tables: INNER JOIN
Filtering your INNER JOIN
Final Thoughts

Intermediate Importing Data in Python

As a data scientist, you will need to clean data, wrangle and munge it, visualize it, build predictive models and interpret these models. Before you can do so, however, you will need to know how to get data into Python. In the prequel to this course, you learned many ways to import data into Python: from flat files such as .txt and .csv; from files native to other software such as Excel spreadsheets, Stata, SAS, and MATLAB files; and from relational databases such as SQLite and PostgreSQL. In this course, you'll extend this knowledge base by learning to import data from the web and by pulling data from Application Programming Interfaces— APIs—such as the Twitter streaming API, which allows us to stream real-time tweets.

3 Modules | 4+ Hours | 3+ Skills

Course Modules

The web is a rich source of data from which you can extract various types of insights and findings. In this module, you will learn how to get data from the web, whether it is stored in files or in HTML. You'll also learn the basics of scraping and parsing web data.

Importing flat files from the web
Importing flat files from the web: your turn!
Opening and reading flat files from the web
Importing non-flat files from the web
HTTP requests to import files from the web
Performing HTTP requests in Python using urllib
Printing HTTP request results in Python using urllib
Performing HTTP requests in Python using requests
Scraping the web in Python
Parsing HTML with BeautifulSoup
Turning a webpage into data using BeautifulSoup: getting the text
Turning a webpage into data using BeautifulSoup: getting the hyperlinks

In this module, you will gain a deeper understanding of how to import data from the web. You will learn the basics of extracting data from APIs, gain insight on the importance of APIs, and practice extracting data by diving into the OMDB and Library of Congress APIs.

Introduction to APIs and JSONs
Pop quiz: What exactly is a JSON?
Loading and exploring a JSON
Pop quiz: Exploring your JSON
APIs and interacting with the world wide web
Pop quiz: What's an API?
API requests
JSON–from the web to Python
Checking out the Wikipedia API

In this module, you will consolidate your knowledge of interacting with APIs in a deep dive into the Twitter streaming API. You'll learn how to stream real-time Twitter data, and how to analyze and visualize it.

The Twitter API and Authentication
Streaming tweets
Load and explore your Twitter data
Twitter data to DataFrame
A little bit of Twitter text analysis
Plotting your Twitter data
Final Thoughts

Data Cleaning in Python

Discover How to Clean Data in Python

It's commonly said that data scientists spend 80% of their time cleaning and manipulating data and only 20% of their time analyzing it. Data cleaning is an essential step for every data scientist, as analyzing dirty data can lead to inaccurate conclusions.

In this course, you will learn how to identify, diagnose, and treat various data cleaning problems in Python, ranging from simple to advanced. You will deal with improper data types, check that your data is in the correct range, handle missing data, perform record linkage, and more!

Learn How to Clean Different Data Types

The first module of the course explores common data problems and how you can fix them. You will first understand basic data types and how to deal with them individually. After, you'll apply range constraints and remove duplicated data points.

The last module explores record linkage, a powerful tool to merge multiple datasets. You'll learn how to link records by calculating the similarity between strings. Finally, you'll use your new skills to join two restaurant review datasets into one clean master dataset.

Gain Confidence in Cleaning Data

By the end of the course, you will gain the confidence to clean data from various types and use record linkage to merge multiple datasets. Cleaning data is an essential skill for data scientists. If you want to learn more about cleaning data in Python and its applications, check out the following tracks: Data Scientist with Python and Importing & Cleaning Data with Python.

4 Modules | 5+ Hours | 4 Skills

Course Modules

In this module, you'll learn how to overcome some of the most common dirty data problems. You'll convert data types, apply range constraints to remove future data points, and remove duplicated data points to avoid double-counting.

Data type constraints
Common data types
Numeric data or ... ?
Summing strings and concatenating numbers
Data range constraints
Tire size constraints
Back to the future
Uniqueness constraints
How big is your subset?
Finding duplicates
Treating duplicates

Categorical and text data can often be some of the messiest parts of a dataset due to their unstructured nature. In this module, you’ll learn how to fix whitespace and capitalization inconsistencies in category labels, collapse multiple categories into one, and reformat strings for consistency.

Membership constraints
Members only
Finding consistency
Categorical variables
Categories of errors
Inconsistent categories
Remapping categories
Cleaning text data
Removing titles and taking names
Keeping it descriptive

In this module, you’ll dive into more advanced data cleaning problems, such as ensuring that weights are all written in kilograms instead of pounds. You’ll also gain invaluable skills that will help you verify that values have been added correctly and that missing values don’t negatively impact your analyses.

Uniformity
Ambiguous dates
Uniform currencies
Uniform dates
Cross field validation
Cross field or no cross field?
How's our data integrity?
Completeness
Is this missing at random?
Missing investors
Follow the money

Record linkage is a powerful technique used to merge multiple datasets together, used when values have typos or different spellings. In this module, you'll learn how to link records by calculating the similarity between strings—you’ll then use your new skills to join two restaurant review datasets into one clean master dataset.

Comparing strings
Minimum edit distance
The cutoff point
Remapping categories II
Generating pairs
To link or not to link?
Pairs of restaurants
Similar restaurants
Linking DataFrames
Getting the right index
Linking them together!
Wrap-up!

Writing Efficient Python Code

As a Data Scientist, the majority of your time should be spent gleaning actionable insights from data -- not waiting for your code to finish running. Writing efficient Python code can help reduce runtime and save computational resources, ultimately freeing you up to do the things you love as a Data Scientist. In this course, you'll learn how to use Python's built-in data structures, functions, and modules to write cleaner, faster, and more efficient code. We'll explore how to time and profile code in order to find bottlenecks. Then, you'll practice eliminating these bottlenecks, and other bad design patterns, using Python's Standard Library, NumPy, and pandas. After completing this course, you'll have the necessary tools to start writing efficient Python code!

4 Modules | 5+ Hours | 4 Skills

Course Modules

In this module, you'll learn what it means to write efficient Python code. You'll explore Python's Standard Library, learn about NumPy arrays, and practice using some of Python's built-in tools. This module builds a foundation for the concepts covered ahead.

Welcome!
Pop quiz: what is efficient
A taste of things to come
Zen of Python
Building with built-ins
Built-in practice: range()
Built-in practice: enumerate()
Built-in practice: map()
The power of NumPy arrays
Practice with NumPy arrays
Bringing it all together: Festivus!

In this module, you will learn how to gather and compare runtimes between different coding approaches. You'll practice using the line_profiler and memory_profiler packages to profile your code base and spot bottlenecks. Then, you'll put your learnings to practice by replacing these bottlenecks with efficient Python code.

Examining runtime
Using %timeit: your turn!
Using %timeit: specifying number of runs and loops
Using %timeit: formal name or literal syntax
Using cell magic mode (%%timeit)
Code profiling for runtime
Pop quiz: steps for using %lprun
Using %lprun: spot bottlenecks
Using %lprun: fix the bottleneck
Code profiling for memory usage
Pop quiz: steps for using %mprun
Using %mprun: Hero BMI
Using %mprun: Hero BMI 2.0
Bringing it all together: Star Wars profiling

This module covers more complex efficiency tips and tricks. You'll learn a few useful built-in modules for writing efficient code and practice using set theory. You'll then learn about looping patterns in Python and how to make them more efficient.

Efficiently combining, counting, and iterating
Combining Pokémon names and types
Counting Pokémon from a sample
Combinations of Pokémon
Set theory
Comparing Pokédexes
Searching for Pokémon
Gathering unique Pokémon
Eliminating loops
Gathering Pokémon without a loop
Pokémon totals and averages without a loop
Writing better loops
One-time calculation loop
Holistic conversion loop
Bringing it all together: Pokémon z-scores

This module offers a brief introduction on how to efficiently work with pandas DataFrames. You'll learn the various options you have for iterating over a DataFrame. Then, you'll learn how to efficiently apply functions to data stored in a DataFrame.

Intro to pandas DataFrame iteration
Iterating with .iterrows()
Run differentials with .iterrows()
Another iterator method: .itertuples()
Iterating with .itertuples()
Run differentials with .itertuples()
pandas alternative to looping
Analyzing baseball stats with .apply()
Settle a debate with .apply()
Optimal pandas iterating
Replacing .iloc with underlying arrays
Bringing it all together: Predict win percentage
Wrap up!

Streamlined Data Ingestion with pandas

Before you can analyze data, you first have to acquire it. This course teaches you how to build pipelines to import data kept in common storage formats. You’ll use pandas, a major Python library for analytics, to get data from a variety of sources, from spreadsheets of survey responses, to a database of public service requests, to an API for a popular review site. Along the way, you’ll learn how to fine-tune imports to get only what you need and to address issues like incorrect data types. Finally, you’ll assemble a custom dataset from a mix of sources.

4 Modules | 5+ Hours | 4 Skills

Course Modules

Practice using pandas to get just the data you want from flat files, learn how to wrangle data types and handle errors, and look into some U.S. tax data along the way.

Introduction to flat files
Get data from CSVs
Get data from other flat files
Modifying flat file imports
Import a subset of columns
Import a file in chunks
Handling errors and missing data
Specify data types
Set custom NA values
Skip bad data

Automate data imports from that staple of office life, Excel files. Import part or all of a workbook and ensure boolean and datetime data are properly loaded, all while learning about how other people are learning to code.

Introduction to spreadsheets
Get data from a spreadsheet
Load a portion of a spreadsheet
Getting data from multiple worksheets
Select a single sheet
Select multiple sheets
Work with multiple spreadsheets
Modifying imports: true/false data
Set Boolean columns
Set custom true/false values
Modifying imports: parsing dates
Parse simple dates
Get datetimes from multiple columns
Parse non-standard date formats

Combine pandas with the powers of SQL to find out just how many problems New Yorkers have with their housing. This module features introductory SQL topics like WHERE clauses, aggregate functions, and basic joins.

Introduction to databases
Connect to a database
Load entire tables
Refining imports with SQL queries
Selecting columns with SQL
Selecting rows
Filtering on multiple conditions
More complex SQL queries
Getting distinct values
Counting in groups
Working with aggregate functions
Loading multiple tables with joins
Joining tables
Joining and filtering
Joining, filtering, and aggregating

Learn how to work with JSON data and web APIs by exploring a public dataset and getting cafe recommendations from Yelp. End by learning some techniques to combine datasets once they have been loaded into data frames.

Introduction to JSON
Load JSON data
Work with JSON orientations
Introduction to APIs
Get data from an API
Set API parameters
Set request headers
Working with nested JSONs
Flatten nested JSONs
Handle deeply nested data
Combining multiple datasets
Concatenate dataframes
Merge dataframes
Wrap-up!

Learn Git

This course introduces learners to version control using Git. You will discover the importance of version control when working on data science projects and explore how you can use Git to track files, compare differences, modify and save files, undo changes, and allow collaborative development through the use of branches. You will gain an introduction to the structure of a repository, how to create new repositories and clone existing ones, and show how Git stores data. By working through typical data science tasks, you will gain the skills to handle conflicting files!

4 Modules | 5+ Hours | 4 Skills

Course Modules

In the first module, you’ll learn what version control is and why it is essential for data projects. Then, you’ll discover what Git is and how to use it for a version control workflow.

Introduction to version control with Git
Using the shell
Checking the version of Git
Saving files
Where does Git store information?
The Git workflow
Adding a file
Adding multiple files
Comparing files
What has changed?
What is going to be committed?
What's in the staging area?

Next, you’ll examine how Git stores data, learn essential commands to compare files and repositories at different times, and understand the process for restoring earlier versions of files in your data projects.

Storing data with Git
Interpreting the commit structure
Viewing a repository's history
Viewing a specific commit
Viewing changes
Comparing to the second most recent commit
Comparing commits
Who changed what?
Undoing changes before committing
How to unstage a file
Undoing changes to unstaged files
Undoing all changes
Restoring and reverting
Restoring an old version of a repo
Deleting untracked files
Restoring an old version of a file

In this module, you'll learn tips and tricks for configuring Git to make you more efficient! You'll also discover branches, identify how to create and switch to different branches, compare versions of files between branches, merge branches together, and deal with conflicting files across branches.

Configuring Git
Modifying your email address in Git
Creating an alias
Ignoring files
Branches
Branching and merging
Creating new branches
Checking the number of branches
Comparing branches
Working with branches
Switching branches
Merging two branches
Handling conflict
Recognizing conflict syntax
Resolving a conflict

This final module is all about collaboration! You'll gain an introduction to remote repositories and learn how to work with them to synchronize content between the cloud and your local computer. You'll also see how to create new repositories and clone existing ones, along with discovering a workflow to minimize the risk of conflicts between local and remote repositories.

Creating repos
Setting up a new repo
Converting an existing project
Working with remotes
Cloning a repo
Defining and identifying remotes
Gathering from a remote
Fetching from a remote
Pulling from a remote
Pushing to a remote
Pushing to a remote repo
Handling push conflicts
Wrap up!

Software Engineering Principles in Python

Data scientists can experience huge benefits by learning concepts from the field of software engineering, allowing them to more easily reutilize their code and share it with collaborators. In this course, you'll learn all about the important ideas of modularity, documentation, & automated testing, and you'll see how they can help you solve Data Science problems quicker and in a way that will make future you happy. You'll even get to use your acquired software engineering chops to write your very own Python package for performing text analytics.

4 Modules | 5+ Hours | 4 Skills

Course Modules

Why should you as a Data Scientist care about Software Engineering concepts? Here we'll cover specific Software Engineering concepts and how these important ideas can revolutionize your Data Science workflow!

Python, data science, & software engineering
The big ideas
Python modularity in the wild
Introduction to packages & documentation
Installing packages with pip
Leveraging documentation
Conventions and PEP 8
Using pycodestyle
Conforming to PEP 8
PEP 8 in documentation

Become a fully fledged Python package developer by writing your first package! You'll learn how to structure and write Python code that you can be installed, used, and distributed just like famous packages such as NumPy and Pandas.

Writing your first package
Minimal package requirements
Naming packages
Recognizing packages
Adding functionality to packages
Adding functionality to your package
Using your package's new functionality
Making your package portable
Writing requirements.txt
Installing package requirements
Creating setup.py
Listing requirements in setup.py

Object Oriented Programming is a staple of Python development. By leveraging classes and inheritance your Python package will become a much more powerful tool for your users.

Adding classes to a package
Writing a class for your package
Using your package's class
Adding functionality to classes
Writing a non-public method
Using your class's functionality
Classes and the DRY principle
Using inheritance to create a class
Adding functionality to a child class
Using your child class
Multilevel inheritance
Exploring with dir and help
Creating a grandchild class
Using inherited methods

You've now written a fully functional Python package for text analysis! To make maintaining your project as easy as possible we'll leverage best practices around concepts such as documentation and unit testing.

Documentation
Identifying good comments
Identifying proper docstrings
Writing docstrings
Readability counts
Using good function names
Using good variable names
Refactoring for readability
Unit testing
Using doctest
Using pytest
Documentation & testing in practice
Documenting classes for Sphinx
Identifying tools
Final Thoughts

ETL and ELT in Python

Empowering Analytics with Data Pipelines

Data pipelines are at the foundation of every strong data platform. Building these pipelines is an essential skill for data engineers, who provide incredible value to a business ready to step into a data-driven future. This introductory course will help you hone the skills to build effective, performant, and reliable data pipelines.

Building and Maintaining ETL Solutions

Throughout this course, you’ll dive into the complete process of building a data pipeline. You’ll grow skills leveraging Python libraries such as pandas and json to extract data from structured and unstructured sources before it’s transformed and persisted for downstream use. Along the way, you’ll develop confidence tools and techniques such as architecture diagrams, unit-tests, and monitoring that will help to set your data pipelines out from the rest. As you progress, you’ll put your new-found skills to the test with hands-on exercises.

Supercharge Data Workflows

After completing this course, you’ll be ready to design, develop and use data pipelines to supercharge your data workflow in your job, new career, or personal project.

4 Modules | 5+ Hours | 4 Skills

Course Modules

Get ready to discover how data is collected, processed, and moved using data pipelines. You will explore the qualities of the best data pipelines, and prepare to design and build your own.

Introduction to ETL and ELT Pipelines
Running an ETL Pipeline
ELT in Action
ETL and ELT Pipelines
Building ETL and ELT Pipelines
Building an ETL Pipeline
The "T" in ELT
Extracting, Transforming, and Loading Student Scores Data

Dive into leveraging pandas to extract, transform, and load data as you build your first data pipelines. Learn how to make your ETL logic reusable, and apply logging and exception handling to your pipelines.

Extracting data from structure sources
Extracting data from parquet files
Pulling data from SQL databases
Building functions to extract data
Transforming data with pandas
Filtering pandas DataFrames
Transforming sales data with pandas
Validating data transformations
Persisting data with pandas
Loading sales data to a CSV file
Customizing a CSV file
Persisting data to files
Monitoring a data pipeline
Logging within a data pipeline
Handling exceptions when loading data
Monitoring and alerting within a data pipeline

Supercharge your workflow with advanced data pipelining techniques, such as working with non-tabular data and persisting DataFrames to SQL databases. Discover tooling to tackle advanced transformations with pandas, and uncover best-practices for working with complex data.

Extracting non-tabular data
Ingesting JSON data with pandas
Reading JSON data into memory
Transforming non-tabular data
Iterating over dictionaries
Parsing data from dictionaries
Transforming JSON data
Transforming and cleaning DataFrames
Advanced data transformation with pandas
Filling missing values with pandas
Grouping data with pandas
Applying advanced transformations to DataFrames
Loading data to a SQL database with pandas
Loading data to a Postgres database
Validating data loaded to a Postgres Database

In this final module, you’ll create frameworks to validate and test data pipelines before shipping them into production. After you’ve tested your pipeline, you’ll explore techniques to run your data pipeline end-to-end, all while allowing for visibility into pipeline performance.

Manually testing a data pipeline
Testing data pipelines
Validating a data pipeline at "checkpoints"
Testing a data pipeline end-to-end
Unit-testing a data pipeline
Validating a data pipeline with assert
Writing unit tests with pytest
Creating fixtures with pytest
Unit testing a data pipeline with fixtures
Running a data pipeline in production
Orchestration and ETL tools
Data pipeline architecture patterns
Running a data pipeline end-to-end
Wrap-Up!

Introduction to Apache Airflow in Python

Now Updated to Apache Airflow 2.7 - Delivering data on a schedule can be a manual process. You write scripts, add complex cron tasks, and try various ways to meet an ever-changing set of requirements—and it's even trickier to manage everything when working with teammates. Apache Airflow can remove this headache by adding scheduling, error handling, and reporting to your workflows. In this course, you'll master the basics of Apache Airflow and learn how to implement complex data engineering pipelines in production. You'll also learn how to use Directed Acyclic Graphs (DAGs), automate data engineering workflows, and implement data engineering tasks in an easy and repeatable fashion—helping you to maintain your sanity.

4 Modules | 5+ Hours | 4 Skills

Course Modules

In this module, you’ll gain a complete introduction to the components of Apache Airflow and learn how and why you should use them.

Introduction to Apache Airflow
Testing a task in Airflow
Examining Airflow commands
Airflow DAGs
Defining a simple DAG
Working with DAGs and the Airflow shell
Troubleshooting DAG creation
Airflow web interface
Starting the Airflow webserver
Navigating the Airflow UI
Examining DAGs with the Airflow UI

What’s up DAG? Now it’s time to learn the basics of implementing Airflow DAGs. Through hands-on activities, you’ll learn how to set up and deploy operators, tasks, and scheduling.

Airflow operators
Defining a BashOperator task
Multiple BashOperators
Airflow tasks
Define order of BashOperators
Determining the order of tasks
Troubleshooting DAG dependencies
Additional operators
Using the PythonOperator
More PythonOperators
EmailOperator and dependencies
Airflow scheduling
Schedule a DAG via Python
Deciphering Airflow schedules
Troubleshooting DAG runs

In this module, you’ll learn how to save yourself time using Airflow components such as sensors and executors while monitoring and troubleshooting Airflow workflows.

Airflow sensors
Sensors vs operators
Sensory deprivation
Airflow executors
Determining the executor
Executor implications
Debugging and troubleshooting in Airflow
DAGs in the bag
Missing DAG
SLAs and reporting in Airflow
Defining an SLA
Defining a task SLA
Generate and email a report
Adding status emails

Put it all together. In this final module, you’ll apply everything you've learned to build a production-quality workflow in Airflow.

Working with templates
Creating a templated BashOperator
Templates with multiple arguments
More templates
Using lists with templates
Understanding parameter options
Sending templated emails
Branching
Define a BranchPythonOperator
Branch troubleshooting
Creating a production pipeline
Creating a production pipeline #1
Creating a production pipeline #2
Adding the final changes to your pipeline
Wrap-up!

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FAQs

Complete Data Analysis & Visualization with Python Course

Python, created by Guido van Rossum in 1991, is a high-level, readable programming language known for its simplicity. It's versatile, with applications in web development, data analysis, AI, and more. Python's extensive standard library and rich ecosystem enhance its capabilities. It's cross-platform compatible and supported by a large community. Python's popularity has grown, making it widely used in diverse industries.

A Python developer is a software developer or programmer who specializes in using the Python programming language for creating applications, software, or solutions. They have expertise in writing Python code, understanding the language's syntax, libraries, and frameworks. Python developers are skilled in utilizing Python's features to develop web applications, data analysis tools, machine learning models, automation scripts, and other software solutions.
They work in various industries, collaborating with teams or independently to design, implement, test, and maintain Python-based projects. Python developers often possess knowledge of related technologies and tools to enhance their development process.

Python Developer Masters Program is a structured learning path recommended by leading industry experts and ensures that you transform into a proficient Python Developer. Being a full fledged Python Developer requires you to master multiple technologies and this program aims at providing you an in-depth knowledge of the entire Python programming practices. Individual courses at GreaterHeight Academy focus on specialization in one or two specific skills; however, if you intend to become a master in Python programming then this is your go to path to follow.

Yes. But you can also raise a ticket with the dedicated support team at any time. If your query does not get resolved through email, we can also arrange one-on-one sessions with our support team. However, our support is provided for a period of Twelve Weeks from the start date of your course.

There are several reasons why becoming a Python developer can be a rewarding career choice. Here are a few:

Versatility and Popularity: Python is a versatile programming language that can be used for various purposes, such as web development, data analysis, machine learning, artificial intelligence, scientific computing, and more. It has gained immense popularity in recent years due to its simplicity, readability, and extensive library ecosystem. Python is widely used in both small-scale and large-scale projects, making it a valuable skill in the job market.
Ease of Learning: Python has a clean and intuitive syntax that emphasizes readability, which makes it relatively easy to learn compared to other programming languages. Its simplicity allows beginners to grasp the fundamentals quickly and start building useful applications in a relatively short amount of time. This accessibility makes Python an attractive choice for both novice and experienced programmers.
Rich Ecosystem and Libraries: Python offers a vast collection of libraries and frameworks that can accelerate development and simplify complex tasks. For example, Django and Flask are popular web development frameworks that provide robust tools for building scalable and secure web applications. NumPy, Pandas, and Matplotlib are widely used libraries for data analysis and visualization. TensorFlow and PyTorch are prominent libraries for machine learning and deep learning. These libraries, among many others, contribute to Python's efficiency and effectiveness as a development language.
Job Opportunities: The demand for Python developers has been steadily growing in recent years. Many industries, including technology, finance, healthcare, and academia, rely on Python for various applications. By becoming a Python developer, you open up a wide range of career opportunities, whether you choose to work for a large corporation, a startup, or even as a freelancer. Additionally, Python's versatility allows you to explore different domains and switch roles if desired.
Community and Support: Python has a vibrant and supportive community of developers worldwide. This community actively contributes to the language's development, creates open-source libraries, and provides assistance through forums, online communities, and resources.

There are no prerequisites for enrollment to this Masters Program. Whether you are an experienced professional working in the IT industry or an aspirant planning to enter the world of Python programming, this masters program is designed and developed to accommodate various professional backgrounds.

Python Developer Masters Program has been curated after thorough research and recommendations from industry experts. It will help you differentiate yourself with multi-platform fluency and have real-world experience with the most important tools and platforms. GreaterHeight Academy will be by your side throughout the learning journey - We’re Ridiculously Committed.

The recommended duration to complete this Python Developer Masters Program is about 20 weeks, however, it is up to the individual to complete this program at their own pace.

The roles and responsibilities of a Python developer may vary depending on the specific job requirements and industry. However, here are some common tasks and responsibilities associated with the role:

Developing Applications: Python developers are responsible for designing, coding, testing, and debugging applications using Python programming language. This includes writing clean, efficient, and maintainable code to create robust software solutions.
Web Development: Python is widely used for web development. As a Python developer, you may be involved in building web applications, using frameworks like Django or Flask. This includes developing backend logic, integrating databases, handling data processing, and ensuring the smooth functioning of the web application.
Data Analysis and Visualization: Python offers powerful libraries like NumPy, Pandas, and Matplotlib, which are extensively used for data analysis and visualization. Python developers may be responsible for manipulating and analyzing large datasets, extracting insights, and presenting them visually.
Machine Learning and AI: Python is a popular choice for machine learning and artificial intelligence projects. Python developers may work on implementing machine learning algorithms, training models, and integrating them into applications. This involves using libraries like TensorFlow, PyTorch, or scikit-learn.
Collaborating and Teamwork: Python developers often work as part of a development team. They collaborate with other team members, including designers, frontend developers, project managers, and stakeholders. Effective communication and teamwork skills are crucial to ensure smooth project execution.
Documentation: Python developers are expected to document their code, providing clear explanations and instructions for others who may work on or maintain the codebase in the future. Documentation helps in understanding the code and facilitating collaboration.
Continuous Learning: Technology is constantly evolving, and as a Python developer, you need to stay updated with the latest advancements, libraries, frameworks, and best practices. Continuous learning and self-improvement are essential to excel in this role.

The Python Developer training course is for those who want to fast-track their Python programming career. This Python Developer Masters Program will benefit people working in the following roles:

Freshers
Engineers
IT professionals
Data Scientist
Machine Learning Engineer
AI Engineer
Business analysts
Data analysts

Top companies such as Microsoft, Google, Meta, Citibank, Well Fargo, and many more are actively hiring certified Python professionals at various positions.

On completing this Python Developer Masters Program, you’ll be eligible for the roles like: Python Developer, Web Developer, Data Analyst, Data Scientist, Software Engineer and many more.

There is undoubtedly great demand for data analytics as 96% of organizations seek to hire Data Analysts. The most significant data analyst companies that employ graduates who wish to have a data analyst career are Manthan, SAP, Oracle, Accenture Analytics, Alteryx, Qlik, Mu Sigma Analytics, Fractal Analytics, and Tiger Analytics. Professional Data Analyst training will make you become a magician of any organization, and you will spin insights by playing with big data.

A successful data analyst possesses a combination of technical skills and leadership skills.

Technical skills include knowledge of database languages such as SQL, R, or Python; spreadsheet tools such as Microsoft Excel or Google Sheets for statistical analysis; and data visualization software such as Tableau or Qlik. Mathematical and statistical skills are also valuable to help gather, measure, organize, and analyze data while using these common tools.
Leadership skills prepare a data analyst to complete decision-making and problem-solving tasks. These abilities allow analysts to think strategically about the information that will help stakeholders make data-driven business decisions and to communicate the value of this information effectively. For example, project managers rely on data analysts to track the most important metrics for their projects, to diagnose problems that may be occurring, and to predict how different courses of action could address a problem.

Career openings are available practically from all industries, from telecommunications to retail, banking, healthcare, and even fitness. Without extensive training and effort, it isn't easy to get data analyst career benefits. So, earning our Data Analyst certification will allow you to keep up-to-date on recent trends in the industry.

Yes, we do. We will discuss all possible technical interview questions and answers during the training program so that you can prepare yourself for interview.

No. Any abuse of copyright is taken seriously. Thanks for your understanding on this one.

Yes, we would be providing you with the certificate of completion of the program once you have successfully submitted all the assessment and it has been verified by our subject matter experts.

GreaterHeight is offering you the most updated, relevant, and high-value real-world projects as part of the training program. This way, you can implement the learning that you have acquired in real-world industry setup. All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry ready.
You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc. After completing the projects successfully, your skills will be equal to 6 months of rigorous industry experience.

All our mentors are highly qualified and experience professionals. All have at least 15-20 yrs. of development experience in various technologies and are trained by GreaterHeight Academy to deliver interactive training to the participants.

Yes, we do. As the technology upgrades, we do update our content and provide your training on latest version of that technology.

All online training classes are recorded. You will get the recorded sessions so that you can watch the online classes when you want. Also, you can join other class to do your missing classes.

OUR POPULAR COURSES

Data Analytics and Visualization With Python

Advanced developments of expertise in cleaning, transforming, and modelling data to obtain insight into corporate decision making as a Senior Data Analyst - Using Python.

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Data Science Training Masters Program

Learn Python, Statistics, Data Preparation, Data Analysis, Querying Data, Machine Learning, Clustering, Text Processing, Collaborative Filtering, Image Processing, etc..

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Microsoft Azure DP-100 Data Science

You will Optimize & Manage Models, Perform Administration by using T-SQL, Run Experiment & Train Models, Deploy & Consume Models, and Automate Tasks.

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Machine Learning using Python

Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science course on.

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Microsoft Azure PL-300 Data Analysis

You will learn how to Design a Data Model in Power BI, Optimize Model Performance, Manage Datasets in Power BI and Create Paginated Reports.

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Microsoft Azure DP-203 Data Engineer

You will learn Batch & Real Time Analytics, Azure Synapse Analytics, Azure Databricks, Implementing Security and ETL & ELT Pipelines.

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The GreaterHeight Advantage

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Accredited Courseware

Most of our training courses are accredited by the respective governing bodies.

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Assured Classes

All our training courses are assured & scheduled dates are confirmed to run by SME.

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Expert Instructor Led Programs

We have well equipped and highly experienced instructors to train the professionals.

OUR CLIENTS

We Have Worked With Some Amazing Companies Around The World

Our awesome clients we've had the pleasure to work with!

Machine Learning Engineer With Python

Machine Learning Engineer With Python Course

Course Benefits & Key Features

Machine Learning Engineer With Python’s benefits and key features.

Modules

Lessons

Practical

Life Projects

Resume

Job

Recording

Interviews

Support

Membership

Networks

Certification

INSTRUCTOR-LED LIVE ONLINE CLASSES

Python Developer Program Job Outlook

Ranked #1 ProgrammingLanguage

Python SalaryTrend

44.8% Compound annual growth rate (CAGR)

Machine Learning Engineer With Python?

Machine Learning Jobs Are Lucrative

Demand for Machine Learning Engineering Skills Is High

Opportunities for Continual Learning

They Live on the Cutting-Edge of Technology

Machine Learning Careers Offer Variety

Become a Leader

GreaterHeight Certificates holders are prepared to work at companies like these.

Some Alumni Testimonies

QUEEN OBIWULU

BISOLA OGUNRO

TUNDE MEREDITH

JOHN OSI PETER

AYODELE PAYNE

ADEBAYO OLADEJO

Machine Learning Fundamentals with SQL

Module 1: Classification

Module 2: Regression

Module 3: Fine-Tuning Your Model

Module 4: Preprocessing and Pipelines

Module 1: Clustering for Dataset Exploration

Module 2: Visualization with Hierarchical Clustering and t-SNE

Module 3: Decorrelating Your Data and Dimension Reduction

Module 4: Discovering Interpretable Features

Module 1: Introduction to PyTorch, a Deep Learning Library

Module 2: Training Our First Neural Network with PyTorch

Module 3: Neural Network Architecture and Hyperparameters

Module 4: Evaluating and Improving Models

Module 1: Introduction to Reinforcement Learning

Module 2: Model-Based Learning

Module 3: Model-Free Learning

Module 4: Advanced Strategies in Model-Free RL

Data Engineer with Python Course

Module 1: Introduction to Cloud Computing

Module 2: Cloud Deployment

Module 3: Cloud Providers and Case Studies

Module 1: Introduction to Python

Module 2: Working with Data Types

Module 3: Control flow and loops

Module 1: The Python Ecosystem

Module 2: Working with Functions

Module 3: Lambda Functions and Error-Handling

Module 1: Introduction and Flat Files

Module 2: Importing Data From Other File Types

Module 3: Working with Relational Databases in Python

Module 1: Importing data from the Internet

Module 2: Interacting with APIs to import data from the web

Module 3: Diving deep into the Twitter API

Module 1: Common Data Problems

Module 2: Text and Categorical Data Problems

Module 3: Advanced Data Problems

Module 4: Record Linkage

Module 1: Foundations for Efficiencies

Module 2: Timing and Profiling Code

Module 3: Gaining Efficiencies

Module 4: Basic Pandas Optimizations

Module 1: Importing Data from Flat Files

Module 2: Importing Data From Excel Files

Module 3: Importing Data from Databases

Ranked #1 Programming
Language

Python Salary
Trend