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 + 40 + 25

Pass Marks: 24 + 16 + 8

Credit Hours: 3

Course Description

This course introduces Python programming fundamentals and their applications in data science, covering syntax, data types, control structures, and essential programming concepts. It emphasizes data manipulation, analysis, and visualization using libraries such as NumPy, Pandas, Matplotlib, and Seaborn to solve real-world data-driven problems.

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 hrs10 marks

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 hrs12 marks

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 hrs10 marks

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 hrs8 marks

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 hrs10 marks

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 hrs10 marks

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

(15 hours)

1.Environment setup: Installation and configuration of Python, Anaconda, and Jupyter Notebook
2.Basic programming tasks: Writing simple Python programs involving variables, expressions, loops, and conditional statements
3.Functions and data structures: Implementing Python functions, list and dictionary operations, and comprehension techniques
4.File handling: Reading and writing text and CSV files using Python’s built-in libraries
5.NumPy practice: Creating, reshaping, and manipulating NumPy arrays; Performing vectorized mathematical operations
6.Data manipulation with pandas: Loading real datasets, handling missing values, filtering, sorting, and aggregating data
7.Data summarization and descriptive statistics: Using Pandas to compute mean, median, standard deviation, correlation, and other statistics
8.Visualization using Matplotlib: Plotting line graphs, bar charts, histograms, and scatter plots; Customizing labels, titles, and legends
9.Statistical visualization using Seaborn: Creating advanced visualizations such as boxplots, heatmaps, and pairplots

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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