Exploratory Data Analysis with Python

(EDA-PYTHON.AJ1) / ISBN: 978-1-64459-298-4
This course includes
Lessons
TestPrep
LiveLab
Mentoring (Add-on)
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Exploratory Data Analysis with Python

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.
Here's what you will get

Lessons
  • 13+ Lessons
  • 47+ Exercises
  • 63+ Quizzes
  • 80+ Flashcards
  • 80+ Glossary of terms
TestPrep
  • 35+ Pre Assessment Questions
  • 35+ Post Assessment Questions
LiveLab
  • 77+ LiveLab
  • 13+ Video tutorials
  • 20+ Minutes
Here's what you will learn
Download Course Outline
  • Who this course is for?
  • What this course covers?
  • To get the most out of this course
  • Conventions used
  • 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
  • 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
  • Technical requirements
  • Loading the dataset
  • Data transformation
  • Data analysis
  • Summary
  • Further reading
  • Technical requirements
  • Background
  • Merging database-style dataframes
  • Transformation techniques
  • Benefits of data transformation
  • Summary
  • Further reading
  • Technical requirements
  • Understanding statistics
  • Measures of central tendency
  • Measures of dispersion
  • Summary
  • Further reading
  • Technical requirements
  • Understanding groupby()
  • Groupby mechanics
  • Data aggregation
  • Pivot tables and cross-tabulations
  • Summary
  • Further reading
  • 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
  • Technical requirements
  • Understanding the time series dataset
  • TSA with Open Power System Data
  • Summary
  • Further reading
  • Hypothesis testing
  • p-hacking
  • Understanding regression
  • Model development and evaluation
  • Summary
  • Further reading
  • Technical requirements
  • Types of machine learning
  • Understanding supervised learning
  • Understanding unsupervised learning
  • Understanding reinforcement learning
  • Unified machine learning workflow
  • Summary
  • Further reading
  • Technical requirements
  • Disclosing the wine quality dataset
  • Analyzing red wine
  • Analyzing white wine
  • Model development and evaluation
  • Summary
  • Further reading
  • String manipulation
  • Using pandas vectorized string functions
  • Using regular expressions
  • Further reading

Hands on Activities (Live Labs)

  • Styling a Dataframe
  • Applying Function to a Dataframe
  • Slicing and Subsetting
  • Dividing NumPy Arrays
  • Inspecting NumPy Arrays
  • Defining NumPy arrays
  • Selecting rows
  • Reading Data from a CSV File
  • Creating a Dataframe
  • Creating a Line chart
  • Creating a Bar Chart
  • Creating a Scatter Plot
  • Creating a Bubble Chart
  • Creating an Area Plot
  • Creating a Pie Chart
  • Creating a Table Chart
  • Creating a Polar Chart
  • Adding the Best-Fit Line for the Normal Distribution
  • Creating a Histogram
  • Creating a Lollipop Chart
  • Performing EDA with Email Data
  • Extracting Email Using Regex
  • Converting a Field to datetime
  • Removing NaN Values
  • Dropping a Column
  • Stacking a Dataframe
  • Concatenating Dataframes
  • Analyzing Dataframes
  • Combining Dataframes
  • Merging on Index
  • Permuting a Dataframe
  • Removing Duplicate Data
  • Replacing Values
  • Interpolating Missing Values
  • Backward and Forward Filling
  • Handling NaN values
  • Counting Missing Values
  • Renaming Axis Indexes
  • Binning
  • Detecting Outliers
  • Generating a Binomial Distribution Plot
  • Generating an Exponential Distribution Plot
  • Generating a Normal Distribution Plot
  • Generating a Uniform Distribution Plot
  • Using Statistical Functions
  • Calculating Standard Deviation
  • Finding Skewness and Kurtosis
  • Creating a Box Plot
  • Calculating Inter-Quartile Range
  • Finding Maximum Value for Each Group
  • Grouping a Dataset
  • Filtering Data
  • Applying Aggregation Functions
  • Creating a Pivot Table
  • Creating a Cross-Tabulation Table
  • Calculating Correlation Coefficient
  • Sampling the Data
  • Resampling the Data
  • Changing the Index of a Dataframe
  • Performing Z-Test
  • Calculating the P-Value
  • Performing T-test
  • Scoring the Model
  • Understanding the Linear Regression Model
  • Using TfidfVectorizer
  • Plotting a Heatmap
  • Visualizing the Data in 3D Form
  • Accessing Characters
  • String Slicing
  • Updating a String
  • Escape Sequencing
  • Formatting Strings
  • Displaying Last 10 items from a Dataframe
  • Using String Functions with a Dataframe
  • Finding Words from a String
  • Counting Full Stops using Regex
  • Matching Characters
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