Programming & Data Science

Description

Module 1: Introduction to Programming

• What is programming? Why it’s important in data science

• Understanding compilers, interpreters, and IDEs

• Programming languages overview: Python, R, JavaScript, SQL

• Basics of algorithmic thinking and flowcharts

• Introduction to Git & GitHub (version control)

🔹 Module 2: Python Programming Essentials

• Variables, data types, and operators

• Control structures: if, for, while

• Functions and modules

• Lists, dictionaries, sets, tuples

• Error handling and debugging

• Object-Oriented Programming (OOP) basics

🔹 Module 3: Data Handling with Python

• Reading/writing data: CSV, Excel, JSON

• Data manipulation with pandas

• Data visualization with matplotlib and seaborn

• Working with dates and times

• Exploratory Data Analysis (EDA) practices

🔹 Module 4: SQL for Data Science

• Databases and RDBMS concepts

• SQL basics: SELECT, WHERE, JOIN, GROUP BY, ORDER BY

• Aggregate functions and subqueries

• Views, indexes, and stored procedures

• Working with real-world datasets

🔹 Module 5: Statistics & Probability for Data Science

• Descriptive statistics: mean, median, mode, standard deviation

• Probability theory and distributions

• Sampling methods and central limit theorem

• Hypothesis testing (t-test, chi-square, ANOVA)

• Correlation and regression analysis

🔹 Module 6: Data Science Tools

• Jupyter Notebook and Google Colab

• Introduction to NumPy for numerical computing

• APIs and web scraping basics (requests, BeautifulSoup)

• Working with Open Data repositories (Kaggle, UCI)

🔹 Module 7: Machine Learning Basics

• Introduction to machine learning: supervised vs unsupervised

• Train/test split and evaluation metrics

• Algorithms overview:

  • Linear and logistic regression

  • Decision trees and random forest

  • K-means clustering

  • Naive Bayes

• Model evaluation: accuracy, precision, recall, F1-score

🔹 Module 8: Advanced Python for Data Science

• Advanced data structures (heap, stack, queue)

• List comprehensions and lambda functions

• Decorators, generators, and iterators

• Working with APIs (REST, JSON)

• Introduction to automation and scripting

🔹 Module 9: Data Visualization & Storytelling

• Creating impactful visuals using:

  • Matplotlib, Seaborn

  • Plotly, Dash, Tableau (overview)

• Telling a story through data

• Creating dashboards and reports

• Design principles for data storytelling

🔹 Module 10: Real-World Projects & Capstone

• Real datasets from healthcare, e-commerce, education, or finance

• Problem identification, data cleaning, analysis, and visualization

• Machine learning model development

• Presentation of results and storytelling

• Peer review and feedback

🏅 Optional Certifications & Add-ons

• IBM Data Science Professional Certificate

• Google Data Analytics Certificate

• Python for Everybody (Coursera)

• Microsoft Certified: Data Analyst Associate (Power BI)

🎯 Ideal For:

• Beginners in programming or data science

• Students and professionals switching careers

• Business analysts, developers, or researchers

• Anyone seeking job-ready skills in tech and analytics