Python for Data Science (and Version Control with GitHub)
Python for Data Science (and Version Control with GitHub)
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There are 6 modules in this course
Master Python programming for data analysis in this comprehensive course designed for aspiring data scientists. Through hands-on projects using real-world datasets, you'll learn essential data manipulation, visualization, and statistical analysis techniques while integrating modern AI tools and version control practices.
This course is perfect for analysts and professionals who want to advance beyond spreadsheets to powerful programming solutions. Starting with Python fundamentals and progressing through advanced analysis techniques, you'll develop practical skills that directly apply to real-world data challenges. Upon completion, you'll be able to: β’ Import, clean, and manipulate data using Python's powerful libraries (Pandas, NumPy) β’ Create compelling visualizations with Matplotlib, Seaborn, and Plotly β’ Perform statistical analysis and A/B testing for data-driven decisions β’ Automate data workflows and generate professional reports β’ Implement version control best practices using GitHub
In this module, you'll set up a powerful development environment, master essential Python syntax, and learn to leverage GitHub for seamless collaboration. By the module's end, you'll be equipped with the same foundational skills used by industry pros, including cutting-edge GenAI applications. Get ready to transform from a coding novice to a confident data explorer.
What's included
3 videos10 readings2 assignments2 ungraded labs
3 videosβ’Total 12 minutes
- Welcome to Python for Data Scienceβ’3 minutes
- Day in the Life - An Interview With an Expertβ’5 minutes
- Getting Started with Jupyter Notebooksβ’5 minutes
10 readingsβ’Total 140 minutes
- Course Syllabus & Roadmapβ’30 minutes
- EngageMetrics Introductionβ’10 minutes
- Video Transcript Accessβ’10 minutes
- Introduction to Jupyter Notebook with Python: Overview and Resourcesβ’30 minutes
- Python Basicsβ’10 minutes
- Hello, Python!β’10 minutes
- Case Study: Data Environments in Industryβ’10 minutes
- Version Control Best Practicesβ’10 minutes
- Familiarize with Git and GitHubβ’10 minutes
- Git Basics for Data Scientistsβ’10 minutes
2 assignmentsβ’Total 60 minutes
- Graded Assessment: Environment Setup and GitHubβ’30 minutes
- Knowledge Check: Python Syntax & Notebook Basicsβ’30 minutes
2 ungraded labsβ’Total 120 minutes
- Jupyter Notebook Setup & Basicsβ’60 minutes
- Initial Setup and Version Control Challengeβ’60 minutes
In this module, you'll develop essential skills for transforming raw data into analysis-ready formats - a critical foundation for any data science workflow. You'll master techniques for importing data from diverse sources, manipulating complex datasets, and optimizing data structures for analysis. Working with real-world datasets from our EngageMetrics and MediTrack case studies, you'll build practical experience in data preparation that directly translates to professional scenarios.
What's included
4 videos2 assignments1 programming assignment4 ungraded labs
4 videosβ’Total 22 minutes
- Data Import Essentials: CSV, Excel, and APIsβ’4 minutes
- Transforming HR Data with Pandasβ’4 minutes
- Python Data Transformation Essentialsβ’6 minutes
- NumPy Speed Boost: From Loops to Lightning-Fast Arraysβ’7 minutes
2 assignmentsβ’Total 60 minutes
- Knowledge Check: Data Import Functions β’30 minutes
- Knowledge Check: Non-numeric Data Handlingβ’30 minutes
1 programming assignmentβ’Total 180 minutes
- Graded Lab: Multi-Source Data Integration Challengeβ’180 minutes
4 ungraded labsβ’Total 240 minutes
- Data Import Labβ’60 minutes
- DataFrame Operations Labβ’60 minutes
- Data Type Transformation Labβ’60 minutes
- Numerical Calculations Labβ’60 minutes
In this module, you'll learn to uncover and communicate insights through powerful data visualization techniques. You'll master both static and interactive visualization tools, from Matplotlib and Seaborn to Plotly, while conducting thorough exploratory data analysis. Using the EngageMetrics and MediTrack datasets, you'll develop the skills to transform complex data into compelling visual stories that drive decision-making.
What's included
3 videos2 readings2 assignments1 programming assignment3 ungraded labs
3 videosβ’Total 16 minutes
- First Look: Smart EDA for HR Data β’7 minutes
- Professional Visualizations: From Basic to Beautifulβ’6 minutes
- Interactive Dashboards with Plotly: Bringing Data to Lifeβ’3 minutes
2 readingsβ’Total 40 minutes
- Exploratory Data Analysis Techniquesβ’30 minutes
- Choosing the Right Visualization: A Guide to Basic Chart Typesβ’10 minutes
2 assignmentsβ’Total 60 minutes
- Graded Assessment: Visualization Challenge β’30 minutes
- Knowledge Check: EDA Concepts β’30 minutes
1 programming assignmentβ’Total 180 minutes
- Graded Lab: Comprehensive EDA Challengeβ’180 minutes
3 ungraded labsβ’Total 180 minutes
- Guided EDA Exercises Labβ’60 minutes
- Building Visualizations Labβ’60 minutes
- Interactive Visualization Labβ’60 minutes
In this module, you'll elevate your analytical capabilities with advanced statistical methods and testing procedures. You'll learn to conduct hypothesis tests, design and analyze A/B tests, and automate analytical workflows. Working with real employee and medical data, you'll gain hands-on experience in applying sophisticated analytical techniques to solve complex business problems.
What's included
3 videos2 readings1 assignment4 ungraded labs
3 videosβ’Total 17 minutes
- Statistical Analysis in HR: From Hypothesis to Insightsβ’7 minutes
- A/B Testing in Action: Analyzing Department Performanceβ’7 minutes
- Automating Your Data Science Workflowβ’3 minutes
2 readingsβ’Total 60 minutes
- Statistical Methods in Pythonβ’30 minutes
- A/B Testing in Action: Training Methods Analysisβ’30 minutes
1 assignmentβ’Total 30 minutes
- Graded Assessment: Advanced Data Analysisβ’30 minutes
4 ungraded labsβ’Total 240 minutes
- Statistical Analysis Labβ’60 minutes
- A/B Testing Labβ’60 minutes
- Automation & Reporting Labβ’60 minutes
- Integrated Statistical Analysis and A/B Test Challengeβ’60 minutes
In this module, you'll learn to implement professional data science workflows using GitHub, AI-assisted documentation, and strategic version control. Working with the EngageMetrics employee dataset, you'll develop essential skills for collaborative data science projects. You'll learn version control best practices for Jupyter notebooks and discover how to leverage AI tools for efficient documentation. Throughout the module, you'll practice effective branching strategies that help manage complex analyses. Through hands-on labs and real-world scenarios, you'll gain practical experience that directly translates to professional data science work. By the end of this module, you'll be prepared to manage collaborative projects using industry-standard version control practices.
What's included
3 videos1 reading2 assignments3 ungraded labs
3 videosβ’Total 10 minutes
- Versioning Your Notebooks with GitHubβ’4 minutes
- Leveraging AI for Better Documentation β’3 minutes
- Branching Strategies for Data Science Projectsβ’3 minutes
1 readingβ’Total 30 minutes
- Automated Documentation Techniquesβ’30 minutes
2 assignmentsβ’Total 60 minutes
- Graded Assessment: GitHub Integration and Version Controlβ’30 minutes
- Knowledge Check: GitHub Version Controlβ’30 minutes
3 ungraded labsβ’Total 180 minutes
- GitHub Integration Labβ’60 minutes
- AI-Assisted Documentation Labβ’60 minutes
- Experiment Management Labβ’60 minutes
This final module combines hands-on practice and focused assessment to validate your data science capabilities. You'll first work through a comprehensive TrendWave Media analysis project, then demonstrate your mastery through a targeted assessment covering key data science concepts and skills.
What's included
1 video3 readings1 assignment1 ungraded lab
1 videoβ’Total 5 minutes
- Industry Expert Interview on Data Science Workflowsβ’5 minutes
3 readingsβ’Total 70 minutes
- Introducing TrendWave Media: Your Capstone Project Datasetβ’10 minutes
- Capstone Project Overviewβ’30 minutes
- Course Wrap-Upβ’30 minutes
1 assignmentβ’Total 30 minutes
- Graded Assessment: End-of-Course Assessmentβ’30 minutes
1 ungraded labβ’Total 60 minutes
- Capstone Project Labβ’60 minutes
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