Exploratory Data Analysis with Python
- Price: $279.99
- Delivery method: eLearning
- DIR Discount: 20%
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Test Prep
35+ Pre Assessment Questions |
35+ Post Assessment Questions |
Features
77+ LiveLab |
13+ Video tutorials |
20+ Minutes
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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
