Big Data Analysis with Python
Processing big data in real time is challenging due to scalability, information inconsistency, and fault tolerance. Big Data Analysis with Python teaches you how to use tools that can control this data avalanche for you. With this course, you'll learn effective techniques to aggregate data into useful dimensions for posterior analysis, extract statistical measurements, and transform datasets into features for other systems.
The course begins with an introduction to data manipulation in Python using Pandas. You'll then get familiar with statistical analysis and plotting techniques. With multiple hands-on activities in store, you'll be able to analyze data that is distributed on several computers by using Dask. As you progress, you'll study how to aggregate data for plots when the entire data cannot be accommodated into memory. You'll also explore Hadoop (HDFS and YARN), which will help you tackle larger datasets. The course further covers Spark and its interaction with other tools.
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
- Any of the following operating systems:
- Windows 7 SP1 32/64-bit
- Windows 8.1 32/64-bit or Windows 10 32/64-bit
- Ubuntu 14.04 or later
- macOS Sierra or later
- Browser: Google Chrome or Mozilla Firefox
- Conda
- Jupyter lab
For the optimal student experience, we recommend the following hardware configuration:
- Processor: Intel or AMD 4-core or better
- Memory: 8 GB RAM
- Storage: 20 GB available space
Lesson 1: The Python Data Science Stack
Python Libraries and Packages
Using Pandas
Data Type Conversion
Aggregation and Grouping
Exporting Data from Pandas
Visualization with Pandas
Lesson 2: Statistical Visualizations
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
Lesson 3: Working with Big Data Frameworks
Hadoop
Spark
Writing Parquet Files
Handling Unstructured Data
Lesson 4: Diving Deeper with Spark
Getting Started with Spark DataFrames
Writing Output from Spark DataFrames
Exploring Spark DataFrames
Data Manipulation with Spark DataFrames
Graphs in Spark
Lesson 5: Handling Missing Values and Correlation
Analysis
Setting up the Jupyter Notebook
Missing Values
Handling Missing Values in Spark DataFrames
Correlation
Lesson 6: Exploratory Data Analysis
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
Lesson 7: Reproducibility in Big Data Analysis
Reproducibility with Jupyter Notebooks
Gathering Data in a Reproducible Way
Code Practices and Standards
Avoiding Repetition
Lesson 8: Creating a Full Analysis Report
Reading Data in Spark from Different Data Sources
SQL Operations on a Spark DataFrame
Generating Statistical Measurements