Data Analysis
| Data Analysis | |
|---|---|
| Course Description | This course prepares students for introductory-level data analyst roles by focusing on R programming, data visualization, statistical methods, and machine learning basics. Students will gain hands-on experience using R Studio and Python to analyze real-world datasets, building proficiency in data manipulation, summarization, probability, and regression analysis. |
| Recommended Books | 1. R for Data Science by Garrett Grolemund and Hadley Wickham. 2. Python for Data Analysis by Wes McKinney. |
| Course Learning Outcomes | After completing this course, a student will be able to: 1. Understand data collection methods, data types, and basic statistical analysis. 2. Utilize R and Python for data manipulation, visualization, and statistical inference. 3. Apply foundational machine learning techniques for data-driven insights. |
| Assessment System | Quizzes: 10-15% Assignments: 5-10% Midterms: 30-40% ESE: 40-50% |
| Lecture Plan | ||
|---|---|---|
| S.No. | Description | Quizzes/Assignment |
| 1 | Introduction to Data Analysis and Course Overview | |
| 2 | Data Collection, Population vs Sampling, Data Types | Quiz 1 |
| 3 | Introduction to R and R Studio | |
| 4 | Data Import, Export, and Basic Data Manipulation | Assignment 1 |
| 5 | Summary Statistics and Data Visualization with R | |
| 6 | Boxplot, Histogram, Scatterplot, and Heatmap | |
| 7 | Probability Basics and Bayes’ Theorem | Quiz 2 |
| 8 | Statistical Inference: t-Test and χ²-Test | |
| 9 | Hands-on Data Analysis with Case Study | |
| 10 | Introduction to Python for Data Analysis | |
| 11 | Python Libraries: NumPy, Matplotlib, Pandas | Assignment 2 |
| 12 | Introduction to Machine Learning: Supervised Learning | Quiz 3 |
| 13 | Linear and Multiple Linear Regression | |
| 14 | Decision Trees and Random Forest | |
| 15 | Final Review and Practical Project | Assignment 3 |
