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Big Data Analysis with Python

Get hands-on experience of big data analysis with Python with the comprehensive course and lab. The lab provides hands-on learning in analyzing data with the use of python, beginning up with the basics to mastering different types of data. The course and lab deal with python data science stack, statistical visualizations, working with big data frameworks, handling missing values and correlation analysis, exploratory data analysis, reproducibility in big data analysis, and many more.
Big Data Analysis with Python
Test Prep
30+ Pre Assessment Questions | 30+ Post Assessment Questions |
48+ LiveLab | 12+ Video tutorials | 20+ Minutes

Why choose TOPTALENT?


Lessons 1:

  • About

Lessons 2:
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

Lessons 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

Lessons 4:
Working with Big Data Frameworks

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

Lessons 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

Lessons 6:
Handling Missing Values and Correlation Analysis

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

Lessons 7:
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

Lessons 8:
Reproducibility in Big Data Analysis

  • Introduction
  • Reproducibility with Jupyter Notebooks
  • Gathering Data in a Reproducible Way
  • Code Practices and Standards
  • Avoiding Repetition
  • Summary

Lessons 9:
Creating a Full Analysis Report

  • Introduction
  • Reading Data in Spark from Different Data Sources
  • SQL Operations on a Spark DataFrame
  • Generating Statistical Measurements
  • Summary