Python for Data Science
Professional Course, Online & In-Person Training, 2024
Course Overview
This comprehensive course introduces Python programming for data science applications. Designed for professionals looking to transition into data science or enhance their analytical capabilities, the course covers fundamental Python programming, data manipulation, visualization, and basic machine learning concepts.
Course Details
- Duration: 12 weeks (3 hours per week)
- Format: Hybrid (Online + In-Person Labs)
- Level: Beginner to Intermediate
- Prerequisites: Basic computer literacy, no programming experience required
- Certification: Certificate of Completion upon passing final project
Learning Objectives
By the end of this course, students will be able to:
- Write Python programs for data analysis tasks
- Manipulate and clean data using pandas
- Create meaningful visualizations using matplotlib and seaborn
- Perform statistical analysis on datasets
- Build basic machine learning models
- Apply data science techniques to real-world problems
Course Curriculum
Module 1: Python Fundamentals (Weeks 1-2)
Topics Covered:
- Python installation and environment setup
- Variables, data types, and operators
- Control structures (if/else, loops)
- Functions and modules
- File handling
Hands-on Projects:
- Build a simple calculator
- Create a file processing script
- Develop a data validation tool
Module 2: Data Structures and NumPy (Weeks 3-4)
Topics Covered:
- Lists, tuples, dictionaries, and sets
- List comprehensions
- Introduction to NumPy
- Array operations and broadcasting
- Mathematical operations with NumPy
Hands-on Projects:
- Statistical calculator using NumPy
- Matrix operations for data transformation
- Performance comparison: Python lists vs NumPy arrays
Module 3: Data Manipulation with Pandas (Weeks 5-6)
Topics Covered:
- DataFrames and Series
- Reading and writing data (CSV, Excel, JSON)
- Data cleaning and preprocessing
- Filtering, sorting, and grouping data
- Handling missing values
- Merging and joining datasets
Hands-on Projects:
- Clean and analyze a messy dataset
- Combine multiple data sources
- Generate summary statistics report
Module 4: Data Visualization (Weeks 7-8)
Topics Covered:
- Matplotlib fundamentals
- Seaborn for statistical visualizations
- Plotly for interactive charts
- Best practices in data visualization
- Creating dashboards
Hands-on Projects:
- Create a comprehensive data visualization portfolio
- Build an interactive dashboard
- Visualize time-series data
Module 5: Statistical Analysis (Week 9)
Topics Covered:
- Descriptive statistics
- Probability distributions
- Hypothesis testing
- Correlation and regression
- Statistical significance
Hands-on Projects:
- Perform A/B testing analysis
- Conduct correlation analysis
- Build a simple linear regression model
Module 6: Introduction to Machine Learning (Weeks 10-11)
Topics Covered:
- Machine learning concepts and types
- scikit-learn library
- Data preprocessing for ML
- Classification algorithms (Decision Trees, Random Forest)
- Regression algorithms
- Model evaluation metrics
Hands-on Projects:
- Build a classification model
- Create a regression model for prediction
- Compare different ML algorithms
Module 7: Final Project (Week 12)
Project Requirements:
- Apply all learned concepts
- Real-world dataset analysis
- Complete data pipeline: cleaning, analysis, visualization, modeling
- Presentation of findings
Teaching Methodology
Interactive Learning
- Live coding demonstrations
- Pair programming exercises
- Code reviews and feedback
Practical Focus
- 60% hands-on exercises
- Real-world datasets from various domains
- Industry-relevant case studies
Support System
- Weekly office hours
- Online discussion forum
- Code review sessions
- One-on-one mentoring
Tools and Technologies
Required Software
- Python 3.8+
- Jupyter Notebook / JupyterLab
- Anaconda Distribution
- VS Code or PyCharm
Key Libraries
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Plotly
- scikit-learn
- statsmodels
Assessment and Grading
| Component | Weight |
|---|---|
| Weekly Assignments | 30% |
| Mid-term Project | 20% |
| Final Project | 35% |
| Class Participation | 15% |
Passing Grade: 70%
Student Outcomes
Cohort Statistics (2024)
- Students Enrolled: 45
- Completion Rate: 87%
- Average Final Grade: 82%
- Student Satisfaction: 4.6/5
Career Impact
- 60% of students applied skills in current job
- 25% transitioned to data-focused roles
- 15% pursued advanced data science education
Student Testimonials
“This course transformed my career. I went from knowing nothing about programming to building data analysis pipelines at work.” - Rahul M., IT Manager
“The hands-on approach and real-world projects made learning Python enjoyable and practical.” - Fatima K., Business Analyst
“Excellent instructor with deep knowledge. The support and feedback were invaluable.” - Ahmed S., System Administrator
Course Materials
Provided Resources
- Comprehensive course notes
- Jupyter notebooks with examples
- Dataset repository
- Video recordings of all sessions
- Cheat sheets and quick references
Recommended Books
- “Python for Data Analysis” by Wes McKinney
- “Hands-On Machine Learning” by Aurélien Géron
- “Python Data Science Handbook” by Jake VanderPlas
Prerequisites and Requirements
Technical Requirements
- Computer with 8GB RAM minimum
- Stable internet connection for online sessions
- Webcam and microphone for interactive sessions
Time Commitment
- 3 hours of live sessions per week
- 4-6 hours of homework and practice per week
- Additional time for final project
Registration and Fees
- Course Fee: Contact for current pricing
- Early Bird Discount: 15% off for early registration
- Group Discount: 20% off for 3+ participants from same organization
Upcoming Sessions
- Next Cohort: Starting February 2025
- Registration Deadline: January 31, 2025
- Limited Seats: 30 participants maximum
Instructor Background
As an IT professional with 6+ years of experience and certified in Data Science & Machine Learning, I bring practical industry experience to the classroom. I’ve implemented data analytics solutions in government systems and have a passion for making complex concepts accessible to learners.
Contact and Enrollment
- Email: shuvokumarshill@gmail.com
- LinkedIn: shuvo-kumar-shill
- Course Updates: Medium Blog
Transform your career with Python and data science. Join the next cohort today!