Get hands-on experience of Exploratory Data Analysis with Python with the comprehensive course and lab. The lab provides hands-on learning of EDA (Exploratory Data Analysis), beginning up with the basics to gain insights along with diverse techniques like data cleaning, data preparation, data exploration, and data visualization. The course and lab deal with importing, cleaning, and exploring data to perform preliminary analysis using powerful Python packages, and many more. Using Python for data analysis, you’ll work with real-world datasets, understand data, summarize its characteristics, and visualize it for business intelligence.

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* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

The course and lab deal with importing, cleaning, and exploring data to perform preliminary analysis using powerful Python packages.

1

  • Preface

  • Who this course is for?
    What this course covers?
    To get the most out of this course
    Conventions used
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
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
4

  • Activity: EDA with Personal Email

  • Technical requirements
    Loading the dataset
    Data transformation
    Data analysis
    Summary
    Further reading
5

  • Data Transformation

  • Technical requirements
    Background
    Merging database-style dataframes
    Transformation techniques
    Benefits of data transformation
    Summary
    Further reading
6

  • Descriptive Statistics

  • Technical requirements
    Understanding statistics
    Measures of central tendency
    Measures of dispersion
    Summary
    Further reading
7

  • Grouping Datasets

  • Technical requirements
    Understanding groupby()
    Groupby mechanics
    Data aggregation
    Pivot tables and cross-tabulations
    Summary
    Further reading
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
9

  • Activity: Time Series Analysis

  • Technical requirements
    Understanding the time series dataset
    TSA with Open Power System Data
    Summary
    Further reading
10

  • Hypothesis Testing and Regression

  • Hypothesis testing
    p-hacking
    Understanding regression
    Model development and evaluation
    Summary
    Further reading
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
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
13

  • Appendix

  • String manipulation
    Using pandas vectorized string functions
    Using regular expressions
    Further reading

Audience

Those wanting to use Python to improve data analysis will benefit from this course.

Language

English

Prerequisites

While there are no prerequisites for this course, please ensure you have the right level of experience to be successful in this training.

Length: 365.0 days ( hours)

Level:

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