1000+ Students Enrolled

Data Science
With Machine Learning

A 12-week live, mentor-led program designed by industry experts to build strong foundations in Data Science and Machine Learning. Learn through hands-on projects and real-world datasets to analyze data and build predictive models.

4–12 LPA

Average Fresher Package

1000+

Students Trained

12 Weeks

Intensive Program

100%

Placement Assistance

Trusted By Learners

4.6/5

The most valuable takeaway was gaining practical, hands-on strategies that I can immediately apply to improve my work and productivity.

Manisha

5/5

I enrolled in Edspark for Data Science, and the learning experience has been clear, practical, and easy to understand with great mentor support.

Manish Kashyap

4.5/5

The sessions gave clear insights into how data scientists work in real-world problem-solving scenarios, with practical examples that made concepts easy to apply.

Parth Suri

4.5/5

The workshop was well-structured, making complex ideas easy to grasp. Recorded sessions helped revisit concepts at our own pace.

Debadrita Ghosh

— BENEFITS —

From This Program You'll Gain

🐍

Python for Data Science

Learn Python programming and essential libraries used in data science.

📈

Data Visualization

Create clear visual representations of data using popular visualization tools.

🤖

Machine Learning Algorithms

Understand and implement key machine learning algorithms for predictive models.

🧠

Model Building

Learn how to build, train, and evaluate machine learning models effectively.

📊

Feature Engineering

Prepare and transform datasets to improve machine learning model performance.

🚀

Hands-on Projects

Work on real-world machine learning projects to gain practical experience.

— WHY DS WITH ML —

Data Science With Machine Learning

📊

Learn how to work with real-world datasets and perform data preprocessing, analysis, and visualization to uncover meaningful insights.

🐍

Build strong programming skills using Python, Pandas, NumPy, and Matplotlib to analyze and manipulate large datasets efficiently.

🤖

Understand and implement machine learning algorithms such as regression, classification, and clustering for predictive modeling.

🚀

Work on end-to-end data science projects including data cleaning, model building, evaluation, and deployment to solve real-world problems.

Structured Curriculum – Data Science With Machine Learning

Beginner Level

Intermediate Level

Advanced Level

Beginner — Weeks 1–2

✔ Introduction to Data Science

✔ Data Science lifecycle

✔ Real-world applications

✔ Industry roles in Data Science

✔ Python fundamentals

✔ Variables, loops, functions

✔ Writing Python in Jupyter Notebook

✔ Python for data analysis basics

Intermediate — Weeks 3–6

✔ NumPy for numerical computing

✔ Data analysis using Pandas

✔ Dataset exploration techniques

✔ Data cleaning & preprocessing

✔ Handling missing values

✔ Feature engineering basics

✔ Statistics for Data Science

✔ Data visualization (Matplotlib, Seaborn)

✔ Exploratory Data Analysis (EDA)

✔ Machine Learning concepts

✔ Supervised vs Unsupervised learning

✔ ML workflow understanding

Advanced — Weeks 7–12

✔ Regression models

✔ Classification algorithms

✔ Scikit-Learn implementation

✔ Decision Trees & Random Forest

✔ Model evaluation metrics

✔ Feature selection techniques

✔ Hyperparameter tuning

✔ Machine learning pipelines

✔ K-Means clustering

✔ PCA dimensionality reduction

✔ Deep Learning fundamentals

✔ TensorFlow / Keras basics

✔ Real-world ML project

✔ Final portfolio project

Industry-Relevant Data Science with Machine Learning Projects

Build real-world machine learning projects using industry datasets to strengthen your portfolio.

Movie Recommendation System

Build a machine learning recommendation engine using collaborative filtering to suggest movies based on user preferences.

Sentiment Analysis on Social Media

Use Natural Language Processing and machine learning to analyze tweets and classify public sentiment as positive, negative, or neutral.

Ride Demand Forecasting

Apply time-series machine learning techniques to predict ride demand and analyze traffic patterns.

Music Genre Classification

Train machine learning models to classify songs into genres using audio features and data visualization techniques.

Don’t wait to start
your Data Science with Machine Learning journey.

Thousands of professionals have upgraded their careers with real-world data science and machine learning skills. Your transformation into a Data Scientist could be next.

Next data science batch filling fast — limited seats available!

What You Will Achieve

✔ Strong foundation in Python, Data Analysis, and Machine Learning
✔ Ability to build predictive models and intelligent systems
✔ Hands-on experience with real-world datasets and projects
✔ Practical knowledge of model deployment and ML applications

Data Science
With Machine Learning