Exploratory Data Analysis with Python
Exploratory Data Analysis (EDA) with Python is the process of using Python libraries to inspect, summarize, and visualize data to uncover trends, patterns, and relationships. It’s a crucial first step in data science projects that helps you understand your data before diving into more complex analysis or modeling techniques.
- Price: $279.99
- Delivery Method: eLearning
| Name | Buy |
|---|
Test Prep
35+ Pre Assessment Questions | 35+ Post Assessment Questions |
Features
77+ LiveLab | 13+ Video tutorials | 20+ Minutes
Why choose TOPTALENT?
- Get assistance every step of the way from our Texas-based team, ensuring your training experience is hassle-free and aligned with your goals.
- Access an expansive range of over 3,000 training courses with a strong focus on Information Technology, Business Applications, and Leadership Development.
- Have confidence in an exceptional 95% approval rating from our students, reflecting outstanding satisfaction with our course content, program support, and overall customer service.
- Benefit from being taught by Professionally Certified Instructors with expertise in their fields and a strong commitment to making sure you learn and succeed.
Outline
Lessons 1:
Preface
- Who this course is for?
- What this course covers?
- To get the most out of this course
- Conventions used
Lessons 2:
Exploratory Data Analysis Fundamentals
- Understanding data science
- The significance of EDA
- Making sense of data
- Comparing EDA with classical and Bayesian analysis
- Software tools available for EDA
- Getting started with EDA
- Summary
- Further reading
Lessons 3:
Visual Aids for EDA
- Technical requirements
- Line chart
- Bar charts
- Scatter plot
- Area plot and stacked plot
- Pie chart
- Table chart
- Polar chart
- Histogram
- Lollipop chart
- Choosing the best chart
- Other libraries to explore
- Summary
- Further reading
Lessons 4:
Activity: EDA with Personal Email
- Technical requirements
- Loading the dataset
- Data transformation
- Data analysis
- Summary
- Further reading
Lessons 5:
Data Transformation
- Technical requirements
- Background
- Merging database-style dataframes
- Transformation techniques
- Benefits of data transformation
- Summary
- Further reading
Lessons 6:
Descriptive Statistics
- Technical requirements
- Understanding statistics
- Measures of central tendency
- Measures of dispersion
- Summary
- Further reading
Lessons 7:
Grouping Datasets
- Technical requirements
- Understanding groupby()
- Groupby mechanics
- Data aggregation
- Pivot tables and cross-tabulations
- Summary
- Further reading
Lessons 8:
Correlation
- Technical requirements
- Introducing correlation
- Types of analysis
- Discussing multivariate analysis using the Titanic dataset
- Outlining Simpson’s paradox
- Correlation does not imply causation
- Summary
- Further reading
Lessons 9:
Activity: Time Series Analysis
- Technical requirements
- Understanding the time series dataset
- TSA with Open Power System Data
- Summary
- Further reading
Lessons 10:
Hypothesis Testing and Regression
- Hypothesis testing
- p-hacking
- Understanding regression
- Model development and evaluation
- Summary
- Further reading
Lessons 11:
Model Development and Evaluation
- Technical requirements
- Types of machine learning
- Understanding supervised learning
- Understanding unsupervised learning
- Understanding reinforcement learning
- Unified machine learning workflow
- Summary
- Further reading
Lessons 12:
Activity: EDA on Wine Quality Data Analysis
- Technical requirements
- Disclosing the wine quality dataset
- Analyzing red wine
- Analyzing white wine
- Model development and evaluation
- Summary
- Further reading
Appendix
- String manipulation
- Using pandas vectorized string functions
- Using regular expressions
- Further reading