Lilly Tech Systems
Learn Data Science
Master the complete data science workflow from data collection to insights. Learn Python, statistics, visualization, and real-world analysis techniques — all for free.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is data science? Explore the lifecycle, roles, skills, and industries driving data-powered decisions.
2. Python for Data Science
Essential Python libraries — NumPy, Pandas, Matplotlib — with hands-on code examples.
3. Data Collection & Cleaning
Gather data from APIs, files, and databases. Clean missing values, duplicates, and outliers.
4. Exploratory Data Analysis
Descriptive statistics, distributions, correlations, and discovering patterns in your data.
5. Data Visualization
Create impactful charts with Matplotlib, Seaborn, and Plotly. Learn storytelling with data.
6. Tools & Libraries
Jupyter, Google Colab, Scikit-learn, cloud platforms, and version control for data projects.
7. Best Practices
Reproducibility, ethics, privacy, bias awareness, documentation, and building your portfolio.
What You'll Learn
By the end of this course, you'll be able to:
Analyze Real Data
Load, clean, explore, and derive insights from real-world datasets using Python and Pandas.
Visualize Insights
Create compelling charts and dashboards that tell a clear story with your data.
Use Industry Tools
Work with Jupyter notebooks, cloud platforms, and the Python data science ecosystem.
Follow Best Practices
Build reproducible, ethical, well-documented data science projects from start to finish.