Curriculum
10 Sections
45 Lessons
10 Weeks
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Introduction to Data Science
4
1.1
What is Data Science?
1.2
Data Science Life Cycle
1.3
Applications in Industry (Healthcare, Finance, Marketing, etc.)
1.4
Roles in Data Science (Analyst, Scientist, Engineer)
Python for Data Science
5
2.1
Python Basics (Variables, Data Types, Operators)
2.2
Control Flow and Loops
2.3
Functions and Modules
2.4
Working with Libraries: NumPy, Pandas
2.5
Jupyter Notebook Basics
Data Analysis with Pandas
4
3.1
DataFrames and Series
3.2
Importing & Exporting Data (CSV, Excel, JSON)
3.3
Data Cleaning (Missing Values, Duplicates, Renaming)
3.4
Data Manipulation (Filtering, Sorting, Grouping, Merging)
Data Visualization
5
4.1
Importance of Visualization
4.2
Matplotlib Basics
4.3
Seaborn for Statistical Plots
4.4
Plot Types: Line, Bar, Histogram, Box, Scatter
4.5
Real-World Data Visualization Projects
Statistics for Data Science
5
5.1
Descriptive Statistics: Mean, Median, Mode, Variance
5.2
Probability Basics
5.3
Distributions: Normal, Binomial, Poisson
5.4
Inferential Statistics: Confidence Intervals, Hypothesis Testing
5.5
Correlation & Covariance
Exploratory Data Analysis (EDA)
5
6.1
EDA Workflow and Tools
6.2
Outlier Detection
6.3
Feature Engineering
6.4
Summary Statistics
6.5
Real-world EDA Mini-Project
Introduction to Machine Learning
5
7.1
What is Machine Learning?
7.2
Types of Machine Learning (Supervised vs Unsupervised)
7.3
Common Algorithms (Linear Regression, KNN, Decision Tree)
7.4
Model Training and Testing
7.5
Overfitting and Underfitting
Working with Real Datasets
3
8.1
Data Collection from APIs
8.2
Web Scraping Basics
8.3
Mini Project: Analyze a Public Dataset (e.g., Titanic, Iris, COVID-19)
Tools & Platforms
4
9.1
Git & GitHub Basics for Data Science Projects
9.2
Introduction to Google Colab
9.3
Version Control in Data Science
9.4
Cloud Basics (Google Cloud / AWS overview)
Capstone Project
5
10.1
Define a real-world problem
10.2
Clean and preprocess data
10.3
Analyze and visualize the dataset
10.4
Build a basic ML model
10.5
Present insights and recommendations
Data Science Fundamentals
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