This course provides a complete pathway to becoming a professional Data Scientist using Python.
You will begin with Python programming, move into data analysis with Pandas & NumPy, learn data cleaning techniques, explore data visualization, and then build machine learning models using Scikit-Learn.

You’ll also gain an understanding of statistics, EDA (Exploratory Data Analysis), feature engineering, model evaluation, and deployment basics.
The course is loaded with hands-on labs and real datasets to help you develop strong analytical and problem-solving skills.

Learning Outcomes

Python Programming Skills

• Python basics: variables, loops, functions, data types

• Use Jupyter Notebook & Google Colab

• Work with lists, dictionaries, tuples & sets

• File handling & data processing

• Functional & object-oriented programming basics

Data Analysis Skills

• Analyze datasets using Pandas

• Use NumPy for numerical operations

• Data cleaning & handling missing values

• Feature selection & transformation

• Filtering, merging, grouping data

• Exploratory Data Analysis (EDA)

Data Visualization Skills

• Visualize data using Matplotlib & Seaborn

• Create line charts, bar graphs, scatter plots

• Build heatmaps, pairplots, histograms

• Create dashboards with Plotly

Statistics & Math for Data Science

• Descriptive & inferential statistics

• Probability concepts

• Hypothesis testing

• Correlation & regression basics

• Normal distribution & sampling

Machine Learning Skills

• Understand ML workflow

• Build models using Scikit-Learn

• Supervised learning: Regression, Classification

• Unsupervised learning: Clustering, PCA

• Train-test split, cross-validation

• Model evaluation: accuracy, precision, recall, F1

• Hyperparameter tuning (GridSearchCV, RandomSearchCV)

Advanced Concepts

• Feature engineering

• Handling imbalanced datasets

• Ensemble methods: Random Forest, XGBoost

• Time series introduction

• Deployment basics using Flask / Streamlit

Project Work

• Data Cleaning Mini Project

• EDA on Real Datasets

• Visualization Dashboard

• Machine Learning Model (Regression/Classification)

• Final Capstone Project: End-to-end ML system with preprocessing, model training, evaluation & deployment-ready output