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. In this course, you’ll learn practical 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 dataset cannot be accommodated into memory. You’ll also explore Hadoop (HDFS and YARN), which will help you tackle larger datasets. The course also 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.
What you will Learn
- 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
Who should attend
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.