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SNHU-DAT223.AJ1
Exploratory Data Analysis with Python
Intermediate Self-paced · 1 year access
78 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
01 / Lessons & labs
See exactly what you will learn and practice
Lessons
13 Interactive Lessons · 90 topics01 Preface 4 topics +
- Who this course is for?
- What this course covers?
- To get the most out of this course
- Conventions used
02 Exploratory Data Analysis Fundamentals 8 topics · 10 LiveLab +
- 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
10 LiveLab in this lesson — see the labs panel →
03 Visual Aids for EDA 14 topics · 11 LiveLab +
- 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
11 LiveLab in this lesson — see the labs panel →
04 Activity: EDA with Personal Email 6 topics · 5 LiveLab +
- Technical requirements
- Loading the dataset
- Data transformation
- Data analysis
- Summary
- Further reading
5 LiveLab in this lesson — see the labs panel →
05 Data Transformation 7 topics · 15 LiveLab +
- Technical requirements
- Background
- Merging database-style dataframes
- Transformation techniques
- Benefits of data transformation
- Summary
- Further reading
15 LiveLab in this lesson — see the labs panel →
06 Descriptive Statistics 6 topics · 9 LiveLab +
- Technical requirements
- Understanding statistics
- Measures of central tendency
- Measures of dispersion
- Summary
- Further reading
9 LiveLab in this lesson — see the labs panel →
07 Grouping Datasets 7 topics · 6 LiveLab +
- Technical requirements
- Understanding groupby()
- Groupby mechanics
- Data aggregation
- Pivot tables and cross-tabulations
- Summary
- Further reading
6 LiveLab in this lesson — see the labs panel →
08 Correlation 8 topics · 1 LiveLab +
- 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
1 LiveLab in this lesson — see the labs panel →
09 Activity: Time Series Analysis 5 topics · 3 LiveLab +
- Technical requirements
- Understanding the time series dataset
- TSA with Open Power System Data
- Summary
- Further reading
3 LiveLab in this lesson — see the labs panel →
10 Hypothesis Testing and Regression 6 topics · 5 LiveLab +
- Hypothesis testing
- p-hacking
- Understanding regression
- Model development and evaluation
- Summary
- Further reading
5 LiveLab in this lesson — see the labs panel →
11 Model Development and Evaluation 8 topics · 1 LiveLab +
- Technical requirements
- Types of machine learning
- Understanding supervised learning
- Understanding unsupervised learning
- Understanding reinforcement learning
- Unified machine learning workflow
- Summary
- Further reading
1 LiveLab in this lesson — see the labs panel →
12 Activity: EDA on Wine Quality Data Analysis 7 topics · 2 LiveLab +
- Technical requirements
- Disclosing the wine quality dataset
- Analyzing red wine
- Analyzing white wine
- Model development and evaluation
- Summary
- Further reading
2 LiveLab in this lesson — see the labs panel →
13 Appendix 4 topics · 10 LiveLab +
- String manipulation
- Using pandas vectorized string functions
- Using regular expressions
- Further reading
10 LiveLab in this lesson — see the labs panel →
Hands-On Labs Our edge
78 LiveLabs- Sandbox
- 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
Labs run in your browser — nothing to install.
02 / FAQs
Questions before you start
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