Python for Data Analysis: Solve Real-World Challenges
1h 57mIntermediate2023-03-21
Authors
Sarah Nooravi
Data Analyst and Educator
Course details
As data and data-related jobs have grown over the past decade, so has the demand for data skills. If you’re a data professional, this is great news! However, it’s important to continue to adapt to the changing demands of the market, which include adopting tools like Python to approach big data challenges. In this course, Sarah Nooravi shares a practical, project-based examination of Python, covering the skills needed to help you stand out among others in a competitive market. Sarah walks through an end-to-end Python analysis, typical of what you would encounter on the job– starting from the problem statement and takes you all the way through to insight delivery. Sarah helps you break down the problem, set expectations, and covers best practices around data cleaning, data visualization and storytelling. Lastly, she also shares common pitfalls and critical business and soft skills that will help you stand out.
Skills covered
Data EngineeringPythonProjectData AnalysisProgramming LanguagesData ScienceBusiness Analysis and StrategyBusiness Software and ToolsOpen SourceSoftware Development
Concepts
Introduction
- Introduction
- Prerequisites
Case Study Introduction
- The who, what, where, and why
- Who
- What
- Where
- Why
- Case study introduction
Breaking Down the Problem Statement
- Define the problem - The three Ds
- Get familiar - Domain
- Domain - Applied
- Get familiar - Data
- Get familiar - Deliverable
- Set the right expectations
Data Collection, Cleaning, and Transformation
- Getting set up in Codespaces
- Read in data from a CSV file
- General cleaning techniques
- General cleaning techniques - High-level checks
- General cleaning techniques - Missing values
- Data transformations - Binning
EDA
- Introduction to EDA
- Summary statistics
- Distributions - Histograms
- Data transformations - Normalization and log
- Other distribution types
- Data visualizations - Comparing categories
- Data visualization - Data tables
- Data visualization - Relationships
- Create a ridge plot
Data Visualization and Storytelling
- Visual best practices - Part 1
- Visual best practices - Part 2
- Leverage exploratory and explanatory visualizations
- Choose a medium
- Storyboarding
- What's in a story
- Putting it all together
Conclusion
- Wrap-up and next steps