Month 1: Introduction to Data Science & Python

– Introduction to Data Science
– Python programming fundamentals
– Data structures and algorithms in Python
– Libraries: NumPy, Pandas, Matplotlib
– Data exploration and visualization

Month 2: Data Wrangling & Exploratory Data Analysis (EDA)

– Data cleaning and preprocessing
– Handling missing data and outliers
– EDA with Pandas, Matplotlib, and Seaborn
– Feature engineering
– Introduction to statistical concepts

Month 3: Machine Learning Basics

– Introduction to Machine Learning
– Supervised vs. Unsupervised Learning
– Regression (Linear, Multiple)
– Classification (Logistic Regression, k-Nearest Neighbors)
– Model evaluation and performance metrics

Month 4: Advanced Machine Learning Techniques

– Decision Trees, Random Forests, Gradient Boosting
– Support Vector Machines (SVM)
– Clustering (K-Means, Hierarchical)
– Dimensionality reduction (PCA)
– Time series forecasting

Month 5: Deep Learning Fundamentals

– Introduction to Neural Networks
– Convolutional Neural Networks (CNNs)
– Recurrent Neural Networks (RNNs)
– Introduction to TensorFlow and Keras
– Building and deploying deep learning models

Month 6: Capstone Project

– Work on a real-world project using data science techniques learned.
– Present project outcomes and models.
– Resume building, interview preparation, and job portal handling.

– Capstone Project: Each course culminates in a capstone project where students work on realworld problems and build a professional portfolio.

 – Certifications: Upon completion, students receive two national-level certifications.

 – Internship Opportunities: A 6-month internship is provided to gain practical experience.

 – Placement Assistance: Lifetime placement support, resume building, and job interview preparation are available for all students.