Didacteo

Mastering Data Analysis with Python: Process, Visualize, and Interpret

Master Data Analysis with Python

D

Didacteo

Instructor

Cover image for Mastering Data Analysis with Python: Process, Visualize, and Interpret

Difficulty Level

Intermediate

Prior knowledge recommended

Estimated duration

519 min

Progress

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Introduction

What you’ll learn

  • Set up reproducible Python environments (Jupyter, virtualenv, Git/GitHub) for data projects
  • Clean, transform, and analyze datasets using NumPy and pandas
  • Design and deliver clear static and interactive visualizations with Matplotlib, Seaborn, and Plotly/Streamlit
  • Apply basic statistical reasoning (sampling, confidence intervals, hypothesis tests) and interpret results
  • Complete an end-to-end, reproducible capstone project and produce stakeholder-ready reports

Prerequisites

  • Basic Python programming (variables, control flow, functions)
  • Familiarity with high-school level math and basic statistics concepts (mean, variance, correlation)
  • Computer with internet access and permission to install Python/Anaconda and packages

Course content

Explore the lessons included in this program.

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Course Introduction

Welcome and Course Overview
What Is Data Analysis and Where It's Used
Learning Path, Outcomes, and Capstone Project

Setup: Python Environment for Data Analysis

Installing Python, pip, and Anaconda Options
Working with Jupyter Notebooks and JupyterLab
Virtual Environments and Package Management
Introduction to Git, GitHub, and Reproducible Workflows

Numerical Computing with NumPy

Introduction to NumPy Arrays and Advantages over Lists
Indexing, Slicing, and Array Manipulation
Vectorized Operations and Performance Considerations
Basic Linear Algebra and Random Sampling with NumPy

Data Manipulation with pandas

pandas Data Structures: Series and DataFrame
Importing and Exporting Data (CSV, Excel, SQL, JSON)
Data Cleaning: Handling Missing Data and Duplicates
Transformations: Filtering, Sorting, GroupBy, Aggregation
Merging, Joining, Reshaping, and Working with Dates

Exploratory Data Analysis (EDA)

Descriptive Statistics and Summaries
Detecting Outliers and Data Quality Issues
Data Profiling and Automated EDA Tools
Feature Engineering and Preparation for Modeling

Foundations of Data Visualization

Principles of Effective Visualization and Chart Types
Choosing the Right Visualization for Your Question
Color, Layout, and Accessibility Considerations
Storytelling with Data and Annotating Charts

Static Visualization with Matplotlib and Seaborn

Matplotlib Basics: Figures, Axes, and Common Plots
Seaborn for Statistical Visuals: Distributions and Categorical Plots
Customizing Plots: Labels, Legends, Styles, and Themes
Multivariate Visualization and Faceting

Interactive Visualization and Dashboards

Introduction to Plotly Express for Interactive Charts
Building Simple Dashboards with Streamlit or Dash
Interactivity: Hover, Filters, and Linked Views
Sharing, Exporting, and Deploying Visualizations

Basic Statistical Thinking for Data Analysis

Probability Concepts and Common Distributions
Sampling, Confidence Intervals, and Estimation
Hypothesis Testing and p-values Explained
Introduction to Correlation and Linear Regression

Interpreting Results and Communicating Insights

Translating Analysis into Actionable Insights
Creating Reproducible Reports with Jupyter and nbconvert
Designing Presentations and Dashboards for Stakeholders
Ethics, Privacy, and Avoiding Common Analytical Pitfalls

Applied Projects and Next Steps

Mini-Project 1: Exploratory Analysis Case Study
Mini-Project 2: Visualization and Storytelling Case Study
Capstone: End-to-End Data Analysis Project
Portfolio, Continuing Learning Paths, and Career Tips