🎓 Data Science for Data Scientist Career

🕒 Duration: 24 – 26 Weeks (Flexible, Modular)
🎯 Prerequisite: Basic computer literacy and interest in problem-solving
🛠️ Tools & Tech:

• Python

• Jupyter Notebooks / Google Colab

• NumPy, Pandas, Matplotlib, Seaborn

• Scikit-learn

• TensorFlow / Keras (for DL)

• SQL (MySQL or PostgreSQL)

• Power BI or Tableau

• OpenCV (for optional vision projects)

Internship: 1 Month Internship program for selected candidates @ Srivin Tech Private Limited

📚 Modules & Topics Covered

📘 Module 1: Foundations of AI & Machine Learning

• Python programming for AI

• NumPy and Pandas for data handling

• Data cleaning and preprocessing

• Supervised & Unsupervised ML (Scikit-learn)

• Model evaluation (accuracy, recall, F1-score)

• Mini-projects (e.g., loan prediction, classification)

📊 Module 2: Data Science & Analytics with Python

• Advanced EDA (Exploratory Data Analysis)

• Data visualization using Matplotlib & Seaborn

• Feature engineering techniques

• Predictive modeling with Scikit-learn

• KPI Dashboards overview (Power BI or Tableau)

• Capstone: Real-world analytics project

🗃️ Module 3: SQL for Data Analysis

• SQL basics: SELECT, WHERE, JOIN, GROUP BY

• Filtering, aggregations, sorting

• Writing subqueries, window functions

• Connecting SQL with Python (using Pandas)

• Hands-on: Analyze and query real business datasets

📊 Module 4: Dashboards & Data Storytelling (Power BI / Tableau)

• Creating visual dashboards

• Importing datasets (Excel, CSV, SQL)

• Building charts, KPIs, slicers, and filters

• Publishing and sharing reports

• Use case: Business performance dashboard

🧠 Module 5: Deep Learning & Computer Vision

• Neural networks with TensorFlow/Keras

• Convolutional Neural Networks (CNNs)

• Image classification with OpenCV

• Training, tuning, saving deep learning models

• Capstone: Face Recognition or Image Classifier

✅ Outcome:

Upon completing this learning path, students will be equipped to:

• Analyze and visualize real-world datasets

• Build and evaluate machine learning models

• Query and manage data using SQL

• Present insights through professional dashboards

• (Optionally) implement AI-powered image-based applications