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Grow Grid Live Track • Guided Industrial Training

Applied
Data Science

Master machine learning, predictive analytics, exploratory data analysis, and model deployment. Build end-to-end data pipelines and production-ready AI solutions.

Duration: 8–12 Weeks (Hybrid) Level: Beginner → Advanced Mode: Recorded + Project Centric

Internship Vision

Real-World Data Wrangling

Gain hands-on experience collecting, cleaning, and preprocessing complex raw datasets.

Machine Learning & Analytics

Build, evaluate, and optimize supervised and unsupervised machine learning models.

Visual Storytelling

Create executive dashboards in Power BI/Tableau and present business insights.

Deployment & MLOps

Learn industry deployment practices, API creation with Flask/FastAPI, and version control.

Competencies

Core Learning Highlights

Data Science Fundamentals
Data Cleaning & Preprocessing
Exploratory Data Analysis (EDA)
Machine Learning Algorithms
Predictive Analytics
Natural Language Processing (NLP)
Data Visualization & Dashboards
Model Deployment & REST APIs
MLOps Basics
Business Insights & Data Storytelling
Statistical Hypothesis Testing
Portfolio Case Study Creation
Curriculum Roadmap

Phase-Wise Learning Journey

Key Topics Covered

  • Introduction to Applied Data Science & Real-World AI Applications
  • Data Science Lifecycle & Industry Use Cases
  • Tools & Environment Setup (Jupyter Notebook, Anaconda, Google Colab)
  • Version Control with Git & GitHub
  • Introduction to Cloud Computing Platforms
  • Team Formation, Collaboration Setup & Industry Project Selection

Hands-On Projects

Mini Project 1: Data Science Environment & Repository Setup

Establish a fully configured cloud analytics environment and setup automated Git workflows.

Data Science Stack & Toolkit

Gain hands-on experience with industry-standard data science, ML, and visualization technologies.

PythonJupyter NotebookGoogle ColabScikit-learnTensorFlowPandasNumPyMatplotlibTableauPower BIFlaskFastAPIGitHub
Capstone Portfolio

Real-World Data Science Capstones

Predictive Analytics

Sales Forecasting for Retail

Build time-series and regression models to forecast multi-store retail sales, optimizing inventory management and revenue strategies.

Machine Learning & Classification

Customer Churn Prediction

Analyze customer behavior data to predict subscription churn rates and identify key retention factors using ensemble ML techniques.

Natural Language Processing

Sentiment Analysis Engine

Develop an NLP pipeline to process unstructured customer feedback and classify sentiments across product categories in real time.

AI & Conversational Systems

AI Chatbot Development

Construct an intelligent intent-driven AI conversational agent capable of handling dynamic customer support inquiries.

Career Readiness

Program Learning Outcomes

Gain practical experience in solving real-world data science problems
Develop machine learning, data wrangling, and predictive analytics skills
Build professional dashboards and deploy production AI models via APIs
Improve collaboration, presentation, and data storytelling abilities
Create a strong portfolio of industry-level data science projects
Be prepared for roles in Data Science, Machine Learning, Analytics, and AI