Python for Data Science: From Arrays to Analysis

Master the essential Python libraries for data science: NumPy, pandas, matplotlib, and seaborn. Learn to manipulate, analyze, and visualize data effectively, building a solid foundation for machine learning.

COURSE DATASHEETv2.1

Lessons10

Total runtime11h 0m

DifficultyIntermediate

CategoryPython Programming

TierFree

Last updated2025-11-26

LocaleEN · RU · ZH

§A · OBJECTIVES

What you’ll be able to do.

01.Master NumPy for efficient numerical computing

02.Use pandas for data manipulation and analysis

03.Create compelling visualizations with matplotlib and seaborn

04.Perform exploratory data analysis (EDA) workflows

05.Understand statistical foundations for data science

06.Work with various data formats (CSV, JSON, APIs)

07.Complete an end-to-end data analysis project

§B · INSTRUMENTS IN THIS COURSE

Working tools, used throughout.

Data Structure Visualizer

FIG. 04INTERACTIVE

Data Visualization Dashboard

FIG. 05CODE-EXECUTION

Python Code Executor

FIG. 06VISUALIZATION

Flow Diagram

§C · SYLLABUS

All lessons. Read in order, or jump.

№LESSONKINDTIME

01NumPy Fundamentals: The Foundation of Scientific PythonCONCEPT· 75 min75 min→02Advanced NumPy: Linear Algebra and Array ManipulationCONCEPT· 60 min60 min→03Pandas Basics: Series and DataFramesCONCEPT· 75 min75 min→04Data Wrangling with Pandas: Transform and CleanCONCEPT· 75 min75 min→05Data Input/Output: Loading and Saving DataCONCEPT· 45 min45 min→06Matplotlib Fundamentals: Creating Publication-Quality PlotsCONCEPT· 60 min60 min→07Seaborn: Statistical Data VisualizationCONCEPT· 60 min60 min→08Exploratory Data Analysis: Discovering InsightsCONCEPT· 75 min75 min→09Statistical Foundations for Data ScienceCONCEPT· 60 min60 min→10Project: End-to-End Data AnalysisCONCEPT· 75 min75 min→