Cleaning and Exploring Big Data using PySpark from Coursera

By the end of this project, you will learn how to clean, explore and visualize big data using PySpark. You will be using an open source dataset containing information on all the water wells in Tanzania. I will teach you various ways to clean and explore your big data in PySpark such as changing column’s data type, renaming categories with low frequency in character columns and imputing missing values in numerical columns. I will also teach you ways to visualize your data by intelligently converting Spark dataframe to Pandas dataframe. Cleaning and exploring big data in PySpark is quite different from Python due to the distributed nature of Spark dataframes. This guided project will dive deep into various ways to clean and explore your data loaded in PySpark. Data preprocessing in big data analysis is a crucial step and one should learn about it before building any big data machine learning model. Note: You should have a Gmail account which you will use to sign into Google Colab. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

This course focuses on a practical approach to cleaning and exploring big data using PySpark, making it particularly relevant for practicing data scientists

Emphasizes the importance of data preprocessing in big data analysis, providing valuable knowledge for building effective machine learning models

The use of real-world data from Tanzania's water wells offers a meaningful context for the data cleaning and exploration tasks

Introduces techniques for handling different data types, such as categorical and numerical columns, which is crucial for effective data preprocessing

Teaches methods for converting Spark dataframes to Pandas dataframes, facilitating data visualization and further analysis

Prerequisites include a Gmail account and access to Google Colab

Reviews summary

Practical pyspark tutorial

Reviews indicate that this course provides a practical introduction to PySpark. It effectively demonstrates essential functions and methods, giving learners a solid foundation for using PySpark for data analysis. However, some reviewers have expressed that they would appreciate more in-depth explanations of the concepts and functions used, as well as a better explanation of the use case before diving into the code.

Easy-to-follow instructions

"fast and simple explanation about ow to start to work with Spak on Colab"

Hands-on, practical walk-through

"Practical walk through of basic PySpark operations"

Code may be outdated

"This project is outdated and I can't get the first line to run in google colab"

Lack of in-depth explanations

"For many codes, the teacher just wrote the codes, without any explanations..."

"use case could be explained a little better, before actually going to the code"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Cleaning and Exploring Big Data using PySpark with these activities:

Read 'Learning Spark' by Holden Karau, Andy Konwinski, Patrick Wendell, and Matei Zaharia

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This book provides a comprehensive introduction to Spark, covering everything from basic concepts to advanced techniques. It's a valuable resource for anyone who wants to learn more about Spark.

View Learning Spark: Lightning-Fast Big Data Analysis on Amazon

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Read the book from cover to cover.
Take notes and highlight important passages.
Review the book regularly to reinforce your understanding.

Follow tutorials on Spark data processing

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There are many helpful tutorials available online that can provide you with step-by-step guidance on how to perform various data processing tasks using Spark.

Browse courses on Data Processing

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Find a tutorial that covers a topic you're interested in.
Follow the instructions in the tutorial.
Experiment with the code and try to apply it to your own data.
Share your experience with others.

Join a study group

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Working together to solve problems, complete homework assignments and prepare for exams can improve retention. Find other learners in this course and ask if you can join their study group, or start your own and invite others to join.

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Locate other students taking the same course.
Ask to join a group or start your own.
Set a regular meeting schedule and meeting format.
Work in a collaborative environment to help each other.

Three other activities

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Solve Spark data processing challenges

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Solving challenges is a great way to practice applying the concepts and techniques you've learned in this course. There are many online resources and platforms that provide Spark data processing challenges.

Browse courses on Data Processing

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Find a challenge that matches your skill level.
Read the problem statement carefully.
Design and implement a solution using Spark.
Test and evaluate your solution.
Share your solution with others.

Build a Spark data visualization dashboard

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Creating your own dashboard requires a deep understanding of the concepts and techniques taught in this course. You'll get to practice visualizing, summarizing, and presenting data in a meaningful way.

Browse courses on Data Visualization

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Identify the data you want to visualize.
Choose the appropriate visualization types.
Use Apache Spark to process and transform the data.
Create a dashboard using a tool like Apache Superset or Tableau.
Publish your dashboard and share it with others.

Contribute to an open-source Spark project

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Contributing to an open-source Spark project is a great way to gain experience and give back to the community. You'll get to work on real-world problems and collaborate with other developers.

Browse courses on Open Source

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Find an open-source Spark project that you're interested in.
Read the project documentation and get familiar with the codebase.
Identify an area where you can contribute.
Create a pull request with your changes.
Collaborate with other developers to get your changes merged.

Career center

Learners who complete Cleaning and Exploring Big Data using PySpark will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data analysts are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. This course can help data analysts develop the skills they need to clean and explore big data using PySpark, which is a powerful tool for working with large datasets. By learning how to clean and explore big data, data analysts can gain insights that can help them make better decisions.

See salaries and explore the career path for Data Analyst

Data Scientist

Data scientists use data to build models that can predict future outcomes. This course can help data scientists develop the skills they need to clean and explore big data using PySpark, which is a powerful tool for working with large datasets. By learning how to clean and explore big data, data scientists can build better models that can make more accurate predictions.

See salaries and explore the career path for Data Scientist

Data Engineer

Data engineers are responsible for designing and building the infrastructure that supports data analysis. This course can help data engineers develop the skills they need to clean and explore big data using PySpark, which is a powerful tool for working with large datasets. By learning how to clean and explore big data, data engineers can build better infrastructure that can support more efficient and effective data analysis.

See salaries and explore the career path for Data Engineer

Machine Learning Engineer

Machine learning engineers build and deploy machine learning models. This course can help machine learning engineers develop the skills they need to clean and explore big data using PySpark, which is a powerful tool for working with large datasets. By learning how to clean and explore big data, machine learning engineers can build better models that can make more accurate predictions.

See salaries and explore the career path for Machine Learning Engineer

Statistician

Statisticians use data to make inferences about populations. This course can help statisticians develop the skills they need to clean and explore big data using PySpark, which is a powerful tool for working with large datasets. By learning how to clean and explore big data, statisticians can make more accurate inferences about populations.

See salaries and explore the career path for Statistician

Business Analyst

Business analysts use data to solve business problems. This course can help business analysts develop the skills they need to clean and explore big data using PySpark, which is a powerful tool for working with large datasets. By learning how to clean and explore big data, business analysts can gain insights that can help them make better decisions.

See salaries and explore the career path for Business Analyst

Database Administrator

Database administrators maintain and manage databases. This course can help database administrators develop the skills they need to clean and explore big data using PySpark, which is a powerful tool for working with large datasets. By learning how to clean and explore big data, database administrators can better maintain and manage databases.

See salaries and explore the career path for Database Administrator

Data Architect

Data architects design and build data warehouses and data lakes. This course can help data architects develop the skills they need to clean and explore big data using PySpark, which is a powerful tool for working with large datasets. By learning how to clean and explore big data, data architects can build better data warehouses and data lakes that can support more efficient and effective data analysis.

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Software Engineer

Software engineers design and build software applications. This course can help software engineers develop the skills they need to clean and explore big data using PySpark, which is a powerful tool for working with large datasets. By learning how to clean and explore big data, software engineers can build better applications that can handle large amounts of data.

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Web Developer

Web developers design and build websites. This course can help web developers develop the skills they need to clean and explore big data using PySpark, which is a powerful tool for working with large datasets. By learning how to clean and explore big data, web developers can build better websites that can handle large amounts of traffic.

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Financial Analyst

Financial analysts provide financial advice to individuals and organizations. This course can help financial analysts develop the skills they need to clean and explore big data using PySpark, which is a powerful tool for working with large datasets. By learning how to clean and explore big data, financial analysts can better analyze financial data and make better recommendations.

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Risk Analyst

Risk analysts assess and manage risks. This course can help risk analysts develop the skills they need to clean and explore big data using PySpark, which is a powerful tool for working with large datasets. By learning how to clean and explore big data, risk analysts can better assess and manage risks.

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Information Security Analyst

Information security analysts protect computer systems and networks from unauthorized access. This course can help information security analysts develop the skills they need to clean and explore big data using PySpark, which is a powerful tool for working with large datasets. By learning how to clean and explore big data, information security analysts can better identify and protect against security threats.

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Auditor

Auditors examine financial records to ensure accuracy and compliance. This course can help auditors develop the skills they need to clean and explore big data using PySpark, which is a powerful tool for working with large datasets. By learning how to clean and explore big data, auditors can better examine financial records and identify any discrepancies.

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Market Researcher

Market researchers conduct research to understand consumer behavior. This course can help market researchers develop the skills they need to clean and explore big data using PySpark, which is a powerful tool for working with large datasets. By learning how to clean and explore big data, market researchers can better understand consumer behavior and make better decisions about marketing campaigns.

See salaries and explore the career path for Market Researcher