Big Data Analysis With Python

Big Data Analysis With Python

This course is designed for Python Developers, Data Analysts, and Data Scientists

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

Duration: 02 days

Target Audience

Python Developer

Data Analysis

Data Scientist

What will you learn?

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

  Big Data Analysis With Python Course Outlines:

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

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