Lilly Tech Systems
Python for Data Science
Master the essential Python libraries and techniques for data science — from NumPy and Pandas to visualization, statistical analysis, and Jupyter Notebooks.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Why Python dominates data science, the DS ecosystem, environment setup, and career paths.
2. NumPy
Arrays, operations, broadcasting, indexing, statistical functions, and linear algebra.
3. Pandas
Series, DataFrames, indexing, filtering, groupby, merge, join, and pivot tables.
4. Data Cleaning
Missing data, duplicates, type conversion, outlier detection, feature scaling, and encoding.
5. Visualization
Matplotlib, Seaborn, Plotly interactive charts, customization, and choosing the right chart.
6. Statistical Analysis
Descriptive stats, probability distributions, hypothesis testing, correlation, and A/B testing.
7. Jupyter Notebooks
Setup, cells, shortcuts, magic commands, extensions, JupyterLab, and Google Colab.
8. Best Practices
Project structure, reproducibility, memory optimization, large datasets, and documentation.
What You'll Learn
By the end of this course, you'll be able to:
Analyze Data with Pandas
Load, clean, transform, and analyze datasets using industry-standard tools.
Create Visualizations
Build compelling charts and dashboards with Matplotlib, Seaborn, and Plotly.
Apply Statistics
Run hypothesis tests, compute correlations, and implement A/B testing.
Use Jupyter Effectively
Master interactive notebooks for exploratory data analysis and reporting.