Data Science Program
Analyze real-world datasets, perform exploratory data analysis (EDA), and build predictive machine learning models in Python.
Track Role
Data Science Intern
Program Overview
Data Science is one of the most in-demand fields in technology. In this 4-week program, you will work with Python libraries like Pandas, NumPy, Matplotlib, Seaborn, and Scikit-Learn to extract actionable insights from raw data and train predictive ML models.
Key Program Outcomes
Technologies & Tools Mastered
Guaranteed Deliverables
Detailed 4-Week Curriculum
Every week builds on the last, taking you from fundamentals to capstone project deployment.
Python for Data Analysis & NumPy
Learn Python data structures, vector mathematics with NumPy arrays, and environment setup in Jupyter Notebooks.
Topics Covered:
Data Wrangling & Visualization with Pandas
Master Pandas DataFrames for data manipulation, filtering, grouping, and create statistical charts with Seaborn.
Topics Covered:
Supervised Machine Learning Models
Understand machine learning algorithms — Linear Regression, Logistic Regression, Decision Trees, and Random Forests.
Topics Covered:
Capstone Project & Model Deployment
Execute a real-world predictive data science project, generate insights report, and build an interactive Streamlit app.
Topics Covered:
What Are Verified Credentials?
Upon completion of your 4-week program, you receive an official verified certificate featuring a unique Credential ID and QR code. Recruiters and employers can verify your certificate instantly on Trainlyze.
Frequently Asked Questions
Common questions regarding the Data Science program.
Do I need advanced math or statistics?
Basic high school math is sufficient. We teach the statistical concepts required during the program.
What datasets will we use?
You will work with real-world datasets from finance, healthcare, e-commerce, and housing markets.
Ready to Master Data Science?
Join hundreds of trainees in our upcoming 4-week batch. Work on real projects and earn verified credentials.
Apply Now — Free