Special Topic: Data Science and Analytics

This course provides a comprehensive introduction to the principles, techniques, and applications of data science. Where we explore the entire data science workflow, from data collection, cleaning, analysis, visualization, and modeling. The course emphasizes hands-on experience using and “Python” programming language with “Notebook” tools, enables developing practical skills in data manipulation, statistical analysis, and machine learning.

Course Outline

1. Statistics for data science Veiw Chapter
2. Python Veiw Chapter
3. Numpy Veiw Chapter
4. Pandas Veiw Chapter
5. Data wrangling and aggregation Veiw Chapter
6. Data cleaning Veiw Chapter
7. Data Visualization Veiw Chapter Part two hands on
8. Machine Learning Intro Veiw Chapter
9. Machine Learning Preprocessing Veiw Chapter
10. Regressionmodeling Veiw Chapter

Course Assignments

1. Intro to ipynb:
2. Minimum price groceries Numpy
3. Normal Distribution Sampling np
4. Coffee reviews analysis pandas
5. Retail queries groupby pd
6. Happiness score Visualization
7. 3D prints Roughness Prefiction Linear Regression