UOP-DSC350.AJ1
Exploratory Data Analysis
- Practice in 63 Hands-On Labs — nothing to install
- 6 Interactive Lessons and 57 topics mapped to the official exam objectives
- 217 Practice Test Questions and 2 Full Length Tests
Intermediate Self-paced · 1 year access
63 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
6Interactive Lessons
57Topics
63LiveLab
217Practice Test Questions
80Flashcards
80Glossary of terms
01 / Lessons & labs
See exactly what you will learn and practice
Lessons
6 Interactive Lessons · 57 topics01 Exploratory Data Analysis Fundamentals 6 topics · 9 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
9 LiveLab in this lesson — see the labs panel →
02 Visual Aids for EDA 13 topics · 12 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
- EDA with Personal Email
12 LiveLab in this lesson — see the labs panel →
03 Data Transformation 5 topics · 15 LiveLab +
- Technical requirements
- Background
- Merging database-style dataframes
- Transformation techniques
- Benefits of data transformation
15 LiveLab in this lesson — see the labs panel →
04 Statistical Tools and Techniques for Investigating Data 15 topics · 19 LiveLab +
- 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
19 LiveLab in this lesson — see the labs panel →
05 Model Development and Evaluation 14 topics · 8 LiveLab +
- 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
8 LiveLab in this lesson — see the labs panel →
06 Appendix 4 topics +
- String manipulation
- Using pandas vectorized string functions
- Using regular expressions
- Further reading
Hands-On Labs Our edge
63 LiveLabs- 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
- 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
Labs run in your browser — nothing to install.
02 / FAQs
Questions before you start
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- 1 year of full access
- 63 LiveLab included
- Certificate of completion
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