What You Will Learn

This course covers the essential concepts and skills in C Programming, Data Science, Python programming, Machine Learning, Artificial Intelligence and SQL. The curriculum is organized into modules that guide you from basic to advanced topics.

Module 1: Foundations & Setup

• Understand the basics of Data Science, including its lifecycle and roles.

• Learn essential math concepts like linear algebra, statistics, and probability.

• Get introduced to Python programming, focusing on syntax, data types, and functions.

• Set up your working environment using tools like Jupyter Notebook and Git.

Module 2: Data Handling & Analysis

• Use NumPy for array operations and data manipulation.

• Work with Pandas to clean and analyze data using DataFrames.

• Conduct exploratory data analysis (EDA) to understand datasets better.

• Learn SQL basics for data retrieval and manipulation.

Module 3: Data Visualization

• Create visualizations using Matplotlib and Seaborn.

• Learn to design dashboards and tell stories with data.

Module 4: Statistics & Probability

• Explore descriptive and inferential statistics, including hypothesis testing.

• Understand probability distributions and their applications.

Module 5: Machine Learning - Supervised

• Learn the fundamentals of machine learning, including model evaluation techniques.

• Practice regression and classification algorithms.

• Explore ensemble methods to improve model performance.

Module 6: Machine Learning - Unsupervised

• Understand clustering techniques and dimensionality reduction methods.

Module 7: Advanced Topics

• Get introduced to time series analysis and natural language processing (NLP).

• Learn about deep learning concepts and tools.

Module 8: Tools & Case Studies

• Practice version control with Git and explore web scraping techniques.

• Work on end-to-end projects in various domains.

Module 9: Industry Readiness

• Understand data science ethics and improve communication skills.

• Prepare for job interviews and build a portfolio.

Throughout the course, you will practice using various tools such as Python, Pandas, NumPy, Scikit-learn, SQL, and Matplotlib. The structure allows for a gradual learning progression from foundational concepts to more advanced applications in data science and machine learning.