Fundamentals of Scalable Data Science from Coursera

Apache Spark is the de-facto standard for large scale data processing. This is the first course of a series of courses towards the IBM Advanced Data Science Specialization. We strongly believe that is is crucial for success to start learning a scalable data science platform since memory and CPU constraints are to most limiting factors when it comes to building advanced machine learning models.

In this course we teach you the fundamentals of Apache Spark using python and pyspark. We'll introduce Apache Spark in the first two weeks and learn how to apply it to compute basic exploratory and data pre-processing tasks in the last two weeks. Through this exercise you'll also be introduced to the most fundamental statistical measures and data visualization technologies.

This gives you enough knowledge to take over the role of a data engineer in any modern environment. But it gives you also the basis for advancing your career towards data science.

Please have a look at the full specialization curriculum:

https://www.coursera.org/specializations/advanced-data-science-ibm

If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.

After completing this course, you will be able to:

• Describe how basic statistical measures, are used to reveal patterns within the data

• Recognize data characteristics, patterns, trends, deviations or inconsistencies, and potential outliers.

• Identify useful techniques for working with big data such as dimension reduction and feature selection methods

• Use advanced tools and charting libraries to:

o improve efficiency of analysis of big-data with partitioning and parallel analysis

o Visualize the data in an number of 2D and 3D formats (Box Plot, Run Chart, Scatter Plot, Pareto Chart, and Multidimensional Scaling)

For successful completion of the course, the following prerequisites are recommended:

• Basic programming skills in python

• Basic math

• Basic SQL (you can get it easily from https://www.coursera.org/learn/sql-data-science if needed)

In order to complete this course, the following technologies will be used:

(These technologies are introduced in the course as necessary so no previous knowledge is required.)

• Jupyter notebooks (brought to you by IBM Watson Studio for free)

• ApacheSpark (brought to you by IBM Watson Studio for free)

• Python

We've been reported that some of the material in this course is too advanced. So in case you feel the same, please have a look at the following materials first before starting this course, we've been reported that this really helps.

Of course, you can give this course a try first and then in case you need, take the following courses / materials. It's free...

https://cognitiveclass.ai/learn/spark

https://dataplatform.cloud.ibm.com/analytics/notebooks/v2/f8982db1-5e55-46d6-a272-fd11b670be38/view?access_token=533a1925cd1c4c362aabe7b3336b3eae2a99e0dc923ec0775d891c31c5bbbc68

This course takes four weeks, 4-6h per week

What's inside

Syllabus

Introduction the course and grading environment

Tools that support BigData solutions

Scaling Math for Statistics on Apache Spark

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Strengthens the foundation of data processing and analytics with Apache Spark for a successful transition to data science

Develops expertise in statistical measures and data visualization techniques for insightful analysis

Provides hands-on experience with Jupyter notebooks and Apache Spark, prevalent tools in industry

Taught by Romeo Kienzler, an instructor recognized in the field of data science

Emphasizes practical applications, preparing learners for real-world data analysis scenarios

Serves as the foundation for the IBM Advanced Data Science Specialization, providing a comprehensive learning path

Reviews summary

Introduction to spark and big data

According to learners, this course provides a positive and solid introduction to Apache Spark and PySpark, which is highly relevant for big data processing. Students appreciate the practical, hands-on labs and assignments that help reinforce the concepts taught in the lectures. However, some students report that the course's difficulty level increases significantly after the initial weeks, suggesting that the recommended prerequisites may be underestimated for some. There are also occasional mentions of technical issues with the IBM Watson Studio environment used for the labs. Overall, it is considered a valuable first step for those aiming for data engineering or data science careers.

Hands-on exercises reinforce learning effectively.

"Really appreciated the hands-on coding exercises in Jupyter notebooks."

"The labs were very practical and helped solidify my understanding."

"Assignments were challenging but fair, making the concepts stick."

"The practical application through labs is the strongest part of the course."

Provides a strong foundation in Apache Spark basics.

"This course is a great introduction to Apache Spark using PySpark."

"I feel like I got a solid foundation in Spark fundamentals from this course."

"The basics of Spark RDDs and DataFrames were explained clearly."

"Excellent starting point for anyone new to large scale data processing with Spark."

Platform setup and stability can be challenging.

"Had some issues getting the Watson Studio environment configured correctly."

"Experienced occasional glitches and downtime with the lab platform."

"Setting up the labs took more time than expected due to environment problems."

"The course content is good, but the platform adds frustration."

Requires more background than stated for comfort.

"Basic Python isn't enough; a solid intermediate level would be better."

"Recommend brushing up on pandas/numpy before starting this course."

"Struggled if I didn't have a stronger programming or data background."

"The course description downplays the needed prior knowledge."

Course complexity increases significantly mid-way.

"The difficulty ramps up quite suddenly around week 3."

"Found the later modules much harder than the first two weeks."

"Wish there was a smoother transition in the complexity of the topics."

"It felt like the course jumped from beginner to intermediate very quickly."

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 Fundamentals of Scalable Data Science with these activities:

Organize Your Course Materials

Show steps

Establish a strong foundation for learning by organizing your course materials.

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Create folders for different topics and assignments
File lecture notes, readings, and assignments in the appropriate folders
Digitize and store important materials
Establish a consistent naming convention

Review Elementary Statistics Concepts

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Refresh your knowledge of basic statistics to enhance comprehension of course material.

Browse courses on Statistics

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Review descriptive statistics (mean, median, mode, standard deviation)
Refamiliarize yourself with inferential statistics (hypothesis testing, confidence intervals)
Practice solving basic statistics problems

Practice Python Programming

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Sharpen your Python skills to maximize comprehension of code examples and exercises.

Browse courses on Python

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Review Python syntax and data structures
Solve coding challenges and exercises
Build a small Python project

Six other activities

Expand to see all activities and additional details

Show all nine activities

Follow Apache Spark Tutorials

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Expand your knowledge of Apache Spark by exploring online tutorials.

Browse courses on Apache Spark

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Search for Apache Spark tutorials
Choose tutorials that align with your learning goals
Follow the tutorials step-by-step
Practice the concepts learned in the tutorials

Build a Simple Apache Spark Application

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Develop a basic understanding of Apache Spark by creating a simple application.

Browse courses on Apache Spark

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Set up your development environment
Create a Spark application
Load data into Spark
Transform and analyze the data
Save the results

Solve Apache Spark Exercises

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Test and strengthen your Apache Spark skills by solving exercises.

Browse courses on Apache Spark

Show steps

Search for Apache Spark exercises
Select exercises that cover different concepts and scenarios
Solve the exercises independently
Review your solutions and identify areas for improvement

Join a Study Group for Apache Spark

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Deepen your understanding of Apache Spark through collaboration and knowledge sharing.

Browse courses on Apache Spark

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Find a study group or create your own
Meet regularly to discuss course material, share ideas, and solve problems
Review lecture notes, readings, and exercises together
Provide constructive feedback and support to group members

Create a Data Visualization Dashboard

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Enhance your understanding of data visualization techniques by creating a dashboard.

Browse courses on Data Visualization

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Gather and clean the data
Choose the appropriate visualization tools
Design and create the dashboard
Present and share your dashboard

Contribute to Apache Spark Projects

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Deepen your understanding of Apache Spark and contribute to the community by participating in open source projects.

Browse courses on Apache Spark

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Identify Apache Spark projects that align with your interests
Read the project documentation and familiarize yourself with the codebase
Make small contributions, such as bug fixes or documentation updates
Collaborate with other contributors on larger features or enhancements

Career center

Learners who complete Fundamentals of Scalable Data Science will develop knowledge and skills that may be useful to these careers:

Data Analyst

A Data Analyst collects, analyzes, interprets, and presents data to help organizations make informed decisions. This course can help you develop the skills needed to succeed in this role by providing a foundation in Apache Spark, a popular tool for working with big data. You will learn how to use Apache Spark to perform data exploration, data visualization, and statistical analysis. This course can also help you build a portfolio of projects that you can use to showcase your skills to potential employers.

See salaries and explore the career path for Data Analyst

Data Engineer

A Data Engineer designs, builds, and maintains data pipelines and databases. This course can help you develop the skills needed to succeed in this role by providing a foundation in Apache Spark, a popular tool for working with big data. You will learn how to use Apache Spark to perform data ingestion, data transformation, and data analysis. This course can also help you build a portfolio of projects that you can use to showcase your skills to potential employers.

See salaries and explore the career path for Data Engineer

Data Scientist

A Data Scientist uses data to solve business problems. This course can help you develop the skills needed to succeed in this role by providing a foundation in Apache Spark, a popular tool for working with big data. You will learn how to use Apache Spark to perform data exploration, data visualization, and statistical analysis. This course can also help you build a portfolio of projects that you can use to showcase your skills to potential employers.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

A Machine Learning Engineer builds and deploys machine learning models. This course can help you develop the skills needed to succeed in this role by providing a foundation in Apache Spark, a popular tool for working with big data. You will learn how to use Apache Spark to perform data preparation, feature engineering, and model training. This course can also help you build a portfolio of projects that you can use to showcase your skills to potential employers.

See salaries and explore the career path for Machine Learning Engineer

Statistician

A Statistician collects, analyzes, interprets, and presents data to help organizations make informed decisions. This course can help you develop the skills needed to succeed in this role by providing a foundation in Apache Spark, a popular tool for working with big data. You will learn how to use Apache Spark to perform data exploration, data visualization, and statistical analysis. This course can also help you build a portfolio of projects that you can use to showcase your skills to potential employers.

See salaries and explore the career path for Statistician

Business Analyst

A Business Analyst uses data to identify and solve business problems. This course can help you develop the skills needed to succeed in this role by providing a foundation in Apache Spark, a popular tool for working with big data. You will learn how to use Apache Spark to perform data exploration, data visualization, and statistical analysis. This course can also help you build a portfolio of projects that you can use to showcase your skills to potential employers.

See salaries and explore the career path for Business Analyst

Financial Analyst

A Financial Analyst uses data to make investment decisions. This course can help you develop the skills needed to succeed in this role by providing a foundation in Apache Spark, a popular tool for working with big data. You will learn how to use Apache Spark to perform data exploration, data visualization, and statistical analysis. This course can also help you build a portfolio of projects that you can use to showcase your skills to potential employers.

See salaries and explore the career path for Financial Analyst

Market Research Analyst

A Market Research Analyst collects, analyzes, and interprets data to help businesses understand their customers. This course can help you develop the skills needed to succeed in this role by providing a foundation in Apache Spark, a popular tool for working with big data. You will learn how to use Apache Spark to perform data exploration, data visualization, and statistical analysis. This course can also help you build a portfolio of projects that you can use to showcase your skills to potential employers.

See salaries and explore the career path for Market Research Analyst

Operations Research Analyst

An Operations Research Analyst uses data to improve the efficiency of business operations. This course can help you develop the skills needed to succeed in this role by providing a foundation in Apache Spark, a popular tool for working with big data. You will learn how to use Apache Spark to perform data exploration, data visualization, and statistical analysis. This course can also help you build a portfolio of projects that you can use to showcase your skills to potential employers.

See salaries and explore the career path for Operations Research Analyst

Product Manager

A Product Manager is responsible for the development and launch of new products. This course can help you develop the skills needed to succeed in this role by providing a foundation in Apache Spark, a popular tool for working with big data. You will learn how to use Apache Spark to perform data exploration, data visualization, and statistical analysis to better understand your customers and their needs.

See salaries and explore the career path for Product Manager

Software Engineer

A Software Engineer designs, develops, and maintains software systems. This course can help you develop the skills needed to succeed in this role by providing a foundation in Apache Spark, a popular tool for working with big data. You will learn how to use Apache Spark to perform data ingestion, data transformation, and data analysis.

See salaries and explore the career path for Software Engineer

Web Developer

A Web Developer designs and develops websites. This course can help you develop the skills needed to succeed in this role by providing a foundation in Apache Spark, a popular tool for working with big data. You will learn how to use Apache Spark to perform data exploration, data visualization, and statistical analysis to better understand your website's visitors.

See salaries and explore the career path for Web Developer

Data Visualization Engineer

A Data Visualization Engineer designs and develops data visualizations. This course can help you develop the skills needed to succeed in this role by providing a foundation in Apache Spark, a popular tool for working with big data. You will learn how to use Apache Spark to perform data exploration, data visualization, and statistical analysis to create beautiful and informative data visualizations.

See salaries and explore the career path for Data Visualization Engineer

Data Architect

A Data Architect designs and manages data systems. This course can help you develop the skills needed to succeed in this role by providing a foundation in Apache Spark, a popular tool for working with big data. You will learn how to use Apache Spark to perform data ingestion, data transformation, and data analysis.

See salaries and explore the career path for Data Architect

Data Governance Analyst

A Data Governance Analyst develops and implements data governance policies and procedures. This course can help you develop the skills needed to succeed in this role by providing a foundation in Apache Spark, a popular tool for working with big data. You will learn how to use Apache Spark to perform data exploration, data visualization, and statistical analysis to identify and address data governance issues.

See salaries and explore the career path for Data Governance Analyst