Data Science

Course Duration: 16–24 Weeks (4–6 Months)(Flexible) | Mode: Offline/Online

Goal: Train students to become Data Analysts, Data Scientists, ML Engineers with strong Python & ML foundations.

MODULE 1: Introduction to Data Science (Week 1)

What is Data Science?
Data Scientist roles & skills
CRISP-DM Framework
Real-world applications
Project lifecycle overview

MODULE 2: Python for Data Science (Week 1–3)

Python basics
Data structures (lists, tuples, dicts)
Loops & conditions
Functions & modules
File handling
OOP fundamentals
Error handling
Virtual environments

MODULE 3: NumPy & Pandas (Week 3–4)

NumPy

Arrays, indexing, slicing
Broadcasting
Mathematical operations

Pandas

Series & DataFrames
Reading/writing data (CSV, Excel, SQL)
Handling missing data
GroupBy operations
Merging & joining datasets

MODULE 4: Data Visualization (Week 4–5)

Matplotlib
Seaborn
Plotly (optional)
Univariate, bivariate, multivariate plots
Correlation heatmaps

MODULE 5: Statistics for Data Science (Week 5–6)

Descriptive statistics
Probability basics
Distributions
Central Limit Theorem
Hypothesis Testing (t-test, z-test, chi-square)
Confidence intervals
A/B Testing

MODULE 6: SQL for Data Science (Week 6–7)

Relational Database Concepts
CRUD operations
Joins
Window Functions
Subqueries
Aggregations
Query optimization basics

MODULE 7: Exploratory Data Analysis (EDA) (Week 7–8)

Data cleaning
Outlier detection
Feature engineering
Feature scaling (standardization, normalization)
Encoding categorical variables
Data discretization

MODULE 8: Machine Learning (Week 8–12)

Supervised Learning

Linear Regression
Logistic Regression
KNN
Decision Trees
Random Forest
Gradient Boosting (XGBoost/LightGBM)
SVM

Unsupervised Learning

K-Means
Hierarchical Clustering
PCA
Dimensionality Reduction

Model Evaluation

Train-test split
Cross-validation
Confusion matrix
ROC-AUC
RMSE, MAE

MODULE 9: Deep Learning Basics (Week 12–14)

Neural Networks basics
Activation functions
Loss functions
Optimizers
TensorFlow / Keras
Building simple neural networks
CNNs (optional advanced)

MODULE 10: Natural Language Processing (NLP) (Week 14–15)

Text preprocessing
Tokenization
Stemming/Lemmatization
Bag of Words
TF-IDF
Sentiment analysis
Intro to Transformers (optional)

MODULE 11: Big Data & Cloud Integration (Optional — Week 15–16)

Introduction to Hadoop & Spark
PySpark basics
Cloud Platforms: AWS/GCP/Azure
Working with cloud storage & ML services