Saraswate Data Labs | Data Science & Machine Learning Course

Data Science & Machine Learning — Foundations to Projects

Course Details

Everything you need to know before joining our next batch.

Start Date

31 October 2026

Upcoming weekend batch commencement

Learning Mode

Live Online via Zoom

Interactive classes with real-time doubt resolution

Class Schedule

Sat & Sun, 4:00 PM – 7:00 PM

2 sessions per week (3 hours each)

Course Duration

3 Months

12 structured weeks across all milestones

Live Teaching

72 Live Hours

Calculation: 2 sessions/wk × 3 hrs × 12 wks = 72 hrs.
Live instruction only · Independent practice not counted.

Language

Hindi & English

Bilingual delivery for easy concept retention

Batch Size

Limited Students

Focused cohort for personal mentorship

Course Track

Data Science & Machine Learning

Foundations to Projects · Beginner-Friendly

Course Fee & Offer

₹6,999 ₹4,499 Save ₹2,500

Offer valid until 25 October 2026. Full course fee including all taxes.

Course Highlights

Key pillars of our practical curriculum and learning approach.

Structured Learning

Build foundations in Python, Statistics, SQL, and Machine Learning through a structured three-month learning path.

Live Weekend Classes

Learn in live sessions every Saturday and Sunday, from 4 PM to 7 PM.

Build Three Projects

Work with real-world-style datasets across Property Price Prediction (Mini Project 1) and Finding Insights Through Data Cleaning & EDA (Mini Project 2), plus a capstone where students choose between Loan Approval Prediction (Option A) or Customer Churn Prediction (Option B).

Beginner-Friendly

Start without prior programming experience. The course is open to learners from all educational backgrounds.

Open to all educational backgrounds | No prior programming experience required | Laptop required

Saraswate Data Labs — Where Concepts Become Projects.

The Learning Roadmap

Your Journey into Data Science

From your first Python program to building predictive models, develop your skills one milestone at a time.

5 Learning Milestones · One Practical Learning Journey

Python Programming

Build your coding foundation and learn to work with datasets using Python, NumPy and Pandas.

Statistics

Understand probability, distributions, descriptive statistics and hypothesis testing.

SQL & Data Analysis

Query, join, filter and summarize data stored in relational databases.

Classical Machine Learning

Explore regression, classification, clustering, model evaluation and introductory tuning.

Mini Projects & Capstone

Apply your learning to real-world-style datasets across Property Price Prediction (Mini Project 1) and Finding Insights Through Data Cleaning & EDA (Mini Project 2), followed by a capstone where students choose between Loan Approval Prediction (Option A) or Customer Churn Prediction (Option B).

Practical Learning Experience

Projects That Turn Knowledge Into Skills

Build practical data science skills with real-world-style datasets through module-wise assignments, Property Price Prediction (Mini Project 1), Finding Insights Through Data Cleaning & EDA (Mini Project 2), and a capstone where students choose between Loan Approval Prediction (Option A) or Customer Churn Prediction (Option B).

Assignments After Every Module

Reinforce each module with practical assignments designed to help you apply concepts, practise independently, and build confidence before moving forward.

Reinforce concepts from each completed module.

Practise coding, data analysis, and problem-solving.

Build confidence through independent implementation.

The Learning Cycle

Learn a Concept

Practise the Skill

Complete Assignment

Next Module

Apply your foundational skills to real-world-style datasets through two guided projects focused on prediction and data analysis.

MINI PROJECT 01

Property Price Prediction

Explore property data, prepare relevant features, and develop a machine learning model to estimate property prices.

Data Preprocessing Feature Engineering Regression

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Objective: Clean real estate listings data and train a regression model to estimate asking prices based on square footage, location, and amenities.

Workflow: Impute missing data → Encode categorical features → Feature scaling → Model training (Linear Regression, Decision Trees) → Evaluation (RMSE, R²).

MINI PROJECT 02

Finding Insights Through Data Cleaning & EDA

Clean an imperfect dataset, investigate missing values and inconsistencies, explore distributions and relationships, and communicate meaningful insights.

Data Cleaning Exploratory Data Analysis Data Visualization

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Objective: Transform raw, noisy data into a structured format and extract actionable insights through statistical summaries and visual mapping.

Workflow: Detect outliers → Handle duplicates & nulls → Univariate & Bivariate analysis → Correlation heatmaps → Present findings.

Bring together your learning in a comprehensive final project using real-world-style datasets. Choose between Loan Approval Prediction (Option A) or Customer Churn Prediction (Option B) and develop your end-to-end solution.

Choose either Option A or Option B for your capstone project.

CAPSTONE OPTION A

Loan Approval Prediction

Prepare applicant data, explore relevant features, and develop a classification model to predict loan approval outcomes.

Data Preparation Classification Model Evaluation

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Objective: Predict whether a loan application will be approved or rejected based on credit history, income, and loan characteristics.

Deliverable: A completed Jupyter Notebook demonstrating the end-to-end ML pipeline, evaluated for Precision and Recall, alongside a presentation of findings.

CAPSTONE OPTION B

Customer Churn Prediction

Analyse customer information, identify patterns associated with churn, and develop a classification model to predict which customers may leave a service.

Exploratory Analysis Classification Predictive Modelling

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Objective: Identify high-risk customers likely to cancel their subscriptions by analysing usage patterns and demographic attributes.

Deliverable: A fully functioning classification model tuned for maximum F1-Score, documented clearly with actionable business recommendations.

STUDENT EXPERIENCES

What Our Students Say

Hear from learners building their foundations in Data Science and Machine Learning.

Student Review

“Learning Data Science through Saraswate Data Labs helped me understand how Python, Statistics, and SQL fit into the overall data analysis workflow. The practical exercises made the concepts easier to understand, and working on projects helped me apply what I learned instead of just studying theory.”

Harsh Rawat

Student | Saraswate Data Labs

Student Review

“What I find useful about the course is its focus on practical learning. From understanding data to building machine learning models, the structured approach helps connect concepts step by step. The assignments and projects also provide a good opportunity to practise independently and improve problem-solving skills.”

Vikas Singh

Student | Saraswate Data Labs

Student Review

“The course is designed to make learning practical and structured. Working through Python, Statistics, SQL, and Machine Learning helps connect theoretical concepts with real-world applications. The hands-on assignments and projects provide opportunities to practise concepts and improve problem-solving skills.”

Balveer Saini

Student | Saraswate Data Labs

From Zero to Building Real Data Science Projects

Course Details

Course Highlights

Structured Learning

Live Weekend Classes

Build Three Projects

Beginner-Friendly

Your Journey into Data Science

Python Programming

Statistics

SQL & Data Analysis

Classical Machine Learning

Mini Projects & Capstone

Projects That Turn Knowledge Into Skills

Assignments After Every Module

The Learning Cycle

Property Price Prediction

Finding Insights Through Data Cleaning & EDA

Loan Approval Prediction

Customer Churn Prediction

What Our Students Say

Certificate of Completion Issued by Saraswate Data Labs

Frequently Asked Questions