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SNHU-DAT260.AJ1
DAT260 - Emerging Technologies and Big Data
Intermediate Self-paced · 1 year access
33 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
7 Interactive Lessons · 42 topics01 The Python Data Science Stack 8 topics · 7 LiveLab +
- Introduction
- Python Libraries and Packages
- Using Pandas
- Data Type Conversion
- Aggregation and Grouping
- Exporting Data from Pandas
- Visualization with Pandas
- Summary
7 LiveLab in this lesson — see the labs panel →
02 Exploratory Data Analysis 5 topics · 4 LiveLab +
- Introduction
- Defining a Business Problem
- Translating a Business Problem into Measurable Metrics and Exploratory Data Analysis (EDA)
- Structured Approach to the Data Science Project Life Cycle
- Summary
4 LiveLab in this lesson — see the labs panel →
03 Statistical Visualizations 10 topics · 7 LiveLab +
- Introduction
- Types of Graphs and When to Use Them
- Components of a Graph
- Seaborn
- Which Tool Should Be Used?
- Types of Graphs
- Pandas DataFrames and Grouped Data
- Changing Plot Design: Modifying Graph Components
- Exporting Graphs
- Summary
7 LiveLab in this lesson — see the labs panel →
04 Working with Big Data Frameworks 6 topics +
- Introduction
- Hadoop
- Spark
- Writing Parquet Files
- Handling Unstructured Data
- Summary
05 Diving Deeper with Spark 7 topics · 7 LiveLab +
- Introduction
- Getting Started with Spark DataFrames
- Writing Output from Spark DataFrames
- Exploring Spark DataFrames
- Data Manipulation with Spark DataFrames
- Graphs in Spark
- Summary
7 LiveLab in this lesson — see the labs panel →
06 Handling Missing Values and Correlation Analysis 6 topics · 5 LiveLab +
- Introduction
- Setting up the Jupyter Notebook
- Missing Values
- Handling Missing Values in Spark DataFrames
- Correlation
- Summary
5 LiveLab in this lesson — see the labs panel →
07 Reproducibility in Big Data Analysis 3 LiveLab +
3 LiveLab in this lesson — see the labs panel →
Hands-On Labs Our edge
33 LiveLabs- Interacting with the Python Shell
- Grouping a DataFrame
- Applying a Function to a Column
- Subsetting a DataFrame
- Reading Data from a CSV File
- Viewing the Standard Deviation
- Calculating the Mean Value
- Generating the Feature Importance of the Target Variable
- Identifying the Target Variable
- Plotting a Heatmap
- Generating a Normal Distribution Plot
- Plotting an Analytical Graph
- Creating a Graph
- Creating a Line Graph Using Seaborn
- Detecting Outliers
- Displaying Histograms
- Using a Box Plot
- Constructing a Scatterplot
- Creating a DataFrame Using a CSV File
- Specifying the Schema of a DataFrame
- Removing a Column from a DataFrame
- Renaming a Column in a DataFrame
- Adding a Column to a DataFrame
- Creating a KDE Plot
- Creating a Bar Chart
- Filtering Data
- Counting Missing Values
- Handling NaN Values
- Using the Backward and Forward Filling Methods
- Calculating Correlation Coefficient
- Performing Data Reproducibility
- Preprocessing Missing Values with High Reproducibility
- Normalizating the Data
Labs run in your browser — nothing to install.
02 / FAQs
Questions before you start
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- 1 year of full access
- 33 LiveLab included
- Certificate of completion
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