Big Data Analysis with Python

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Get to grips with processing large volumes of data and presenting it as engaging, interactive insights using Spark and 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.

This course is designed for Python developers, data analysts, and data scientists. This course is not for beginners.

Big Data Analysis with Python is designed for Python developers, data analysts, and data scientists who want to get hands-on with methods to control data and transform it into impactful insights. Basic knowledge of statistical measurements and relational databases will help in understanding various concepts explained in this course.

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

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

Thời lượng: 2 Ngày

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English

Big Data Analysis with Python

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