Is Data Science a good career option in Pune?

Yes. Pune has strong demand for Data Scientists and ML Engineers as IT, product, and fintech companies increasingly rely on data-driven decision making.

Can beginners join the Data Science & Machine Learning course?

Yes. The course starts with Python and statistics fundamentals and is suitable for beginners, engineering students, and freshers with no prior data science background.

What job roles can I apply for after completing the course?

Students can apply for roles such as Data Scientist, Machine Learning Engineer, Data Analyst, and AI/ML Developer.

Does the course include Deep Learning and Generative AI?

Yes. The curriculum includes Deep Learning with TensorFlow and Keras, NLP with Transformers, and an introduction to Generative AI and LLMs.

Are live projects included in the Data Science program?

Yes. Students build mini and mega projects such as churn prediction, fraud detection, and demand forecasting, and deploy models as Streamlit and FastAPI apps.

Which is better for freshers: Data Analytics or Data Science & Machine Learning?

Data Analytics focuses on dashboards and business reporting, while Data Science & Machine Learning goes deeper into predictive modeling, deep learning, and AI. The best option depends on your career goals.

Data Science & Machine Learning – JFP
Job Fulfillment Program

Become a certified Data Scientist with our industry-aligned training. Learn Python, SQL, Statistics, Machine Learning, Deep Learning, NLP, and Generative AI through hands-on projects and get 100% job placement support.

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Industry Oriented Curriculum

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Case Studies & Projects

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Languages & Tools

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Live Session Hours

Introduction to Python & Career Scope in Data Science
Installing Tools (Anaconda, Jupyter Notebook, VS Code, Google Colab)
Syntax, Indentation, Comments & Variables
Data Types & Type Casting (int, float, str, bool)
Operators (Arithmetic, Comparison, Logical, Membership, Identity)
Strings (Indexing, Slicing, String Methods, f-strings)
Conditional Statements (if, elif, else, Nested Conditions)
Loops (for, while, break, continue, pass, range)
Lists (Creating, Indexing, Slicing, List Methods, Nested Lists)
Tuples, Sets & Dictionaries (Properties, Methods, Use Cases)
Comprehensions (List, Set, Dictionary)
Functions (Parameters, Return Values, Default & Keyword Arguments, *args, **kwargs)
Lambda, map(), filter() & zip()
Modules & Packages (import, pip, Virtual Environments)
File Handling (Text, CSV, JSON Files)
Exception Handling (try, except, else, finally, raise)
Object-Oriented Programming Basics (Class, Object, __init__, Methods, Inheritance)
Using AI Coding Assistants Responsibly (Debugging & Code Explanation)

Version Control Concepts
Git Commands (init, add, commit, status, log)
Branching & Merging
Working with GitHub (push, pull, clone, Pull Requests)
Writing a Professional README & Building a Project Portfolio

Database & RDBMS Concepts (Tables, Keys, Relationships)
Normalization Basics (1NF, 2NF, 3NF)
DDL & DML Commands (CREATE, ALTER, DROP, INSERT, UPDATE, DELETE)
SELECT Queries (WHERE, ORDER BY, LIMIT, DISTINCT, LIKE, IN, BETWEEN)
Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
GROUP BY & HAVING
Joins (INNER, LEFT, RIGHT, SELF, CROSS)
Subqueries & Common Table Expressions (CTEs)
Window Functions (ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, Running Totals)
CASE Statements, Date Functions & NULL Handling
Connecting MySQL with Python (SQLAlchemy, Loading Data into Pandas)

Introduction to NumPy Arrays (1D, 2D, Multi-dimensional)
Array Creation, Attributes & Data Types
Indexing, Slicing & Boolean Filtering
Vectorized Operations & Broadcasting
Mathematical & Statistical Functions
Reshaping, Stacking & Splitting Arrays

Series & DataFrame
Reading & Writing Data (CSV, Excel, JSON, SQL)
Exploring Data (head, info, describe, shape, dtypes)
Selecting Data (loc, iloc, Conditional Filtering)
Handling Missing Values & Duplicates
Data Type Conversion & String Cleaning
apply() & Custom Transformations
GroupBy & Aggregation
Merging, Joining & Concatenating DataFrames
Pivot Tables, melt() & Reshaping Data
Working with Dates & Time (to_datetime, Resampling)

Choosing the Right Chart for the Question
Matplotlib (Line, Bar, Scatter, Histogram, Pie, Subplots, Customization)
Seaborn (Histplot, Boxplot, Violin Plot, Heatmap, Pairplot, Countplot)
Plotly (Interactive Charts)
Exploratory Data Analysis (Univariate, Bivariate, Multivariate)
Outlier Detection (IQR Method, Z-score)
Presenting Insights from Data

Types of Data & Measurement Scales
Descriptive Statistics (Mean, Median, Mode, Variance, Standard Deviation, Percentiles)
Probability Basics, Conditional Probability & Bayes' Theorem
Probability Distributions (Normal, Binomial, Poisson, Uniform)
Sampling Techniques & Central Limit Theorem
Confidence Intervals
Hypothesis Testing (Null & Alternative Hypothesis, p-value, Type I & Type II Errors)
Statistical Tests (Z-test, T-test, Chi-Square Test, ANOVA)
Correlation vs Causation (Pearson, Spearman)
A/B Testing
Math for Machine Learning (Vectors, Matrices, Dot Product, Derivatives as Slope)

What is Machine Learning? (AI vs ML vs Deep Learning vs Data Science)
Types of Machine Learning (Supervised, Unsupervised, Reinforcement Overview)
Machine Learning Project Lifecycle (CRISP-DM)
Train, Validation & Test Split; Data Leakage
Bias-Variance Trade-off, Overfitting & Underfitting
Feature Engineering (Encoding, Scaling, Binning, Date Features)
Feature Selection Techniques
Scikit-learn Pipelines & ColumnTransformer
Simple & Multiple Linear Regression (Assumptions, Interpretation)
Gradient Descent (Intuition)
Polynomial Regression
Regularization (Ridge, Lasso, ElasticNet)
Regression Metrics (MAE, MSE, RMSE, R², Adjusted R²)

Logistic Regression
K-Nearest Neighbors (KNN)
Naive Bayes
Support Vector Machines (Linear & Kernel SVM)
Confusion Matrix (TP, TN, FP, FN)
Evaluation Metrics (Accuracy, Precision, Recall, F1-Score, ROC-AUC, PR Curve)
Threshold Tuning
Cross-Validation (K-Fold, Stratified K-Fold)
Handling Imbalanced Data (Class Weights, SMOTE)
Explaining Model Results in Business Terms

Decision Trees (Gini, Entropy, Information Gain, Pruning)
Ensemble Learning Concepts (Bagging, Boosting)
Random Forest
AdaBoost & Gradient Boosting
XGBoost & LightGBM
Feature Importance & Model Explainability (SHAP)
Hyperparameter Tuning (GridSearchCV, RandomizedSearchCV, Optuna Intro)
Saving & Loading Models (Pickle, Joblib)

K-Means Clustering (Elbow Method, Silhouette Score)
Hierarchical Clustering (Dendrograms)
DBSCAN
Principal Component Analysis (PCA)
Anomaly Detection (Isolation Forest)
Association Rules & Market Basket Analysis (Apriori)
Customer Segmentation (RFM Analysis)

Time Series Components (Trend, Seasonality, Cyclic, Noise)
Stationarity & Differencing
Moving Averages & Exponential Smoothing
ARIMA & SARIMA
Forecasting with Prophet
Machine Learning for Forecasting (Lag Features, Rolling Features)
Forecast Evaluation (MAE, MAPE, RMSE)

Introduction to Neural Networks (Neuron, Layers, Weights, Bias)
Activation Functions (Sigmoid, ReLU, Tanh, Softmax)
Forward Propagation & Backpropagation (Intuition)
Loss Functions & Optimizers (SGD, Adam)
Building ANNs with Keras (Sequential API)
Regularization (Dropout, Batch Normalization, Early Stopping)
Convolutional Neural Networks (Convolution, Pooling, Feature Maps)
Transfer Learning with Pre-trained Models (MobileNet, ResNet)
RNN & LSTM (Concept Overview)
Training on GPU with Google Colab

Text Preprocessing (Tokenization, Stopwords, Stemming, Lemmatization)
Text Representation (Bag of Words, TF-IDF)
Word Embeddings (Word2Vec Concept)
Text Classification & Sentiment Analysis
Transformers & Attention Mechanism (Intuition)
Hugging Face Pipelines (Sentiment Analysis, NER, Summarization, Zero-shot Classification)

What are Large Language Models? (GPT, Gemini, Claude, Llama)
How LLMs Work (Tokens, Context Window, Temperature)
Prompt Engineering Basics (Zero-shot, Few-shot, Structured Output)
Calling an LLM API from Python
Embeddings & Semantic Search
Retrieval-Augmented Generation (RAG) – Concept & Demo
Limitations & Responsible AI (Hallucination, Bias, Data Privacy)

Building Interactive ML Apps with Streamlit
Creating REST APIs for Models with FastAPI
Managing Requirements & Environments
Docker Basics (Demo)
Deploying to Streamlit Community Cloud & Hugging Face Spaces
Experiment Tracking with MLflow (Intro)
Model Monitoring & Data Drift (Concept)

Resume Building for Data Science Roles
LinkedIn Profile Optimization
GitHub Portfolio Review
Top 150 Python, SQL, Statistics & ML Interview Questions
Case Study Interview Practice
Mock Interviews (Technical & HR)
Soft Skills & Communication Sessions

House Price Prediction – Build a regression pipeline to predict property prices from location, size and amenities
Loan Default Prediction – Classify high-risk loan applicants and tune the model for recall
Customer Churn Predictor – Compare 5 ML models, tune the best one and explain predictions with SHAP; deployed as a Streamlit app
Customer Segmentation – Group customers using RFM analysis and K-Means clustering
Retail Sales EDA & A/B Test – Clean a messy sales dataset, find insights and test a business hypothesis
Product Review Sentiment Analyzer – Compare TF-IDF + Logistic Regression with a Hugging Face transformer
Image Classifier – Classify images using CNN and transfer learning

Insurance Claim Fraud Detection – Detect fraudulent claims on imbalanced data with explainable predictions
Hospital Readmission Risk Predictor – Predict patient readmission and deploy a risk calculator app
Retail Demand Forecasting System – Forecast weekly demand with Prophet and XGBoost, served through a FastAPI endpoint
Credit Risk Scoring Model – Build a scorecard-style model with threshold and cost analysis
Customer Lifetime Value & Segmentation – Predict CLV and create targeted customer segments
Crop Disease Detection – Identify plant diseases from leaf images using deep learning

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Benefits beyond learning

Github profile

LinkedIn profile

Resume writing

Soft skills

Interview preparation

What Our Learners Say About Us

Aditi Deshpande

The Data Science JFP at Wisdom Sprouts took me from Python basics to building real Machine Learning models. The SHAP and model deployment sessions made my resume stand out.

Rohan Kale

Statistics and ML concepts that used to confuse me finally made sense with hands-on projects. The mentors broke down regression, classification, and deep learning step by step.

Shruti Patil

Building and deploying a Streamlit app for my churn prediction project was the highlight of the course. Mock interviews helped me crack my first Data Analyst role.

Rohan Kale

Statistics and ML concepts that used to confuse me finally made sense with hands-on projects. The mentors broke down regression, classification, and deep learning step by step.

Frequently Asked Questions