Python Libraries and Packages
Using Pandas
Data Type Conversion
Aggregation and Grouping
Exporting Data from Pandas
Visualization with Pandas
Types of Graphs and When to Use Them
Components of a Graph
Which Tool Should Be Used?
Types of Graphs
Pandas DataFrames and Grouped Data
Changing Plot Design: Modifying Graph Components
Exporting Graphs
Hadoop
Spark
Writing Parquet Files
Handling Unstructured Data
Getting Started with Spark DataFrames
Writing Output from Spark DataFrames
Exploring Spark DataFrames
Data Manipulation with Spark DataFrames
Graphs in Spark
Setting up the Jupyter Notebook
Missing Values
Handling Missing Values in Spark DataFrames
Correlation
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
Reproducibility with Jupyter Notebooks
Gathering Data in a Reproducible Way
Code Practices and Standards
Avoiding Repetition
Reading Data in Spark from Different Data Sources
SQL Operations on a Spark DataFrame
Generating Statistical Measurements

Learner Outcomes
By the end of this course, you'll be able to bootstrap your own Python environment, process large files, and manipulate data to generate statistics, metrics, and graphs.
Learning Objectives:
Use Python to read and transform data into different formats
Generate basic statistics and metrics using data on the disk
Work with computing tasks distributed over a cluster
Convert data from various sources into storage or querying formats
Prepare data for statistical analysis, visualization, and machine learning
Present data in the form of effective visuals

There are no prerequisites for this course.