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DAT260 - Emerging Technologies and Big Data

(SNHU-DAT260.AJ1)
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Skills You’ll Get

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1

The Python Data Science Stack

  • Introduction
  • Python Libraries and Packages
  • Using Pandas
  • Data Type Conversion
  • Aggregation and Grouping
  • Exporting Data from Pandas
  • Visualization with Pandas
  • Summary
2

Exploratory Data Analysis

  • 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
3

Statistical Visualizations

  • 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
4

Working with Big Data Frameworks

  • Introduction
  • Hadoop
  • Spark
  • Writing Parquet Files
  • Handling Unstructured Data
  • Summary
5

Diving Deeper with Spark

  • Introduction
  • Getting Started with Spark DataFrames
  • Writing Output from Spark DataFrames
  • Exploring Spark DataFrames
  • Data Manipulation with Spark DataFrames
  • Graphs in Spark
  • Summary
6

Handling Missing Values and Correlation Analysis

  • Introduction
  • Setting up the Jupyter Notebook
  • Missing Values
  • Handling Missing Values in Spark DataFrames
  • Correlation
  • Summary
7

Reproducibility in Big Data Analysis

1

The Python Data Science Stack

  • 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
2

Exploratory Data Analysis

  • Generating the Feature Importance of the Target Variable
  • Identifying the Target Variable
  • Plotting a Heatmap
  • Generating a Normal Distribution Plot
3

Statistical Visualizations

  • Plotting an Analytical Graph
  • Creating a Graph
  • Creating a Line Graph Using Seaborn
  • Detecting Outliers
  • Displaying Histograms
  • Using a Box Plot
  • Constructing a Scatterplot
4

Diving Deeper with Spark

  • 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
5

Handling Missing Values and Correlation Analysis

  • Filtering Data
  • Counting Missing Values
  • Handling NaN Values
  • Using the Backward and Forward Filling Methods
  • Calculating Correlation Coefficient
6

Reproducibility in Big Data Analysis

  • Performing Data Reproducibility
  • Preprocessing Missing Values with High Reproducibility
  • Normalizating the Data

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