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