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GreaterHeight Technologies LLC ~ GreaterHeight Academy
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      • Mastering Data and Business Analytics
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Data Science With Python Certification 


It will help you master important Python programming concepts such as data operations, file operations, object-oriented programming, and various Python libraries such as Pandas, Numpy, and Matplotlib essential for Data Science.


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Data Science With Python Certification Course Training



Who this course is for:

  • Analysts or BI professionals looking for a deep introduction to basic Python.
  • Aspiring data scientists who want to build foundational Python programming skills.
  • Anyone interested in learning one of the most popular open source programming languages in the world.
  • Students looking to learn powerful, practical skills with hands-on projects and course demos.


What you will Learn:

  • Master the building blocks of base Python, including data types, variables, loops, functions and more.
  • Learn how to use Jupyter Notebooks to write, manage, and comment your Python code
  • Analyze and manipulate numeric data, text strings, lists, tuples, dictionaries and sets.
  • Explore raw data using conditional logic, nested loops, custom functions, and comprehensions.
  • Use Python's Openpyxl package to read & write data to Excel worksheets.
  • Build solid, foundational Python skills for data analysis & business intelligence

Course Benefits & Key Features

Python Programming Certification Course’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
.

Data Science with Python Certification Course 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.



  • Python Developer
  • Data Analyst
  • Python Data Scientist

Job Titles include:


  • Data Scientist
  • AI Engineer


  • AI Researcher
  • Machine Learning Engineer


Data Science with Python Tools Covered

.

Data Science with Python Skills Covered

.
  • Python Programming
  • Supervised and Unsupervised Machine Learning

  • Statistical Foundations
  • Database Integration with Python

  • Data Analysis and Visualization
  • Data Visualization Using Tableau

Why Data Science With Python Certification Course?

Learn In-demand Skills

Those with careers in data analysis learn relevant in-demand skills that span industries and add value to every digital-enabled organization.


Earn a Higher Salary

Experienced data analysts can earn up to $112,000 per year and transition into higher-paying jobs as Senior Data Analysts, Data Scientists, or Analytics Managers.

Positive Job Outlook

The data analytics market is predicted to hit $132.90 Billion USD by 2026. COVID-19 pandemic accelerated the adoption of data analytics solutions and services.

Shape the Future

Data analysts transform organizations by capitalizing on data to improve their business decisions and solve critical real-world problems.

Become a Leader

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

Data Analysis are Constantly Evolving

Data analysis moves quickly, and data analysts are constantly learning and advancing in their careers.




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)

Data Science With Python Certification Course


GreaterHeight Academy's Data Science with Python Certification Course is designed to help you master Data Science with Python Programming language. It will help you master important Python programming concepts such as data operations, file operations, object-oriented programming, and various Python libraries such as Pandas, Numpy, and Matplotlib essential for Data Science. This course is well-suited for professionals and beginners. This training will introduce you to different types of Machine Learning, Recommendation Systems, and other relevant concepts to kickstart your Data Science career.

WEEK 4-7 | 19 Modules | 20+ Hours | 18 Skills

Course Modules


  1. Overview of Python
  2. The Companies using Python
  3. Different Applications where Python is Used
  4. Discuss Python Scripts on UNIX/Windows
  5. Values, Types, Variables
  6. Operands and Expressions
  7. Conditional Statements
  8. Loops
  9. Command Line Arguments
  10. Writing to the Screen
  11. What is Data Science?
  12. What does Data Science involve?
  13. Era of Data Science
  14. Business Intelligence vs Data Science
  15. Life cycle of Data Science
  16. Tools of Data Science

Hands-on:

  1. Creating “Hello World” code
  2. Variables
  3. Demonstrating Conditional Statements
  4. Demonstrating Loops

  1. Data Analysis Pipeline
  2. What is Data Extraction?
  3. Types of Data
  4. Raw and Processed Data
  5. Data Wrangling
  6. Python files I/O Functions
  7. Numbers
  8. Strings and related operations
  9. Tuples and related operations
  10. Lists and related operations
  11. Dictionaries and related operations
  12. Sets and related operations

Hands-on:

  1. Tuple - properties, related operations, compared with the list
  2. List - properties, related operations
  3. Dictionary - properties, related operations
  4. Set - properties, related operations

  1. Functions
  2. Function Parameters
  3. Global Variables
  4. Variable Scope and Returning Values
  5. Lambda Functions
  6. Object Oriented Concepts
  7. Standard Libraries
  8. Modules Used in Python
  9. The Import Statements
  10. Module Search Path
  11. Package Installation Ways
  12. Errors and Exception Handling
  13. Handling Multiple Exceptions

Hands-on:

  1. Lambda function in Python
  2. Errors and Exceptions in Python
  3. Packages and Modules in Python
  4. Functions - Syntax, Arguments, Keyword Arguments, Return Values
  5. Sorting - Sequences, Dictionaries, Limitations of Sorting

  1. Data Analysis
  2. NumPy - arrays
  3. Operations on arrays
  4. Indexing, slicing, and iterating
  5. Reading and writing arrays on files
  6. Pandas - data structures & index operations
  7. Reading and Writing data from Excel/CSV formats into Pandas
  8. Metadata for imported Datasets
  9. Matplotlib library
  10. Grids, axes, plots
  11. Markers, colors, fonts, and styling
  12. Types of plots - bar graphs, pie charts, histograms
  13. Contour plots

Hands-on:

  1. NumPy library - Creating NumPy array, operations performed on NumPy array
  2. Pandas library - Creating series and data frames, Importing and exporting data
  3. Matplotlib library - Using Scatterplot, histogram, bar graph, a pie chart to show information, Styling of Plot

  1. Data Manipulation
  2. Basic Functionalities of a data object
  3. Merging of Data objects
  4. Concatenation of data objects
  5. Types of Joins on data objects
  6. Exploring and analyzing datasets
  7. Analysing a dataset

Hands-on:

  1. Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples(), GroupBy operations, Aggregation, Concatenation, Merging and joining

  1. What is Machine Learning?
  2. Machine Learning Use-Cases
  3. Machine Learning Process Flow
  4. Machine Learning Categories
  5. Linear regression
  6. Gradient descent

Hands-on:

  1. Linear Regression – Boston Dataset

  1. What are Classification and its use cases?
  2. What is a Decision Tree?
  3. Algorithm for Decision Tree Induction
  4. Creating a Perfect Decision Tree
  5. Confusion Matrix
  6. What is Random Forest?

Hands-on:

  1. Implementation of Logistic Regression, Decision Tree, Random Forest algorithms

  1. Introduction to Dimensionality
  2. Why Dimensionality Reduction
  3. PCA
  4. Factor Analysis
  5. Scaling dimensional model
  6. LDA

Hands-on:

  1. Implementing PCA
  2. Scaling dimensional model
  3. Implementing LDA

  1. What is Naïve Bayes?
  2. How Naïve Bayes works?
  3. Implementing Naïve Bayes Classifier
  4. What is a Support Vector Machine?
  5. Illustrate how Support Vector Machine works
  6. Hyperparameter Optimization
  7. Grid Search vs. Random Search
  8. Implementation of Support Vector Machine for Classification

Hands-on:

  1. Implementation of Naïve Bayes, SVM algorithms

  1. What is Clustering & its Use Cases?
  2. What is K-means Clustering?
  3. How does the K-means algorithm works?
  4. How to do optimal clustering
  5. What is C-means Clustering?
  6. What is Hierarchical Clustering?
  7. How does Hierarchical Clustering work?

Hands-on:

  1. Implementing K-means Clustering
  2. Implementing Hierarchical Clustering

  1. What are Association Rules?
  2. Association Rule Parameters
  3. Calculating Association Rule Parameters
  4. Recommendation Engines
  5. How do Recommendation Engines work?
  6. Collaborative Filtering
  7. Content-Based Filtering

Hands-on:

  1. Implementing Apriori Algorithm
  2. Performing Market Basket Analysis

  1. What is Reinforcement Learning?
  2. Why Reinforcement Learning?
  3. Elements of Reinforcement Learning
  4. Exploration vs. Exploitation dilemma
  5. Epsilon Greedy Algorithm
  6. Markov Decision Process (MDP)
  7. Q values and V values
  8. Q – Learning
  9. Values

Hands-on:

  1. Calculating Reward
  2. Discounted Reward
  3. Calculating Optimal quantities
  4. Implementing Q Learning
  5. Setting up an Optimal Action

  1. What is Time Series Analysis?
  2. Importance of TSA
  3. Components of TSA
  4. White Noise
  5. AR model
  6. MA model
  7. ARMA model
  8. ARIMA model
  9. Stationarity
  10. ACF & PACF

Hands-on:

  1. Checking Stationarity
  2. Converting non-stationary data to stationary
  3. Implementing Dickey-Fuller Test
  4. Plotting ACF and PACF
  5. Generating the ARIMA plot

  1. What is Model Selection?
  2. Need for Model Selection
  3. Cross Validation
  4. What is Boosting?
  5. How do Boosting Algorithms work?
  6. Types of Boosting Algorithms
  7. Adaptive Boosting

Hands-on:

  1. Performing Cross Validation
  2. Implementing AdaBoost using Python

  1. What is Exploratory Data Analysis?
  2. EDA Techniques
  3. EDA Classification
  4. Univariate Non-graphical EDA
  5. Univariate Graphical EDA
  6. Multivariate Non-graphical EDA
  7. Multivariate Graphical EDA
  8. Heat Maps

Hands-on:

  1. Implementing Graphical EDA Techniques
  2. Implementing Non-Graphical EDA Techniques

  1. Basics of database management
  2. Python SQL Server
  3. Create database
  4. Create a table
  5. Insert into table
  6. Select query
  7. Where clause
  8. OrderBy clause
  9. Delete query
  10. Drop table
  11. Update query
  12. Limit clause
  13. Join and Self-Join
  14. MongoDB (Unstructured)
  15. Insert_one query
  16. Insert_many query
  17. Update_one query
  18. Update_many query
  19. Create_index query
  20. Drop_index query
  21. Delete and drop collections
  22. Limit query

Hands-on:

  1. CRUD operations using Python SQL Server and MongoDB

  1. Data Visualization
  2. Business Intelligence tools
  3. VizQL Technology
  4. Connect to data from the File
  5. Connect to data from the Database
  6. Basic Charts
  7. Chart Operations
  8. Combining Data
  9. Calculations

Hands-on:

  1. Connecting to data from File, Database, and Server
  2. Performing operations on Hierarchies, Data Granularity and Highlighting feature
  3. Creating calculated fields using basic functions
  4. Defining LOD expressions
  5. Creating Parameters
  6. Performing User Input and What-if analysis

  1. Trend lines
  2. Reference lines
  3. Forecasting
  4. Clustering
  5. Geographic Maps
  6. Using charts effectively
  7. Dashboards
  8. Story Points
  9. Visual best practices
  10. Publish to Tableau Online

Hands-on:

  1. Analyzing data using techniques including Forecasting, Trend Lines, Reference Lines, Clustering, and Geographic Maps
  2. Building Dashboard Layout and Formatting
  3. Building Story points

  1. Predict the species of Plant

Hands-on:

  1. Analyze the data
  2. Predict the plant species

DATA SCIENCE WITH PYTHON CERTIFICATION COURSE COST


United States

$349.99

United Kingdom

£299.99

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Python Programming Certification Course Trainng

  • GreaterHeight Academy's Data Science with Python Certification Training teaches you the significance of data science, machine learning, and its application in Python. This course helps you gain proficiency in automating real-world scenarios with machine learning algorithms. 
  • GreaterHeight Academy offers the most exceptional Data Science with Python training if you are dedicated to becoming proficient in Python.
  • Seize the opportunity and enroll in Data Science with Python Certification Course now to benefit from the teaching of industry experts. Elevate your Python skills with us today.

The prerequisites for GreaterHeight Academy's Python for Data Science Course include a fundamental understanding of Computer Programming Languages.

  • Python is one of the premier, flexible, and powerful open-source languages that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. For over a decade, It has been used in scientific computing and highly quantitative domains such as finance, oil and gas, physics, and signal processing. It's continued to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python cuts development time in half with its simple-to-read syntax and easy compilation feature. The debugging program is a breeze in Python, and it has a built-in debugger. It has evolved as the most preferred Language for Data Analytics. The increasing search trends also indicate that it is the Next Big Thing and a must for Professionals in the Data Analytics domain.

After completing this Data Science with Python Certification course, you will be able to:

  1. Programmatically download and analyze data
  2. Learn techniques to deal with different types of data – ordinal, categorical, encoding
  3. Learn data visualization
  4. Using python notebooks, master the art of presenting step-by-step data analysis
  5. Gain insight into the 'Roles' played by a Machine Learning Engineer
  6. Describe Machine Learning
  7. Work with real-time data
  8. Learn tools and techniques for predictive modeling
  9. Discuss Machine Learning algorithms and their implementation
  10. Validate Machine Learning algorithms
  11. Explain Time Series and its related concepts
  12. Perform Text Mining and Sentiment analysis GreaterHeight Academy offers the best course for Python data science and ML.

Enroll now in our Data Science with Python training and get a chance to learn from industry leaders.

  1. Freshers, Programmers, Developers, Technical Leads, Architects
  2. Developers aspiring to be a ‘Machine Learning Engineer'
  3. Analytics Managers who are leading a team of analysts
  4. Business Analysts who want to understand Machine Learning (ML) Techniques
  5. Information Architects who want to gain expertise in Predictive Analytics
  6. Professionals who want to design automatic predictive models

  • The duration of the Data Science with Python Course is approximately 5 to 10 weeks. Depending on their experience with web development, data science, and other related fields, it may vary from person to person.

  • The Data Science with Python course fee is INR 21995, but you can get it at a discounted price of INR 19795. You can get 10% OFF and save 2200. No Cost EMI Starts at 6,599 / month.

Here is the list of topics covered in the Data Science with Python Course Syllabus:

  1. Introduction to Data Science and ML using Python
  2. Data Handling, Sequences and File Operations
  3. Deep Dive – Functions, OOPs, Modules, Errors, and Exceptions
  4. Introduction to NumPy, Pandas, and Matplotlib
  5. Data Manipulation
  6. Introduction to Machine Learning with Python
  7. Supervised Learning - I
  8. Dimensionality Reduction
  9. Supervised Learning - II
  10. Unsupervised Learning
  11. Association Rules Mining and Recommendation Systems
  12. Reinforcement Learning
  13. Time Series Analysis
  14. Model Selection and Boosting
  15. Statistical Foundations
  16. Database Integration with Python
  17. Data Connection and Visualization in Tableau
  18. Advanced Visualizations
  19. In-Class Project

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