Coursera

Introduction to PySpark (Coursera)

Offered by Edureka,

Welcome to Introduction to PySpark, a short course strategically crafted to empower you with the skills needed to assess the concepts of Big Data Management and efficiently perform data analysis using PySpark. Throughout this short course, you will acquire the expertise to perform data processing with PySpark, enabling you to efficiently handle large-scale datasets, conduct advanced analytics, and derive valuable insights from diverse data sources.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

During this short course, you will explore the industry-specific applications of PySpark. By the end of this course, you will be able to:

Attain a basic understanding of the introduction of big data, including its characteristics, challenges, and importance in modern data-driven environments.
Familiarize with Spark architecture and its components, such as Spark Core and Spark SQL.
Familiarize with distributed computing concepts and how they apply to Spark's parallel processing model.
Explore PySpark and big data concepts to solve data-related challenges.
Write PySpark code to solve real-world data analysis and processing tasks.

This short course is designed for Data Analysts, Data Engineers, Data Scientists, and Big Data Developers seeking to enhance their skills in utilizing PySpark for data processing and analysis.
Prior experience with Python and Hadoop is beneficial but not mandatory for this course.
Join us on this journey to enhance your PySpark skills and elevate your analytical and design capabilities.

What you'll learn
Data processing with Pyspark

Syllabus

Big Data Processing with Pyspark
Welcome to Introduction to PySpark. In this short course, you will learn the fundamental concepts of PySpark and Bigdata, and learn to perform real-time data processing with PySpark to gain useful insights from the data.

Go to Class

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Coursera

Universidad Austral

Fundamentos de Excel para Negocios (Coursera)

Statistics & Data Analysis Data Science

Cuando finalices este curso habrás logrado un gran número de habilidades como introducir información, ordenarla, manipularla, realizar cálculos de diversa índole (matemáticos, trigonométricos, estadísticos, financieros, ingenieriles, probabilísticos), extraer conclusiones, trabajar con fechas y horas, construir gráficos, imprimir reportes y muchas más.

Nov 9th 2026

5-12 Weeks

Business Excel Data Analysis

Coursera

IBM

Machine Learning Rapid Prototyping with IBM Watson Studio (Coursera)

Data Science

An emerging trend in AI is the availability of technologies in which automation is used to select a best-fit model, perform feature engineering and improve model performance via hyperparameter optimization. This automation will provide rapid-prototyping of models and allow the Data Scientist to focus their efforts on applying domain knowledge to fine-tune models. This course will take the learner through the creation of an end-to-end automated pipeline built by Watson Studio’s AutoAI experiment tool, explaining the underlying technology at work as developed by IBM Research.

Nov 9th 2026

4 Weeks

Python Artificial Intelligence Prototyping

Coursera

University of Illinois at Urbana-Champaign

Accounting Data Analytics with Python (Coursera)

Statistics & Data Analysis Data Science

This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data).

Nov 9th 2026

5-12 Weeks

Python SQL Linear Regression

Coursera

Indian Institute of Management Ahmedabad (IIMA)

Pre-MBA Statistics (Coursera)

Statistics & Data Analysis

Welcome to the Pre-MBA Statistics course! By the end of this course, you will be able to describe how statistics can be used to summarize, analyze, and interpret data. This course introduces you to some aspects of descriptive and inferential statistics. You will learn to distinguish between various data types and describe the operations that you can execute with each type of data and the right tools to use.

Nov 9th 2026

5-12 Weeks

Statistics Probability Data Analysis

Coursera

Northeastern University

Healthcare Analytics Essentials (Coursera)

Business

This course is best suited for individuals currently in the healthcare sector, as a provider, payer, or administrator. Individuals pursuing a career change to the healthcare sector may also be interested in this course.

Nov 9th 2026

4 Weeks

Statistics Healthcare Data Management

Coursera

University of Michigan

Applied Machine Learning in Python (Coursera)

Statistics & Data Analysis Data Science

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial.

Nov 9th 2026

4 Weeks

Python ML Machine Learning

Coursera

Johns Hopkins University

Advanced Reproducibility in Cancer Informatics (Coursera)

Statistics & Data Analysis Data Science

This course introduces tools that help enhance reproducibility and replicability in the context of cancer informatics. It uses hands-on exercises to demonstrate in practical terms how to get acquainted with these tools but is by no means meant to be a comprehensive dive into these tools. The course introduces tools and their concepts such as git and GitHub, code review, Docker, and GitHub actions.

Nov 9th 2026

5-12 Weeks

Github Data Analysis R Language

Coursera

Johns Hopkins University

Mathematical Biostatistics Boot Camp 1 (Coursera)

Sci: Mathematics

This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus. A small amount of linear algebra and programming are useful for the class, but not required.

Nov 9th 2026

4 Weeks

Math Statistics Probability

Coursera

École Polytechnique Fédérale de Lausanne

Big Data Analysis with Scala and Spark (Scala 2 version) (Coursera)

CS: Theory Data Science

Manipulating big data distributed over a cluster using functional concepts is rampant in industry, and is arguably one of the first widespread industrial uses of functional ideas. This is evidenced by the popularity of MapReduce and Hadoop, and most recently Apache Spark, a fast, in-memory distributed collections framework written in Scala. In this course, we'll see how the data parallel paradigm can be extended to the distributed case, using Spark throughout.

Nov 9th 2026

4 Weeks

SQL Scala Big Data

Coursera

University of Cape Town

Julia Scientific Programming (Coursera)

Statistics & Data Analysis Data Science

This four-module course introduces users to Julia as a first language. Julia is a high-level, high-performance dynamic programming language developed specifically for scientific computing. This language will be particularly useful for applications in physics, chemistry, astronomy, engineering, data science, bioinformatics and many more.

Nov 9th 2026

4 Weeks

IPython Data Analysis Computer Programming

Coursera

University of Illinois at Urbana-Champaign

Infonomics II: Business Information Management and Measurement (Coursera)

Business

Even decades into the Information Age, accounting practices yet fail to recognize the financial value of information. Moreover, traditional asset management practices fail to recognize information as an asset to be managed with earnest discipline. This has led to a business culture of complacence, and the inability for most organizations to fully leverage available information assets. This second course in the two-part Infonomics series explores how and why to adapt well-honed asset management principles and practices to information, and how to apply accepted and new valuation models to gauge information’s potential and realized economic benefits.

Nov 9th 2026

4 Weeks

Business Big Data Accounting

Coursera

École Polytechnique Fédérale de Lausanne

Big Data Analysis with Scala and Spark (Coursera)

CS: Theory CS: Programming

Nov 9th 2026

4 Weeks

Programming Algorithms SQL