Data Science Beginner
Data Science with Python
Master the art of data analysis, visualization, and predictive modeling.
8 Weeks
Project-Based Learning
About this Course
Master the art of data analysis, visualization, and predictive modeling. This comprehensive course is designed to take you from beginner concepts to advanced mastery. You will build real-world skills through hands-on practice and expert guidance.
Course Syllabus
Module 1: Python for Data Science
1 Week- Setting up the Environment (Anaconda, Jupyter Lab, Google Colab).
- Python Syntax Essentials: Variables, Data Types, Control Flow (If/Else, Loops).
- Functions and Lambda expressions.
- Key Concept: Understanding Python Lists vs. Dictionaries.
- Project: The Logic Builder – Build a simple script to automate a file organizing task."
Module 2: Numerical Computing with NumPy
1 Week- Introduction to Arrays and Matrix operations.
- Vectorization vs. Loops (Speed optimization).
- Basic statistical functions (Mean, Median, Standard Deviation).
- Project : Matrix Mastery – Perform statistical analysis on a dataset of students' exam scores without using loops.
Module 3: Data Analysis with Pandas (Part I)
1 Week- The DataFrame and Series objects.
- Loading Data (CSV, Excel, JSON).
- Indexing, Slicing, and Filtering data.
- Crucial Skill: Handling Missing Data (Imputation vs. Dropping).
- Project: Data Detective – Clean a "messy" dataset of customer records (fixing typos, filling null values)."
Module 4: Advanced Pandas (Part II)
1 Week- GroupBy and Aggregation (Pivot Tables).
- Merging, Concatenating, and Joining multiple datasets.
- Time Series Analysis (Handling dates and times).
- Feature Engineering (Creating new columns from existing data).
- Project : Retail Analyst – Analyze 12 months of sales data to find the best-performing financial quarter.
Module 5: Data Visualization Fundamentals
1 Week- The Grammar of Graphics.
- Matplotlib: Creating the canvas, subplots, and customizing axes.
- Seaborn: Statistical plots (Boxplots, Heatmaps, Pairplots) for distribution analysis.
- Project : Visual Audit – Create a static report showing the correlation between marketing spend and revenue."
Module 6: Exploratory Data Analysis (EDA)
1 Week- Univariate vs. Multivariate analysis.
- Identifying Outliers and Anomalies.
- Storytelling: How to title, label, and annotate charts for a non-technical audience.
- Project: The Insight Report – Perform a full EDA on a "Housing Price" dataset to determine what factors impact price the most."
Module 7: Introduction to Machine Learning
1 Week- Supervised Learning: Regression vs. Classification.
- Scikit-Learn Workflow: Train-Test Split, Model Fitting, Prediction.
- Model Evaluation: Accuracy, Precision, Recall, and RMSE.
- Algorithms: Linear Regression and Decision Trees.
- Project: The Predictor – Build a model to predict customer churn (or house prices)."
Module 8: Capstone Integration & Deployment
1 Week- Building an interactive frontend with Streamlit.
- Integrating the Data Pipeline and ML Model into the app.
- Final Code Review and Refactoring.
- Publishing to GitHub."
Capstone Project
Featured Project
E-commerce Sales Dashboard
Analyze real-world sales data to identify trends, top-selling products, and customer demographics, visualized in an interactive dashboard.
Key Features:
- Data Cleaning Pipeline
- Interactive Charts
- Sales Forecasting Model