Programme Parameters
Foundation
6 Weeks (60 Hrs)
Online (e-LMS)
• No prior experience required — basic computer literacy is sufficient.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
The Data Science and AI for Beginners course introduces key concepts of data science and artificial intelligence, perfect for those just starting out. Over 6 weeks, you'll learn how to handle data, understand machine learning basics, and use industry-standard AI tools. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
The Data Science and AI for Beginners course introduces key concepts of data science and artificial intelligence, perfect for those just starting out. Over 6 weeks, you'll learn how to handle data, understand machine learning basics, and use industry-standard AI tools.
📋 Course Objectives
1. Apply biotechnology methods to authentic research and industry problems.
2. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
🚀 Key Learning Outcomes
• A demonstrable biotechnology project for your research or industry portfolio.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Introduction to Data Science (2 Weeks)
Overview of data science and its role in AI • Key techniques in data collection, cleaning, and analysis • Structured vs. unstructured data explained • Introduction to Python for data handling
Fundamentals of Machine Learning (2 Weeks)
Supervised vs. unsupervised learning • Key algorithms: Decision Trees, K-Nearest Neighbors, Linear Regression • Using Scikit-learn to build machine learning models • Evaluating model performance: metrics and error measurement
Core AI Tools – Pandas, NumPy, and Matplotlib (2 Weeks)
Using Pandas for data manipulation • NumPy for numerical computing and arrays • Data visualization with Matplotlib: charts, graphs, and plots • Hands-on project: Create a visual data report for business insights
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Hugging Face |
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