Statistical Learning with Python

The QMH Statistics Club is pleased to announce an exciting new course titled “Statistical Learning with Python” for the students of the Department of Statistics, University of Dhaka (Except 1st year’s students). This course aims to provide students with hands-on experience in using Python for statistical analysis and data science, preparing them for future careers in data-driven fields.

Course Details:

• Schedule: Classes will be held every Thursday from 9.00 AM to 10.50 AM. Classes will start from 14th November, 2024. Number of classes will be 8.
• Format: The course includes lectures, hands-on coding sessions, and guided practice with real-world data.
• Course Materials: PDF copies of all materials and resources will be provided to participants.

Course Highlights:

• Python for Data Analysis: Develop a strong foundation in Python programming, focusing on data manipulation, visualization, and analysis.
• Practical Applications: Engage in practical exercises and assignments with real datasets to solidify your understanding.
• Guidance from Experts: Learn from experienced members of the QMH Statistics Club with expertise in Python and data science.

Certification Requirements:
• Attendance: Participants must attend at least 60% of the classes to qualify for evaluation-exam.
• End-of-Course Evaluation: A final exam will be held to assess understanding and award completion certificates.

Registration Fee:

• 100 BDT (to be submitted to the class representative by 13th November).

This course is an excellent opportunity to gain practical experience with statistical learning and strengthen your analytical skills. We encourage all interested students to take part and make the most of this chance to build a solid foundation in python programming.

Registration Link:

Click the below link to fill in the form for registration.

https://forms.gle/1B8gPNVLXhosoNqf9

Course Outline:

1. Python Introduction
   • History of python
   • How python work?
   • Why python needed?
2. Variable and Operators
3. Type conversion
4. Input and output
5. Escape sequence
6. Condition
7. Loop
8. Array
9. String
10. Function
11. List
12. Tuple
13. Set
14. Dictionary
15. Comprehension
16. Build in function
17. Error handling
18. Module and package
19. Lambda function
20. Recursion
21. Library
    • Numpy (different mathematical calculation)
    • Pandas (Data manipulation for analysis)
    • Matplotlib, seaborn (Visualization of graph and graph save)
    • Skit-learn
    • Tensorflow
22. Linear Algebra
    Matrix, determinant, Inverse Matrix, Eigen matrix and Eigen value, Multiplication, Transpose, sum and substruction, Identity matrix, Trace
23. Descriptive statistics
    Mean, median, mode, max, min, variance, std, sum, five number summary, missing value handle, Z-score
24. Probability and distribution
25. Regression
26. A/B test
27. Different statistical tools
    • – P-value and confidence interval
    • – Missing value handle
    • – Decode
    • -Dummy variable
    • -Nominal and ordinal scale
    • -Random number
    • -Sample draw
    • -Pivot table
28. Data manipulation
    • Marge dataset, join, concatenate, reshape, Group, Frequency and cross tabulation, qualitative and quantitative data
29. Different mathematical function
    • Logarithm, trigonometry, different expression, power, differential
30. Library and package installation
31. Real life project

Instructor details:

Name: Gulam Kibria
Gmail: [email protected]
Contact number: 01762423777