Python Programming for Data Science

Course Title: Python Programming for Data Science

Course No: ENCT 329

Nature of the Course: Theory + Lab

Semester: 5

Full Marks: 60 + 0 + 0

Pass Marks: 24 + 0 + 0

Credit Hours: 3

Course Objectives

The objective of this course is to develop a fundamental understanding of Python programming and its applications in data science. It covers Python basics including syntax, variables, data types, and control structures, and provides hands-on experience with essential libraries such as NumPy, Pandas, Matplotlib, and Seaborn for data manipulation, analysis, and visualization. By the end of the course, students will be able to work with real-world datasets and apply Python programming to solve data-driven problems effectively.

Course Contents

1. Introduction

8 hrs

1.1. Overview of Python and its role in data science

1.2. Setting up Python environment (Anaconda, Jupyter Notebook)

1.3. Basic syntax, variables, keywords, and expressions

1.4. Data types: Numeric, string, boolean, and type conversion

1.5. Input/output operations and basic file handling

2. Control Structures and Functions

8 hrs

2.1. Conditional statements (if, elif, else)

2.2. Looping constructs (for, while, nested loops)

2.3. Loop control statements (break, continue, pass)

2.4. Functions – Definition, arguments, return values, lambda functions

2.5. Scope and lifetime of variables, error, and exception handling

3. Data Structures and Collections

8 hrs

3.1. Lists, tuples, sets, and dictionaries – Creation and manipulation

3.2. List comprehensions and dictionary comprehensions

3.3. Working with nested data structures

3.4. Introduction to Python modules and importing libraries

4. NumPy for Numerical Computing

5 hrs

4.1. Introduction to NumPy and array creation

4.2. Indexing, slicing, and reshaping arrays

4.3. Array operations and broadcasting

4.4. Mathematical, statistical, and aggregate functions

4.5. Practical applications in scientific computing

5. Data Manipulation with Pandas

8 hrs

5.1. Introduction to Series and DataFrame

5.2. Data import/export (CSV, Excel, JSON)

5.3. Data cleaning: handling missing values, filtering, sorting

5.4. Data transformation: grouping, merging, joining, and aggregation

5.5. Descriptive statistics and data summarization

6. Data Visualization with Matplotlib and Seaborn

8 hrs

6.1. Importance of visualization in data science

6.2. Plotting basics using Matplotlib (Line, bar, scatter, histogram)

6.3. Customizing plots: Titles, labels, legends, colors, subplots

6.4. Seaborn for statistical visualization: Boxplot, heatmap, pairplot, distplot

6.5. Creating visual stories using combined plots

Laboratory Works

Practical exercises include environment setup with Python, Anaconda, and Jupyter Notebook; basic programming tasks involving variables, expressions, loops, and conditional statements; implementing Python functions, list and dictionary operations, and comprehension techniques; file handling with text and CSV files; NumPy array creation, reshaping, and vectorized operations; data manipulation with Pandas including loading real datasets, handling missing values, filtering, sorting, and aggregating; data summarization and descriptive statistics using Pandas; visualization using Matplotlib; and statistical visualization using Seaborn.

1.Environment setup
2.Basic programming tasks
3.Functions and data structures
4.File handling
5.NumPy practice
6.Data manipulation with Pandas
7.Data summarization and descriptive statistics
8.Visualization using Matplotlib
9.Statistical visualization using Seaborn

Reference Books

1.VanderPlas, J. (2016). Python data science handbook. O'Reilly Media.
2.McKinney, W. (2018). Python for data analysis. O'Reilly Media.
3.Gaddis, T. (2018). Starting out with Python. Pearson Education.
4.Matthes, E. (2023). Python crash course. No Starch Press.

Notes:

This syllabus follows the official BEI curriculum of Tribhuvan University. In case of any doubt or revision, the university’s published syllabus shall be considered authoritative.https://ioe.tu.edu.np/pages/electronics-engineering-curriculum-structure-2660

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