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Data Science & Analytics

Er Yogendra Singh Rajput December 13, 2025
Winter Training - Data Science & Analytics
Winter Training 2025

Master Data
SCIENCE

Transform raw data into actionable insights. Master Python, SQL, Tableau, and Machine Learning algorithms on real datasets.

Start Analyzing Download Report

SQL

Database

BI

Visualization

ML

Prediction

Jupyter Notebook - Analysis.ipynb
In [1]:
Sales Prediction (2025)
JanFebMarAprMayJun
Data Science Curriculum

Detailed Topic Breakdown

A rigorous curriculum covering Data Analysis, Database Management, Statistical Modeling, and Business Intelligence.

Module 1

Python & Stats

Building the foundation with Python programming, NumPy, and statistical mathematics.

Week 1 (Day 1-7)

Part A: Python for Data

  • Python Env Setup (Anaconda/Jupyter)
  • Variables, Lists, Dictionaries, Sets
  • List Comprehensions & Lambda Functions
  • File I/O (CSV, Excel, JSON handling)
  • Exception Handling in Data Pipelines
  • NumPy Arrays (1D, 2D operations)
  • Broadcasting & Vectorization
  • Indexing & Slicing Arrays
  • NumPy Math Functions
  • Random Number Generation

Part B: Statistics

  • Measures of Central Tendency
  • Dispersion (Variance, Std Dev)
  • Probability Distributions (Normal, Binomial)
  • Central Limit Theorem
  • Hypothesis Testing (Z-Test, T-Test)
  • P-Values & Confidence Intervals
  • Correlation vs Causation
  • Covariance Matrix
  • ANOVA Test
  • Chi-Square Test
Module 2

Data Wrangling

Mastering Pandas for data manipulation and SQL for database querying.

Week 2 (Day 8-14)

Part A: Pandas Mastery

  • Series vs DataFrames
  • Loading & Saving Data
  • Data Cleaning (Null Values, Duplicates)
  • Data Filtering & Selection (loc, iloc)
  • Group By & Aggregation
  • Merging, Joining & Concatenating
  • Pivot Tables & Cross-Tabs
  • Time Series Data Handling
  • Apply & Map Functions
  • String Operations in Pandas

Part B: SQL for Data

  • RDBMS Concepts (MySQL/PostgreSQL)
  • SELECT, WHERE, DISTINCT
  • Aggregate Functions (SUM, AVG, COUNT)
  • GROUP BY & HAVING Clauses
  • Joins (Inner, Left, Right, Full)
  • Subqueries & Nested Queries
  • Window Functions (RANK, ROW_NUMBER)
  • Common Table Expressions (CTE)
  • Connecting SQL with Python
  • Data Normalization
Module 3

Visual Analytics

Creating compelling stories with data using Python libs and Tableau/PowerBI.

Week 3 (Day 15-21)

Part A: Python Viz

  • Matplotlib Anatomy of a Figure
  • Line, Bar, Scatter, Pie Charts
  • Seaborn Statistical Plots
  • Heatmaps & Pair Plots
  • Box Plots & Violin Plots
  • Customizing Themes & Styles
  • Plotly for Interactive Graphs
  • Geospatial Plotting (Folium)
  • Subplots & Grid Layouts
  • Exploratory Data Analysis (EDA)

Part B: Tableau/PowerBI

  • Connecting to Data Sources
  • Dimensions vs Measures
  • Creating Basic Charts in Tableau
  • Calculated Fields & Parameters
  • Dashboards & Storytelling
  • Filters & Slicers
  • Data Blending & Joins
  • Geographic Mapping
  • Publishing Reports
  • DAX Basics (PowerBI)
Module 4

ML & Capstone

Introduction to Predictive Modeling and working on Real-world Projects.

Week 4 (Day 22-28)

Part A: ML Basics

  • Supervised vs Unsupervised
  • Linear Regression Algorithm
  • Logistic Regression
  • Decision Trees & Random Forest
  • K-Means Clustering
  • Train/Test Split (Scikit-Learn)
  • Model Evaluation (RMSE, Accuracy)
  • Confusion Matrix & ROC Curve
  • Feature Engineering
  • Overfitting & Underfitting

Part B: Major Projects

  • Project 1: Sales Forecasting (Time Series)
  • Project 2: Customer Segmentation (Clustering)
  • Project 3: Titanic Survival Prediction
  • Project 4: IPL Data Analysis
  • Project 5: Sentiment Analysis (NLP Intro)
  • Web Scraping (BeautifulSoup)
  • Big Data Intro (Spark/Hadoop)
  • GitHub for Data Scientists
  • Resume Building
  • Final Presentation

Download Certification Resources

Access the complete 30+ page Project Report, Jupyter Notebooks, Datasets, and Slides.

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