Data Science

— Beginner Level Roadmap

Module 1: Introduction to Data & Its Applications

  • What is Data? Types (Structured vs. Unstructured)

  • Importance of Data in Business, Health, Finance, and AI

  • Roles in Data Science (Data Analyst, Data Scientist, ML Engineer)

  • Real-world case studies (Netflix, Amazon, Healthcare)

Outcome: Understand why data matters and the career opportunities in data science.

Module 2: Python Programming Basics

  • Setting up Python & Jupyter Notebook/Google Colab

  • Understanding Syntax, Indentation & Comments

  • Variables & Data Types (int, float, string, boolean)

  • Basic Input/Output

  • Operators (Arithmetic, Logical, Relational)

Practice: Write a program to calculate BMI or convert Celsius to Fahrenheit.

Module 3: Data Types, Variables & Operators in Depth

  • Lists, Tuples, Sets, Dictionaries

  • Indexing & Slicing

  • Conditional Statements (if, elif, else)

  • Loops (for, while)

  • Functions (def, parameters, return values)

Mini-Task: Write a Python function to check if a number is prime.

Module 4: Fundamental Statistics

  • Descriptive Statistics (Mean, Median, Mode, Range, Variance, Std. Deviation)

  • Probability Basics (Events, Outcomes, Probability Rules)

  • Visualizing Data Distribution (Histograms, Box Plots)

  • Sampling and Population concepts

Mini-Task: Analyze a dataset (e.g., student marks) to calculate averages and visualize distributions.

Module 5: Introduction to Data Visualization

  • What is Data Visualization?

  • Using Matplotlib: Line, Bar, Pie, Scatter plots

  • Using Seaborn: Heatmaps, Pairplots, Histograms

  • Styling Charts (labels, titles, legends)

Practice: Visualize COVID-19 cases by country using a CSV dataset.

Module 6: Working with Excel & CSV Files

  • Reading/Writing CSV files using Pandas

  • Loading Excel sheets

  • DataFrames: rows, columns, indexing

  • Filtering, Sorting & Grouping Data

  • Handling missing values

Practice: Import an Excel sales dataset and generate a monthly sales summary.

Module 7: Mini Project — Simple Data Analysis with Python

Project Idea: Student Performance Analysis

  • Dataset: Student exam scores

  • Tasks:

    • Load and clean data with Pandas

    • Calculate averages and identify top performers

    • Visualize performance trends using Matplotlib/Seaborn

    • Write a short report summarizing insights

Outcome: Apply Python, Statistics, and Visualization together in one project.

✅ By the end of the Beginner Level, students will be able to:

  • Write Python programs for data analysis

  • Understand and apply basic statistics

  • Handle Excel & CSV datasets

  • Visualize data effectively

  • Complete a beginner-level project

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