Advanced Python Programming for Data Science

Course Title: Advanced Python Programming for Data Science

Course No: ENCT 325

Nature of the Course: Theory + Lab

Semester: 5

Full Marks: 40 + 60 + 25

Pass Marks: 16 + 24 + 10

Credit Hours: 3

Course Description

This course develops advanced proficiency in Python programming for data science applications. It focuses on efficient coding practices, sophisticated data manipulation, statistical analysis, and data visualization using modern Python libraries. Students will also learn fundamental data engineering and pipeline design concepts to automate and scale real-world data workflows.

Course Objectives

The objective of this course is to develop advanced proficiency in Python programming for data science applications. It focuses on efficient coding practices, sophisticated data manipulation, statistical analysis, and data visualization using modern Python libraries. Students will also learn fundamental data engineering and pipeline design concepts to automate and scale real-world data workflows.

Course Contents

1. Advanced Python Concepts and Best Practices

7 hrs9 marks

1.1. Review of Python essentials and coding conventions

1.2. Advanced data structures: Collections, iterators, generators, and decorators

1.3. Functions and lambda expressions

1.4. Object-Oriented Programming for data science applications

1.5. Exception handling, debugging, and logging

1.6. Working with modules and packages

2. Data Sources and APIs

7 hrs9 marks

2.1. Reading and writing structured/unstructured data (CSV, JSON, Excel, text)

2.2. Database access with relational database and non-relational database

2.3. Accessing and processing data from APIs (REST, SOAP)

2.4. Web scraping using requests and BeautifulSoup

2.5. Handling large datasets with chunking and lazy evaluation

3. Advanced Data Wrangling and Transformation

9 hrs12 marks

3.1. Advanced Pandas operations: Merging, joining, reshaping, pivoting

3.2. Handling missing, categorical, and time-series data

3.3. Feature transformation, scaling, and encoding

3.4. Memory optimization and efficient data processing

3.5. Building a reusable data-cleaning pipeline

3.6. Introduction to data pipeline components (Ingestion, transformation, storage)

4. Applied Statistics and Exploratory Analysis

7 hrs10 marks

4.1. Statistical measures: Correlation, covariance, skewness, kurtosis

4.2. Probability review, sampling, and hypothesis testing

4.3. Regression and trend analysis using stats models

4.4. Exploratory data analysis (EDA) using descriptive and inferential methods

4.5. Automation of EDA workflows using Python

5. Data Visualization and Storytelling

7 hrs9 marks

5.1. Principles of effective visualization and dashboard design

5.2. Visualization with Matplotlib: Line, bar, histogram, scatter, subplots

5.3. Seaborn for statistical visualization: Box plot, pair plot, heat map

5.4. Interactive visualization using Plotly

5.5. Visualization driven insight generation

5.6. Case study: End-to-end visualization and reporting project

6. Data Engineering and Automation

8 hrs11 marks

6.1. Overview of data engineering in applied data science

6.2. Designing and implementing ETL pipelines

6.3. Automating workflows with schedulers (CRON, schedule)

6.4. Logging, monitoring, and error handling in pipelines

6.5. Data storage and retrieval strategies for pipelines

6.6. Automated report generation (Excel, HTML, PDF)

6.7. Case study: End-to-end automated analytics pipeline

Laboratory Works

Practical sessions covering Python environment setup with OOP and functions, data collection via APIs and web scraping, data cleaning and transformation pipelines using Pandas, exploratory data analysis and statistical summaries, interactive visualizations, automating ETL tasks, generating automated analytical reports, and a mini project building a complete applied data pipeline from ingestion to visualization and reporting on a real-world dataset. (15 hours)

1.Setting up Python environment for applied data workflows and writing modular programs using OOP and functions
2.Collecting data via APIs and web scraping
3.Building advanced data cleaning and transformation pipelines using Pandas
4.Conducting exploratory data analysis and statistical summaries
5.Developing interactive visualizations using Plotly, matplotlib and Seaborn
6.Automating ETL tasks and data refresh using Python schedulers
7.Generating summary dashboards and automated analytical reports
8.Mini Project: Build a complete applied data pipeline from ingestion to visualization and reporting on a real-world dataset

Text Books

1.McKinney, W. (2022). Python for data analysis. O'Reilly Media.
2.VanderPlas, J. (2016). Python data science handbook. O'Reilly Media.

Reference Books

1.Beazley, D. (2021). Python cookbook. O'Reilly Media.
2.Grus, J. (2022). Data science from scratch. O'Reilly Media.

Notes:

This syllabus follows the official BCT curriculum of Tribhuwan University. In case of any doubt or revision, the university's published syllabus shall be considered authoritative. https://ioe.tu.edu.np/pages/computer-engineering-curriculum-structure-2635

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Advanced Python Programming for Data Science

Course Description

Course Objectives

Course Contents

Laboratory Works

Text Books

Reference Books

Notes: