Premium Features

Previous Buy now to get complete version Next
  • Home
uCertify Logo
  • login
  • Signup
    • Help & Support
    • Accessibility
    • Testimonials
  • Powered by uCertify
  • Request Demo
  • Hello GuestLogin or Signup
  • Feedback & Support
    • Support
    • Keyboard Shortcuts
    • Send Feedback
Scroll to top button

Exploratory Data Analysis with Python

(EDA-PYTHON.AJ1) / ISBN: 978-1-64459-298-4
This course includes
Lessons
TestPrep
LiveLab
Mentoring (Add-on)
EDA-PYTHON.AJ1 : Exploratory Data Analysis with Python
Try this course Pre-Assessment and first two Lessons free No credit card required
Are you an instructor? Teach using uCertify products
Request a free evaluation copy

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
Lesson 1: Preface
  • Who this course is for?
  • What this course covers?
  • To get the most out of this course
  • Conventions used
Lesson 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
Lesson 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
Lesson 4: Activity: EDA with Personal Email
  • Technical requirements
  • Loading the dataset
  • Data transformation
  • Data analysis
  • Summary
  • Further reading
Lesson 5: Data Transformation
  • Technical requirements
  • Background
  • Merging database-style dataframes
  • Transformation techniques
  • Benefits of data transformation
  • Summary
  • Further reading
Lesson 6: Descriptive Statistics
  • Technical requirements
  • Understanding statistics
  • Measures of central tendency
  • Measures of dispersion
  • Summary
  • Further reading
Lesson 7: Grouping Datasets
  • Technical requirements
  • Understanding groupby()
  • Groupby mechanics
  • Data aggregation
  • Pivot tables and cross-tabulations
  • Summary
  • Further reading
Lesson 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
Lesson 9: Activity: Time Series Analysis
  • Technical requirements
  • Understanding the time series dataset
  • TSA with Open Power System Data
  • Summary
  • Further reading
Lesson 10: Hypothesis Testing and Regression
  • Hypothesis testing
  • p-hacking
  • Understanding regression
  • Model development and evaluation
  • Summary
  • Further reading
Lesson 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
Lesson 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

Hands on Activities (Live Labs)

Exploratory Data Analysis Fundamentals

  • 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

Visual Aids for EDA

  • 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

Activity: EDA with Personal Email

  • Performing EDA with Email Data
  • Extracting Email Using Regex
  • Converting a Field to datetime
  • Removing NaN Values
  • Dropping a Column

Data Transformation

  • 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

Descriptive Statistics

  • 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

Grouping Datasets

  • Finding Maximum Value for Each Group
  • Grouping a Dataset
  • Filtering Data
  • Applying Aggregation Functions
  • Creating a Pivot Table
  • Creating a Cross-Tabulation Table

Correlation

  • Calculating Correlation Coefficient

Activity: Time Series Analysis

  • Sampling the Data
  • Resampling the Data
  • Changing the Index of a Dataframe

Hypothesis Testing and Regression

  • Performing Z-Test
  • Calculating the P-Value
  • Performing T-test
  • Scoring the Model
  • Understanding the Linear Regression Model

Model Development and Evaluation

  • Using TfidfVectorizer

Activity: EDA on Wine Quality Data Analysis

  • Plotting a Heatmap
  • Visualizing the Data in 3D Form

Appendix

  • 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
×
uc logo for app downloadDownload our uCertify App [lms_setting_placeholder: This filed is used to set the LMS settings.

Share with your friends and colleagues

We use cookies to enhance your experience. By continuing to visit this site you agree to our use of cookies. More information
Accept