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All in all PySpark is based on Apache Spark, which is one of the most popular big data processing frameworks. Pyspark allows developers to process large data sets in parallel, making it an efficient tool for big data processing. needed to work with large datasets.
What is PySpark?
Overall PySpark is the Python API for Apache Spark. It enables you to perform real-time, large-scale data processing in a distributed environment using Python. It also provides a PySpark shell for interactively analyzing your data.
Fast forward your career in the IT industry with a part-time course at School of IT. In turn Part-time courses allow working professionals to transition into a new skill set while working. Moreover at School of IT we are agile and customize a course to the individual.
Ready to start a career in IT? Use the PySpark SQL as a full time student at School of IT. Thus beginning your career in Data Analytics.
Learn about the the python api. Thus prepare for the future while you’re still in high school. Thus no matter where you are, we come to you! Thus giving you the analytical skills to pursue your dreams!
Learn about pyspark api and up skill yourself or your company while you’re working. Thus no matter where you are, we come to you and give the tools to move up in your company.
By the end of the PySpark Courses, students will have usable knowledge of the following:
Understanding Big Data
Overview of Spark
Overview of Python
Overview of PySpark
- Distributing Data Using Resilient Distributed Datasets Framework
- Distributing Computation Using Spark API Operators
Setting Up Python with Spark
Setting Up PySpark
Using Amazon Web Services (AWS) EC2 Instances for Spark
Setting Up Databricks
Setting Up the AWS EMR Cluster
Learning the Basics of Python Programming
- Getting Started with Python
- Using the Jupyter Notebook
- Using Variables and Simple Data Types
- Working with Lists
- Using if Statements
- Using User Inputs
- Working with while Loops
- Implementing Functions
- Working with Classes
- Working with Files and Exceptions
- Working with Projects, Data, and APIs
Learning the Basics of Spark DataFrame
- Getting Started with Spark DataFrames
- Implementing Basic Operations with Spark
- Using Groupby and Aggregate Operations
- Working with Timestamps and Dates
Working on a Spark DataFrame Project Exercise
Understanding Machine Learning with MLlib
Working with MLlib, Spark, and Python for Machine Learning
- Learning Linear Regression Theory
- Implementing a Regression Evaluation Code
- Working on a Sample Linear Regression Exercise
- Learning Logistic Regression Theory
- Implementing a Logistic Regression Code
- Working on a Sample Logistic Regression Exercise
Understanding Random Forests and Decision Trees
- Learning Tree Methods Theory
- Implementing Decision Trees and Random Forest Codes
- Working on a Sample Random Forest Classification Exercise
Working with K-means Clustering
- Understanding K-means Clustering Theory
- Implementing a K-means Clustering Code
- Working on a Sample Clustering Exercise
Working with Recommender Systems
Implementing Natural Language Processing
- Understanding Natural Language Processing (NLP)
- Overview of NLP Tools
- Working on a Sample NLP Exercise
Streaming with Spark on Python
- Overview Streaming with Spark
- Sample Spark Streaming Exercise
- General programming skills
- IT Professionals
- Data Scientists
The career prospects for PySpark graduates is high in demand. The cloud environment and analyzing data is in huge demand: on all platforms and devices and in all countries around the world!
- Junior Python Developer
- API Developer.
- Web Developer.
- Furthermore a junior developer.
- Become a Data Analyst.
- Cloud Software Engineer.
- Data Scientist.