Overview

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Data science in Python comes down to having the right functions ready at the right moment, not memorizing the entire language from the ground up. This course covers the tools that data analysts and data scientists rely on daily: Python's own built-in functions, NumPy, SciPy, pandas, matplotlib, and seaborn. Rather than treating each library as an abstract topic, you'll build a single, practical toolkit one skill at a time. You'll inspect, aggregate, sort, and filter data using nothing but Python's built-in functions, then move into NumPy to create and transform numeric arrays for fast, large-scale calculations. From there, you'll use SciPy to solve matrix-based problems and test whether a difference in your data is statistically meaningful. You'll bring that same data into pandas, where you'll build, clean, and reshape real tabular datasets, combining and grouping information exactly the way a real analysis project demands. Finally, you'll turn your findings into clear, presentation-ready charts with matplotlib and seaborn. By the end of this course, you'll be able to inspect, transform, analyze, and visualize real datasets using Python's core data science toolkit, ready to apply immediately to your own data.

Syllabus

Core Python Essentials
- Your Python environment needs to be ready before you can put any data science skill to use. In this module, you'll set up Python and Jupyter for hands-on practice and apply built-in functions to inspect your data's values and types.
Aggregating and Transforming Data with Python's Built-In Functions
- Your raw data won't sort, size up, or summarize itself. In this module, you'll apply Python's built-in functions to control numeric precision, summarize a dataset with quick statistics, and sort, filter, and transform your data into the shape you actually need.
NumPy and SciPy Fundamentals
- Your datasets are only going to grow, and plain Python lists can't keep up forever. In this module, you'll create NumPy arrays, extract and slice their values, reshape them for different computations, and apply fast, vectorized operations across entire datasets at once.
Analyzing Data with NumPy and SciPy
- You can eyeball a dataset all day and still miss what actually matters inside it. In this module, you'll compute summary statistics, solve matrix-based problems, and run a hypothesis test to determine whether a difference in your data is real or just noise.
Building and Modifying Pandas Series and DataFrames
- Your data rarely arrives in the shape you actually need to work with it. In this module, you'll create pandas Series and DataFrames, load data from a CSV file, and modify your DataFrames by handling missing values and adding, dropping, and renaming columns.
Combining, Grouping, and Transforming Data with Pandas
- Your data almost never comes to you in one clean table. In this module, you'll combine data from multiple pandas objects, group it by category to compare patterns, and apply your own custom functions to transform it exactly the way you need.
Visualization Essentials
- Numbers on their own rarely convince anyone of anything. In this module, you'll create line, scatter, bar, and pie charts, examine distributions and pairwise relationships with seaborn, and organize multiple visualizations together using subplots.
Conclusion
- You've built a full toolkit of Python functions, and the only way to make it truly yours is to use it. In this module, you'll apply that full toolkit to a real dataset you choose, from inspecting it to visualizing your results.