What You Will Learn

Build a strong foundation in Python programming for Data Science

• Learn Python syntax, variables, operators, loops, and functions

• Understand how to write, organize, and execute Python programs

• Practice solving programming exercises and logical problems

Study Python Data Structures

• Learn lists, tuples, dictionaries, and sets

• Understand how to store, access, and manipulate data efficiently

• Practice data handling and problem-solving using different structures

Learn Object-Oriented Programming (OOP) concepts

• Understand classes, objects, inheritance, polymorphism, and encapsulation

• Practice creating reusable and structured Python programs

• Learn how OOP is used in real-world applications and data projects

Explore Advanced Python concepts

• Learn modules, file handling, exception handling, and advanced functions

• Practice writing clean and structured Python code

• Understand efficient coding techniques for larger projects

Study NumPy for numerical computing

• Learn array creation, indexing, slicing, and mathematical operations

• Practice working with multidimensional arrays and numerical data

• Understand vectorized operations and data processing techniques

Learn Pandas for data analysis

• Understand DataFrames, series, data cleaning, and data transformation

• Practice importing, filtering, grouping, and analyzing datasets

• Learn methods for handling missing and structured data

Explore Data Visualization techniques

• Learn how to represent data using charts and graphs

• Practice creating visual reports and graphical analysis of datasets

• Understand trends, comparisons, and data interpretation methods

Study SQL for Data Science and Data Engineering

• Learn database concepts, queries, joins, filtering, and aggregations

• Practice retrieving and managing data using SQL commands

• Understand advanced SQL operations used in data analysis workflows

Understand Statistics for Data Science

• Learn mean, median, probability, distributions, and hypothesis concepts

• Practice statistical calculations and data interpretation methods

• Understand how statistics is applied in machine learning and analysis

Learn Machine Learning basics

• Understand supervised and unsupervised learning concepts

• Practice working with simple machine learning models and datasets

• Learn the basic workflow of training and evaluating models

Work on Mini Projects

• Apply Python, SQL, statistics, and data analysis concepts in practical tasks

• Practice building structured data science projects step by step

• Learn how to organize data, analyze results, and present findings through guided projects