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Exploratory Data Analysis

(UOP-DSC350.AJ1)
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Skills You’ll Get

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1

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
2

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
  • EDA with Personal Email
3

Data Transformation

  • Technical requirements
  • Background
  • Merging database-style dataframes
  • Transformation techniques
  • Benefits of data transformation
4

Statistical Tools and Techniques for Investigating Data

  • Understanding statistics
  • Measures of central tendency
  • Measures of dispersion
  • Understanding groupby()
  • Groupby mechanics
  • Data aggregation
  • Pivot tables and cross-tabulations
  • Introducing correlation
  • Types of analysis
  • Discussing multivariate analysis using the Titanic dataset
  • Outlining Simpson's paradox
  • Correlation does not imply causation
  • Activity: Time Series Analysis
  • Understanding the time series dataset
  • TSA with Open Power System Data
5

Model Development and Evaluation

  • Hypothesis testing
  • p-hacking
  • Understanding regression
  • Model development and evaluation
  • Types of machine learning
  • Understanding supervised learning
  • Understanding unsupervised learning
  • Understanding reinforcement learning
  • Unified machine learning workflow
  • Activity: EDA on Wine Quality Data Analysis
  • Disclosing the wine quality dataset
  • Analyzing red wine
  • Analyzing white wine
  • Model development and evaluation
A

Appendix

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

1

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
2

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
  • Performing EDA with Email Data
3

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
4

Statistical Tools and Techniques for Investigating Data

  • 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
5

Model Development and Evaluation

  • 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

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