This course is designed to provide students with a comprehensive introduction to Python programming, focusing on its application in data science. Students will learn the fundamentals of Python, including data types, control structures, functions, and libraries commonly used in data analysis such as pandas and numpy. The course will also cover data visualization techniques using libraries like matplotlib and seaborn, as well as basic statistics and probability concepts essential for data analysis.
By the end of this course, students will write clean, efficient Python code for data tasks, Analyze and manipulate datasets using pandas and numpy, Visualize data using matplotlib, seaborn, and plotly, Apply foundational statistics and probability concepts, Build reproducible data workflows using Python tools
| Course Code | sop-AI-101 |
|---|---|
| Course Duration | 10 weeks |
| Cost | USD 100 |
| Commitment | 12-16 hrs./week |
| Tools Needed | A stable internet connection for streaming videos is needed. (a Device [Phone, Tablet, or PC]) |
| Prerequisite Course | Python Fundamentals |
| Weighted grade distribution |
- Passing Grade: - Assignments: - Final Examination: |
| Batch | Start Date | End Date |
|---|---|---|
| I | February 2, 2026 | May 17, 2026 |
| Week | Topics | Assessment |
|---|---|---|
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Week 1
(Module 1) Python Programming Foundations |
- Python syntax & variables - Data types: strings, numbers, booleans - Lists, tuples, dictionaries, and sets - Loops, conditionals, and functions - Jupyter Notebook & Google Colab basics |
Exercise: Build a simple text-based data calculator (e.g., weather tracker) |
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Week 2
(Module 2) Working with Data using pandas and numpy |
- Series and DataFrames - Importing/exporting data (CSV, Excel, JSON) - Indexing, slicing, and filtering - Aggregation and group operations - Intro to numpy arrays and broadcasting |
Project: Analyze a CSV dataset (e.g., global population or Netflix titles) |
| Week 3
(Module 3) Data Cleaning and Transformation |
- Handling missing data - String operations and data type conversion - Duplicates and outlier detection - Merging and joining datasets - Date/time parsing and manipulation |
Assignment: Clean and transform a messy dataset (e.g., survey or ecommerce data) |
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Week 4
(Module 4) Data Visualization with Python |
- Plotting with matplotlib: line, bar, scatter, pie - Styling and annotations - seaborn: histograms, boxplots, violin plots, heatmaps - Intro to plotly for interactive charts | Project: Create a data dashboard (static or interactive) |
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Week 5
(Module 5) Exploratory Data Analysis (EDA) |
- Summary statistics - Distributions and frequency analysis - Correlation analysis - Crosstabs and pivot tables - Visual EDA storytelling |
Exercise: Perform an EDA on a public dataset (e.g., Titanic, World Bank) |
| Week 6
(Module 6) Basic Probability & Statistical Analysis |
- Descriptive statistics (mean, median, std, IQR) - Probability basics: events, conditional, independence - Common distributions: normal, binomial, uniform - Central Limit Theorem (CLT) introduction | Assignment: Analyze patterns using a real dataset |
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Week 7
(Module 7) Hypothesis Testing & Inferential Statistics |
- Sampling and confidence intervals - t-tests, chi-square tests - p-values and statistical significance - Assumptions and limitations | Exercise: Conduct hypothesis testing on real-world data |
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Week 8
(Module 8) Automating & Documenting Data Workflows |
- Using Python scripts to automate data cleaning - Creating reusable functions and modules - Introduction to argparse and basic CLI tools - Exporting reports and dataframes to Excel/PDF/CSV - Documenting analysis with Markdown and Notebooks | Project: Build a reproducible end-to-end data pipeline |
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Week 9
(Optional Module 9) Working with APIs and Web Data |
- HTTP requests with requests - Parsing JSON data - Accessing open APIs (e.g., weather, finance) - Intro to web scraping (using BeautifulSoup) | |
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Week 10
(Optional Module 10) Intro to SQL with Python |
- SQL basics (SELECT, WHERE, JOIN) - Running SQL queries using sqlite3 or SQLAlchemy - Integrating SQL data into pandas workflows |
Capstone Project : complete an independent or group project using real-world data. Deliverables include: - Cleaned dataset - Visualizations - EDA report - Statistical findings - Optional: automated script/report Examples: - Analysis of open government spending - Weather trend explorer |
| Core Tools & Libraries | Recommended Resources |
|---|---|
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This course provides a comprehensive introduction to machine learning (ML) concepts and techniques. Students will learn the fundamentals of ML, including supervised and unsupervised learning, model evaluation, and feature engineering. The course will cover popular ML algorithms such as linear regression, decision trees, support vector machines, and clustering methods. Students will gain hands-on experience with Python libraries like scikit-learn, TensorFlow, and Keras to build and evaluate ML models using real-world datasets.
By the end of this course, students will be able to understand core ML concepts and algorithms, preprocess and prepare data for ML, build and evaluate supervised and unsupervised models, use real-world datasets to develop and test predictive models, gain hands-on experience with scikit-learn and modern ML tools
| Course Code | sop-AI-102 |
|---|---|
| Course Duration | 14 weeks |
| Cost | USD 350 (half price for first batch) |
| Commitment | 12 - 16 hrs./week |
| Tools Needed | A stable internet connection for streaming videos is needed. (a Device [Phone, Tablet, or PC]) |
| Prerequisite Course | Python Fundamentals - sop-webfb-101, Python for Data Science - sop-AI-101 |
| Weighted grade distribution |
- Passing Grade: - Assignments: - Final Examination: |
| Batch | Start Date | End Date |
|---|---|---|
| I | February 2, 2026 | May 17, 2026 |
| Week | Topics | Assessment |
|---|---|---|
|
Week 1
(Module 1) Introduction to Machine Learning |
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Exercise: Train/test split + baseline model on sample dataset |
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Week 2
(Module 2) Data Preparation & Feature Engineering |
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Assignment: Prepare a dataset for model training |
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Week 3
(Module 3) Model Evaluation & Metrics |
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Mini-project: Evaluate different metrics on a classification problem |
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Week 4
(Module 4) Linear Models |
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Assignment: Predict house prices or customer churn |
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Week 5
(Module 5) Tree-Based Models |
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Mini-project: Predict loan approval or income classification |
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Week 6
(Module 6) k-NN, SVMs, and Naive Bayes |
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Assignment: Build and compare models for email spam detection |
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Week 7
(Module 7) Clustering & Dimensionality Reduction |
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Project: Customer segmentation or topic clustering |
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Week 8
(Module 8) Model Tuning & Pipelines |
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Exercise: Build a complete pipeline from raw data to prediction |
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Week 9
(Module 9) Advanced Topics |
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Optional: Intro to AutoML (e.g., H2O, TPOT, Auto-sklearn) |
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Week 10
(Module 10) AI Ethics & Model Interpretability |
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Case Study: Audit a real-world model for fairness or bias |
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Week 11 - 14
Capstone Project |
Students build a complete ML project:
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| Core Tools & Libraries | Recommended Resources |
|---|---|
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