Subject Overview

A professionally oriented study of Python for the end-to-end data science lifecycle, covering programming foundations, data acquisition, numerical computing, data preparation, analytics, visualization, machine learning, reproducibility, deployment, governance and emerging AI practices.

Learning Goal

Students will be able to design, implement, evaluate and communicate reliable Python-based data science solutions that support organizational decision-making while meeting contemporary standards for quality, ethics, security and governance.

Syllabus

1. Python and the Data Science Ecosystem

Learning Objective: Explain Python terminology, the data science lifecycle, analytical problem framing, interpreters, notebooks, IDEs, packages, virtual environments and responsible use of AI coding assistants.

Category: foundation

2. Python Syntax, Variables and Control Flow

Learning Objective: Write reliable programs using data types, operators, expressions, input/output, conditional logic, loops, exceptions and Pythonic conventions.

Category: foundation

3. Core Data Structures and Comprehensions

Learning Objective: Manipulate strings, lists, tuples, sets and dictionaries; apply indexing, slicing, mutability, comprehensions and suitable structure selection.

Category: core

4. Functions, Modules and Object-Oriented Programming

Learning Objective: Develop reusable code using functions, scope, parameters, modules, packages, classes, objects, inheritance, iterators, generators and type hints.

Category: core

5. Files, Databases, APIs and Data Acquisition

Learning Objective: Acquire and exchange data through text, CSV, JSON, Excel, SQL databases, REST APIs and web sources while addressing validation, privacy and access controls.

Category: application

6. Numerical Computing with NumPy

Learning Objective: Perform efficient numerical analysis using arrays, vectorization, broadcasting, indexing, aggregation, linear algebra, random sampling and performance-aware computation.

Category: core

7. Data Manipulation with pandas

Learning Objective: Create and transform Series and DataFrames using selection, filtering, sorting, grouping, aggregation, merging, reshaping and time-series operations.

Category: core

8. Data Cleaning, Transformation and Feature Engineering

Learning Objective: Diagnose and treat missing values, duplicates, outliers, inconsistent categories and data types; construct reproducible transformation and feature-engineering pipelines.

Category: application

9. Exploratory Data Analysis and Statistical Reasoning

Learning Objective: Use descriptive statistics, probability concepts, sampling, distributions, correlation, hypothesis testing and uncertainty to investigate data and avoid misleading conclusions.

Category: core

10. Data Visualization and Analytical Communication

Learning Objective: Build accessible visualizations with Matplotlib, Seaborn and interactive tools; select appropriate charts and communicate findings through dashboards, narratives and decision-focused reports.

Category: application

11. Machine Learning with Python

Learning Objective: Implement supervised and unsupervised learning with scikit-learn, including regression, classification, clustering, preprocessing, train-test design, cross-validation and baseline comparison.

Category: application

12. Model Evaluation, Interpretation and Decision Support

Learning Objective: Select appropriate metrics, diagnose overfitting, tune models, interpret predictions, assess bias and translate analytical results into operational and managerial decisions.

Category: strategic

13. Software Quality, Reproducibility and Collaboration

Learning Objective: Apply Git, documentation, coding standards, logging, debugging, unit testing, dependency management, pipeline design and reproducible experiment practices expected in industry.

Category: application

14. Deployment, Automation and Scalable Data Science

Learning Objective: Operationalize Python solutions through scripts, scheduled workflows, APIs, dashboards, containers and cloud platforms; monitor data, model performance, cost and reliability.

Category: advanced

15. Ethics, Governance and Emerging Python Practice

Learning Objective: Evaluate privacy, fairness, explainability, security, intellectual property and regulatory obligations; examine MLOps, AutoML, generative AI, large language models, responsible AI and Python’s role in digital transformation.

Category: contemporary