Finest Data Science Course in Pune with Placement | Data Science Classes in Pune

Module 1: Introduction to Data Science

What is data science? Role in industries

Data science workflow: Collection, cleaning, analysis, modeling

Tools: Python, R, Jupyter Notebook

Career paths: Data scientist, analyst, ML engineer

Hands-on: Set up Python environment and Jupyter

Python syntax: Variables, data types (int, str, list)

Python syntax: Variables, data types (int, str, list)

Control structures: if-else, loops (for, while)

Functions: Defining, arguments, return

Exception handling: try, except

Hands-on: Write basic Python scripts (e.g., calculator)

Module 3: Advanced Python Programming

Data structures: Lists, dictionaries, sets, tuples

List comprehensions and lambda functions

Object-Oriented Programming: Classes, inheritance

Modules: NumPy, Pandas introduction

Hands-on: Build a program using OOP

Module 4: Mathematics for Data Science

Linear Algebra: Vectors, matrices, dot product

Statistics: Mean, median, variance

Probability: Distributions (normal, binomial)

Calculus: Derivatives for optimization

Hands-on: Solve matrix equations with NumPy

Module 5: Statistics for Data Science

Measures of central tendency, dispersion

Inferential statistics with Hypothesis testing, p-values

Correlation and covariance

Sampling techniques: Random, stratified

Hands-on: Perform statistical analysis in Python

Module 6: Data Wrangling with Pandas

Pandas: DataFrames, Series, indexing

Data cleaning: Handling missing values, duplicates

Merging and joining datasets

Data transformation: Grouping, pivoting

Hands-on: Clean a real-world dataset

Module 7: Data Visualization with Python

Visualization libraries: Matplotlib, Seaborn

Plot types: Bar, line, scatter, histogram

Customizing plots: Labels, colors, themes

Storytelling with visualizations

Hands-on: Create visualizations for a dataset

Module 8: SQL for Data Science

SQL basics: Tables, queries, joins

Filtering: WHERE, GROUP BY, HAVING

Subqueries and window functions

Connecting SQL with Python (e.g., sqlite3)

Hands-on: Write SQL queries for analysis

Module 9: Introduction to Machine Learning

What is machine learning? Supervised vs. unsupervised

ML workflow: Data, model, evaluation

Key terms: Features, labels, overfitting

Scikit-learn: Python’s ML library

Hands-on: Explore a sample ML dataset

Module 10: Linear Regression

Concept: Predicting continuous values

Cost function: Mean Squared Error (MSE)

Optimization: Gradient Descent

Evaluation: R-squared, RMSE

Hands-on: Build a linear regression model

Module 11: Logistic Regression

Concept: Predicting categories

Sigmoid function and decision boundaries

Loss function: Log loss

Evaluation: Accuracy, precision, recall

Hands-on: Implement logistic regression

Module 12: Decision Trees

How decision trees work: Splits, nodes

Entropy and Information Gain

Pruning to avoid overfitting

Pros and cons of decision trees

Hands-on: Train a decision tree model

Module 13: Random Forests

Ensemble learning: Combining trees

Bagging and feature randomness

Hyperparameter tuning: Number of trees

Feature importance analysis

Hands-on: Build a random forest model

Module 14: KNNs

Concept: Predicting based on nearest points

Distance metrics: Euclidean, Manhattan

Choosing optimal k value

Strengths and limitations

Hands-on: Apply KNN to a dataset

Module 15: Support Vector Machines (SVM)

Concept: Finding the best hyperplane

Kernels: Linear, RBF for non-linear data

Soft vs. hard margins

Evaluation metrics for SVM

Hands-on: Train an SVM model

Module 16: Clustering with K-Means

Unsupervised learning: Grouping data

K-Means algorithm: Steps, iteration

Choosing k: Elbow method

Applications: Customer segmentation

Hands-on: Cluster a dataset

Module 17: Principal Component Analysis (PCA)

Dimensionality reduction: Why and when

Variance and eigenvectors

Steps to apply PCA

Visualizing reduced data

Hands-on: Reduce dataset dimensions with PCA

Module 18: Into Deep Learning

Neural networks & Layers, neurons, weights

Activation functions & ReLU, Sigmoid

Backpropagation and gradient descent

Frameworks: TensorFlow, Keras

Hands-on: Build a simple neural network

Module 19: CNNs

CNNs for image data: Convolution, pooling

Architecture: Filters, feature maps

Transfer learning with pre-trained models

Applications: Image classification

Hands-on: Train a CNN for image recognition

Module 20: RNNs

Sequential data: Time series, text

RNN structure: Loops, memory

Variants: LSTM, GRU

Challenges: Vanishing gradients

Hands-on: Train an RNN on time series data

Module 21: Natural Language Processing (NLP) Basics

Text preprocessing: Tokenization, stemming

Bag of Words, TF-IDF models

Word embeddings: Word2Vec introduction

Applications: Sentiment analysis

Hands-on: Process and analyze text data

Module 22: Advanced NLP with Transformers

Transformer architecture: Attention, encoder-decoder

Pre-trained models: BERT, GPT basics

Fine-tuning for NLP tasks

Applications: Text classification, Q&A

Hands-on: Fine-tune a BERT model

Module 23: Time Series Analysis

Time series components: Trend, seasonality

Models: ARIMA, Exponential Smoothing

Stationarity and differencing

Evaluation: Mean Absolute Error (MAE)

Hands-on: Forecast sales with ARIMA

Module 24: Learn Recommendation Systems

Matrix factorization techniques

Building a simple recommender

Evaluation: Precision, recall at k

Hands-on: Build a movie recommender

Module 25: Big Data Basics

What is big data? Volume, velocity, variety

Tools: Hadoop, Spark overview

PySpark for distributed computing

Handling large datasets

Hands-on: Process data with PySpark

Module 26: Data Engineering Basics

ETL processes: Extract, Transform, Load

Data pipelines: Airflow introduction

Cloud storage: AWS S3, Google Cloud Storage

Data quality and validation

Hands-on: Build a simple ETL pipeline

Module 27: Model Evaluation and Tuning

Metrics: F1-score, ROC-AUC, confusion matrix

Cross-validation: k-fold technique

Hyperparameter tuning: Grid search, random search

Bias-variance tradeoff

Hands-on: Tune a machine learning model

Module 28: Model Interpretability

Why interpretability matters

Techniques: SHAP, LIME for feature importance

Visualizing model decisions

Communicating results to stakeholders

Hands-on: Interpret a model’s predictions

Module 29: Model Deployment

Deploying models: Flask, FastAPI

Model serialization: Pickle, Joblib

Cloud platforms: AWS, Heroku

API creation for model access

Hands-on: Deploy a model as an API

Module 30: MLOps Basics

What is MLOps? ML lifecycle management

Tools: MLflow for tracking

CI/CD for ML models

Monitoring: Model drift detection

Hands-on: Set up an MLOps pipeline

Module 31: Data Science in Industry - Part 1

Healthcare: Disease prediction, imaging

Finance: Fraud detection, credit scoring

Retail: Demand forecasting

Case studies: Real-world applications

Hands-on: Solve a healthcare problem

Module 32: Data Science in Industry - Part 2

Marketing: Customer segmentation

Manufacturing: Predictive maintenance

E-commerce: Churn prediction

Case studies: Industry success stories

Hands-on: Solve an e-commerce problem

Module 33: Ethics in Data Science

Bias in data and models: Causes, mitigation

Privacy: GDPR, data anonymization

Fairness and transparency

Ethical case studies: Misuse of data

Hands-on: Analyze a dataset for bias

Module 34: Data Storytelling

Communicating insights effectively

Building dashboards: Tableau, Power BI

Crafting reports for non-technical audiences

Visual design principles

Hands-on: Create a dashboard

Module 35: Version Control for Data Science

Git basics: Commit, branch, merge

GitHub: Repositories, collaboration

Versioning datasets and models

Handling team projects

Hands-on: Push a project to GitHub

Module 36: Advanced Tools for Data Science

Cloud platforms: AWS SageMaker, GCP AI

Automation: Airflow, Kedro

Containerization: Docker basics

Collaborative tools: JupyterHub

Hands-on: Use a cloud tool for analysis

Module 37: Placement Preparation - Technical Skills

Resume building: Highlight data science projects

Portfolio: Showcase ML models, dashboards

Coding interviews: Python, SQL, algorithms

Mock technical interviews

Hands-on: Solve data science challenges

Module 38: Placement Preparation - Professional Skills

Behavioral interviews: Communication, teamwork

Job search: LinkedIn, Pune tech networks

Role expectations: Data scientist, analyst

Mock HR interviews

Hands-on: Prepare a job pitch

Module 39: Capstone Project - Part 1

Project selection: Real-world data science problem

Planning: Data, models, deliverables

Initial work: Data collection, preprocessing

Mentor feedback session

Hands-on: Submit project proposal

Module 40: Capstone Project - Part 2

Implementation: Model building, evaluation

Deployment: API or dashboard

Presentation: Demo to peers and recruiters

Placement support: Showcase to employers

Final submission: Project report and demo