Python For Analytics

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This structured 30-hour Python for analytics course covers essential Python programming, data structures, a capstone project, and mock interview preparation to equip participants with practical skills for data analysis roles.

  1. Introduction to Python for Analytics (2 hours)
    • Overview of Python in data analytics
    • Setting up Python environment (Anaconda, Jupyter)
  2. Python Basics (2 hours)
    • Syntax and data types
    • Control structures (if, loops)
    • Functions and modules
  3. Data Structures in Python (4 hours)
    • Lists, tuples, and dictionaries
    • Sets and their applications
    • Working with strings
  4. File Handling in Python (2 hours)
    • Reading and writing files
    • CSV and JSON data formats
  5. NumPy and Pandas (4 hours)
    • Introduction to NumPy arrays
    • Data manipulation with Pandas DataFrames
  6. Data Cleaning and Preprocessing (4 hours)
    • Handling missing data
    • Data normalization and scaling
  7. Data Visualization with Matplotlib and Seaborn (4 hours)
    • Creating basic plots
    • Customizing visualizations
  8. Introduction to Capstone Project (2 hours)
    • Overview of the project requirements
    • Choosing a dataset for analysis
  9. Capstone Project Development (8 hours)
    • Participants work on the guided analytics project
    • Instructor guidance and troubleshooting
  10. Capstone Project Presentations (2 hours)
    • Participants present their project findings
    • Feedback and discussion
  11. Mock Interview Preparation (2 hours)
    • Review of common interview questions
    • Tips for effective communication
  12. Mock Interview Sessions (4 hours)
    • Participants undergo mock interviews
    • Feedback on presentation and problem-solving skills
  13. Advanced Data Manipulation with Pandas (4 hours)
    • Merging and joining DataFrames
    • Grouping and aggregating data
  14. Working with Dates and Times in Python (2 hours)
    • Handling date and time data
    • Time series analysis basics
  15. Web Scraping with Beautiful Soup and Requests (4 hours)
    • Introduction to web scraping
    • Extracting data from websites
  16. Introduction to SQL and Database Connectivity (4 hours)
    • Basics of SQL queries
    • Connecting Python to databases
  17. Statistical Analysis with SciPy and Statsmodels (4 hours)
    • Overview of statistical tests
    • Regression analysis in Python
  18. Final Review and Q&A Session (2 hours)
    • Recap of key Python concepts for analytics
    • Open discussion and addressing participant queries

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Total Students 160
Total Lessons 32
Total Quizzes 6
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About Instructors

1. Education: The instructor holds a Master’s degree in Computer Science from a BPUT (NIST) university, specializing in analytics and programming.

2. Professional Experience: With over 6+ years of industry experience with Top Mncs the instructor has worked in roles involving data analysis, Python programming, and business intelligence.

3. Certifications: The instructor possesses relevant certifications, including Python for Data Science and Analytics, showcasing expertise in the field.

4. Teaching Experience: With a passion for education, the instructor has conducted workshops and training sessions on Python, data analytics, and visualization.

—-” Baishalini Sahu ”

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