The Data Science Training at Netpro Training Institute in Pune offers an industry-aligned curriculum that transforms raw data into actionable business insights through advanced analytics. This course covers the end-to-end data lifecycle, including Python and R programming, statistical modeling, and data visualization using tools like Power BI and Tableau.

1. Data Foundations
• Python for Data Science: Move beyond basic syntax. Focus on Pandas (for dataframes) and NumPy (for numerical arrays).
• SQL (The Most Important Skill): Learn to extract data from databases. Focus on Joins, Subqueries, and Window Functions.
• Mathematics & Statistics: * Descriptive Stats: Mean, Median, Standard Deviation, and Outliers.
• Inferential Stats: Hypothesis testing and p-values (crucial for A/B testing)
• Linear Algebra: Understanding how data is stored as matrices.

2. Exploratory Data Analysis (EDA)
• Data Cleaning: Handling missing values, duplicates, and inconsistent formatting.
• Visualization: Master Matplotlib and Seaborn. Learn to tell a story with data—choose the right chart (Boxplots for outliers, Heatmaps for correlations).
• Feature Engineering: Creating new data points from old ones (e.g., turning a "Date" column into "Day of Week" to find weekend trends).

3. Machine Learning & Modelingb>
• Supervised Learning: * Regression: Predicting numbers (e.g., House prices).
• Classification: Predicting categories (e.g., Is this email spam?).
• Unsupervised Learning: Clustering (segmenting customers based on behavior).
• Model Evaluation: Don't just look at "Accuracy." Learn Precision, Recall, and F1-Score to understand if your model is truly working.

4. Modern AI & GenAI Integration
• Natural Language Processing (NLP): Learn how machines "read" text using Tokenization and Sentiment Analysis.
• Generative AI for Data: Use LLMs to explain your code, generate synthetic data for testing, or build a RAG (Retrieval-Augmented Generation) system to query your own datasets using natural language.
• Deep Learning Basics: Introduction to Neural Networks using PyTorch or TensorFlow.

5. MLOps & Portfolio
• Portfolio Project: Pick a real-world dataset from Kaggle or UCI Machine Learning Repository.
• MLOps Lite: Learn to use GitHub for version control and Streamlit to turn your Python script into an interactive web app for your portfolio.
• Storytelling: Practice explaining your technical findings to a "non-technical manager."