Data Science Bootcamp- Bridging the Skills Gap

This introductory module is mandatory. It ensures you understand the methodology and master the technological tools necessary to succeed, removing technical barriers that often frustrate beginners.

This is the Training overview for the course.

This lesson gives the basic introduction to Python for data Science. It is intended for the absolute beginners.

Learn the Advanced Python concepts.

Learn the Python Functions.

This lesson introduces NumPy, Python's fundamental library for numerical computing, focusing on statistical analysis. You'll learn to:

• Create and manipulate multidimensional arrays for efficient data storage

• Perform vectorized operations (fast math without loops)

• Calculate key statistical measures (mean, median, standard deviation)

• Generate random distributions (normal, uniform) for simulations

• Apply linear algebra (dot products, matrix multiplication)

• Visualize data distributions with histograms

Perfect for data analysis, scientific computing, and machine learning prep. Includes hands-on exercises with real-world datasets.

Prerequisites: Basic Python (lists, loops).

Master Pandas, Python's essential library for data manipulation and analysis. Learn to clean, transform, and analyze structured data efficiently using Series and DataFrames. Ideal for data science, analytics, and machine learning workflows.

This beginner-friendly course introduces Seaborn, a powerful Python library for creating stunning data visualizations with minimal code. Designed for those with no prior programming experience, you’ll learn to transform raw data into insightful charts—faster and easier than tools like Excel.