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Big Data Analytics

Lewis Mitchell, Simon Tuke, and David Suter

Gain essential skills in today’s digital age to store, process and analyse data to inform business decisions.

In this course, part of the Big Data MicroMasters program, you will develop your knowledge of big data analytics and enhance your programming and mathematical skills. You will learn to use essential analytic tools such as Apache Spark and R.

Topics covered in this course include:

  • cloud-based big data analysis;
  • predictive analytics, including probabilistic and statistical models;
  • application of large-scale data analysis;
  • analysis of problem space and data needs.
Read more

Gain essential skills in today’s digital age to store, process and analyse data to inform business decisions.

In this course, part of the Big Data MicroMasters program, you will develop your knowledge of big data analytics and enhance your programming and mathematical skills. You will learn to use essential analytic tools such as Apache Spark and R.

Topics covered in this course include:

  • cloud-based big data analysis;
  • predictive analytics, including probabilistic and statistical models;
  • application of large-scale data analysis;
  • analysis of problem space and data needs.

By the end of this course, you will be able to approach large-scale data science problems with creativity and initiative.

Enroll now

What's inside

Learning objectives

  • How to develop algorithms for the statistical analysis of big data;
  • Knowledge of big data applications;
  • How to use fundamental principles used in predictive analytics;
  • Evaluate and apply appropriate principles, techniques and theories to large-scale data science problems.

Syllabus

Section 1: Simple linear regressionFit a simple linear regression between two variables in R;Interpret output from R;Use models to predict a response variable;Validate the assumptions of the model.
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Designed for learners with some background in data science or programming
Designed for learners who want to enhance their programming and mathematical skills
Taught by Lewis Mitchell, Simon Tuke, and David Suter, who are recognized for their work in the field
Develops skills in cloud-based big data analysis, predictive analytics, and large-scale data analysis

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Reviews summary

Practical big data analytics with r and spark

According to students, this course provides a strong foundation in big data analytics, focusing on practical application using tools like R and Apache Spark. Learners particularly highlight the hands-on labs and challenging, rewarding assignments as key to developing real-world skills. While the course offers comprehensive coverage, including deep learning, some reviewers caution that its fast pace and tendency to assume prior knowledge in R and statistics may make it more suitable for intermediate learners than absolute beginners. Additionally, some felt certain topics lacked sufficient depth, requiring external research. Overall, it's a valuable investment for career-focused individuals.
Covers essential tools like R, Spark, and Deep Learning.
"This course provided an excellent foundation in big data analytics using R and sparklyr."
"The deep learning section was particularly well-done and relevant to current industry trends."
"A good introduction to big data analytics. The focus on R and sparklyr is relevant."
Strong emphasis on practical labs and assignments.
"The hands-on labs were incredibly helpful for understanding the practical applications of the theories."
"Fantastic course for understanding big data concepts and applying them with Spark. The assignments were challenging but very rewarding."
"The practical hands-on exercises were key to my learning. It prepared me well for real-world big data challenges."
Support through forums can be minimal.
"The forum was not very active."
"the support through the forums was minimal."
"It feels like the course assumes a certain level of self-sufficiency."
Covers many topics but may lack in-depth explanations.
"While the course provides a good overview, I felt it lacked the depth needed for a professional. I had to do a lot of external research."
"The theoretical explanations sometimes felt brief. More examples and clearer derivations would have been beneficial."
"Some topics felt like they were touched upon too lightly. Still, a worthwhile investment."
Course can be challenging, assuming existing R/stats background.
"The pace can be quite fast, especially if you're not already familiar with R."
"I struggled with this course. It assumes too much prior knowledge in statistics and R programming."
"I think it's better suited for intermediate learners, not beginners as advertised."

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 Big Data Analytics with these activities:
Review the book 'Applied Statistics and Probability for Engineers'
This book provides a comprehensive overview of the fundamental concepts of statistics and probability, with a focus on applications in engineering
View Applied Statistics and Probability for... on Amazon
Show steps
  • Read the book carefully
  • Take notes on the key concepts
  • Work through the practice problems
Follow the tutorials on the 'StatQuest' YouTube channel
These tutorials provide clear and concise explanations of statistical concepts, with a focus on practical applications
Browse courses on Statistics
Show steps
  • Find a tutorial on the topic you are interested in
  • Watch the tutorial carefully
  • Take notes on the key concepts
Review the fundamentals of statistics
In order to understand the concepts covered in this course you should review the basic principles of statistics
Browse courses on Statistics
Show steps
  • Review the basics of probability distributions
  • Review the basics of statistical inference
  • Review the basics of regression analysis
Six other activities
Expand to see all activities and additional details
Show all nine activities
Create a cheat sheet of statistical formulas
Creating a cheat sheet will help you organize your knowledge and make it easier to remember the formulas you need
Browse courses on Statistics
Show steps
  • List all of the statistical formulas you need to know
  • Organize the formulas into a logical order
  • Create a visually appealing cheat sheet
Participate in a peer study group
Studying with peers can help you to learn the material more effectively and identify areas where you need additional support
Browse courses on Statistics
Show steps
  • Find a group of students who are also taking the course
  • Meet with the group regularly to discuss the material
  • Work together on practice problems
Practice solving statistical problems
Solving statistical problems will help you develop your problem-solving skills and improve your understanding of the material
Browse courses on Statistics
Show steps
  • Find a collection of statistical problems
  • Attempt to solve the problems on your own
  • Check your solutions against the provided answer key
Mentor other students who are struggling with statistics
Helping others to learn statistics will reinforce your own understanding of the material
Browse courses on Statistics
Show steps
  • Find a student who is struggling with statistics
  • Offer to help them with their studies
  • Meet with the student regularly to provide guidance and support
Start a project that uses statistical analysis to solve a real-world problem
Working on a project will give you the opportunity to apply your statistical knowledge to a practical problem and develop your problem-solving skills
Browse courses on Statistics
Show steps
  • Identify a real-world problem that can be solved using statistical analysis
  • Collect data on the problem
  • Analyze the data using statistical methods
  • Develop a solution to the problem based on your analysis
Participate in a statistics competition
Participating in a competition can help you to test your knowledge and skills, and to learn from others
Browse courses on Statistics
Show steps
  • Find a statistics competition that you are interested in
  • Register for the competition
  • Prepare for the competition by studying and practicing
  • Compete in the competition

Career center

Learners who complete Big Data Analytics will develop knowledge and skills that may be useful to these careers:
Data Analyst
A Data Analyst designs and builds systems and tools that retrieve, scrub, interpret, and visualize large amounts of raw data from a variety of sources. Part of this job is to develop new data mining techniques that will improve the quality and efficiency of data analysis. Data Analysts require knowledge of analytics, modeling, and databases, which are all skills that you will gain from taking the Big Data Analytics course.
See salaries and explore the career path for Data Analyst
Data Scientist
Data Scientists refine existing machine learning and statistical models using data provided from business analysts to identify risks and make predictions. They often wear many hats and are expected to be skilled in machine learning, data wrangling, communication, and programming. These are skills that will be refined taking the Big Data Analytics course.
See salaries and explore the career path for Data Scientist
Deep Learning Scientist
Deep Learning Scientists design and implement deep learning algorithms and models to solve complex problems in areas like image recognition and natural language processing. They use large data sets to train neural networks and other deep learning models. This course will introduce you to deep learning, enabling you to pursue a career as a Deep Learning Scientist.
See salaries and explore the career path for Deep Learning Scientist
Machine Learning Engineer
Machine Learning Engineers tune and implement algorithms that learn from big data, then develop and maintain the systems that power machine learning applications. They help companies stay competitive by improving the effectiveness of products and services through the use of data. While this course does not directly teach machine learning, you will learn about models that can be applied to machine learning.
See salaries and explore the career path for Machine Learning Engineer
Business Intelligence Analyst
Business Intelligence Analysts analyze data and trends to help businesses make informed decisions about strategy, operations, and marketing. They interpret complex data and communicate their insights to stakeholders in a clear and concise way. This course will give you the skills you need to become a successful Business Intelligence Analyst.
See salaries and explore the career path for Business Intelligence Analyst
Data Architect
Data Architects design, build, and maintain data architectures that support the needs of an organization. They work with business stakeholders to understand their data requirements and then design and implement data solutions that meet those requirements. This course will help you develop a solid understanding of the principles of data architecture.
See salaries and explore the career path for Data Architect
Database Administrator
Database Administrators maintain and optimize databases to ensure that they are running efficiently and securely. They also work with database users to provide support and training. This course will give you the skills you need to become a successful Database Administrator.
See salaries and explore the career path for Database Administrator
Data Engineer
Data Engineers build and maintain the systems that store and process data. They work with data scientists and other stakeholders to understand the data needs of the organization and then design and implement data solutions that meet those needs. This course will give you the skills you need to become a successful Data Engineer.
See salaries and explore the career path for Data Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with stakeholders to understand their needs and then design and implement software solutions that meet those needs. This course will help you develop a solid foundation in software engineering, which will be helpful if you want to pursue a career as a Software Engineer.
See salaries and explore the career path for Software Engineer
Statistician
Statisticians collect, analyze, and interpret data. They use their knowledge of statistics to draw conclusions about the world around them. This course will help you develop a solid foundation in statistics, which will be helpful if you want to pursue a career as a Statistician.
See salaries and explore the career path for Statistician
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve problems in business and industry. They work with stakeholders to understand their needs and then develop and implement solutions that improve efficiency and productivity. This course will help you develop a solid foundation in operations research, which will be helpful if you want to pursue a career as an Operations Research Analyst.
See salaries and explore the career path for Operations Research Analyst
Financial Analyst
Financial Analysts use data to make investment decisions. They analyze financial data to identify trends and make recommendations about which investments to buy or sell. This course will help you develop a solid foundation in financial analysis, which will be helpful if you want to pursue a career as a Financial Analyst.
See salaries and explore the career path for Financial Analyst
Market Research Analyst
Market Research Analysts conduct research to understand consumer behavior and trends. They use their findings to help businesses make informed decisions about product development and marketing. This course will help you develop a solid foundation in market research, which will be helpful if you want to pursue a career as a Market Research Analyst.
See salaries and explore the career path for Market Research Analyst
Actuary
Actuaries use mathematics and statistics to assess risk and uncertainty. They work with insurance companies and other organizations to develop and implement strategies to manage risk. This course will help you develop a solid foundation in actuarial science, which will be helpful if you want to pursue a career as an Actuary.
See salaries and explore the career path for Actuary
Economist
Economists study how people make decisions in the face of scarcity. They use economic models to analyze data and make predictions about the economy. This course will help you develop a solid foundation in economics, which will be helpful if you want to pursue a career as an Economist.
See salaries and explore the career path for Economist

Reading list

We've selected 11 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Big Data Analytics.
Cover image
Principles of Database Management
Save
Provides a comprehensive overview of big data analytics, from strategic planning to enterprise integration. It covers both the theoretical foundations and practical applications of big data analytics, and valuable resource for both students and practitioners.
Principles of Database Management: The Practical...
Hardcover
$$$
Principles of Database Management: The Practical...
Kindle Edition
$$$
Data Mining
Save
Classic textbook on data mining, and valuable resource for students and practitioners who want to learn about the foundations of data mining. It covers a wide range of topics, including data preprocessing, clustering, classification, and association rule mining.
Data Mining: Concepts and Techniques (The Morgan...
Kindle Edition
$$$
Data Mining: Concepts and Techniques (The Morgan...
Hardcover
$$$
Data Mining: Concepts and Techniques, Second...
Hardcover
$$
Data Mining: Know It All
Hardcover
$$$
Cover image
Reinforcement Learning, second edition
Save
Classic textbook on reinforcement learning, and valuable resource for students and practitioners who want to learn about the foundations of reinforcement learning.
Reinforcement Learning, second edition: An...
Hardcover
$$$
Reinforcement Learning, second edition: An...
Kindle Edition
$$$
Cover image
The Elements of Statistical Learning
Save
Classic textbook on statistical learning, and valuable resource for students and practitioners who want to learn about the foundations of statistical learning.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Kindle Edition
$$$
Cover image
The AI Playbook
Save
Provides a practical introduction to predictive analytics, and valuable resource for students and practitioners who want to learn how to use predictive analytics to solve business problems.
The AI Playbook: Mastering the Rare Art of Machine...
Hardcover
$$
The AI Playbook: Mastering the Rare Art of Machine...
Kindle Edition
$$
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
Provides a practical introduction to machine learning with Scikit-Learn, Keras, and TensorFlow, and valuable resource for students and practitioners who want to learn how to use these libraries to build and train machine learning models.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
Cover image
Data Science for Business
Save
Provides a practical introduction to data science for business, and valuable resource for students and practitioners who want to learn how to use data science to solve business problems.
Data Science for Business: What You Need to Know...
Paperback
$$
Data Science for Business: What You Need to Know...
Kindle Edition
$$
Cover image
Bayesian Analysis with Python
Save
Provides a practical introduction to TensorFlow, and valuable resource for students and practitioners who want to learn how to use TensorFlow to build and train deep learning models.
Bayesian Analysis with Python - Third Edition: A...
Paperback
$$
Bayesian Analysis with Python - Second Edition:...
Paperback
$$
Bayesian Analysis with Python: Click here to enter...
Kindle Edition
$$
Cover image
Deep Learning with TensorFlow and Keras
Save
Provides a practical introduction to deep learning with Python, and valuable resource for students and practitioners who want to learn how to use deep learning to solve real-world problems.
Deep Learning with TensorFlow and Keras - Third...
Paperback
$$
Deep Learning with TensorFlow 2 and Keras - Second...
Paperback
$$
Deep Learning with TensorFlow and Keras: Build and...
Kindle Edition
$$
Deep Learning with TensorFlow 2 and Keras:...
Kindle Edition
$$
Cover image
Big Data: Second Edition
Save
Gentle introduction to data analytics, and good starting point for students and practitioners who are new to the field. It covers the basic concepts of data analytics, including data collection, cleaning, and analysis.
Big Data: Second Edition
Paperback
Check
Big Data: Second Edition
Kindle Edition
Check
Cover image
Big Data for Dummies
Save
Gentle introduction to big data analytics, and good starting point for students and practitioners who are new to the field.
Big Data for Dummies
Paperback
Check
Big Data for Dummies
Kindle Edition
Check

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Course image
Effort
8 to 10 hours per week for 10 weeks
Level
Intermediate
Via
edX
Institution
University of Adelaide
Instructors
Lewis Mitchell
Simon Tuke
David Suter
Language
English
Transcript
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Designed for learners with some background in data science or programming
Designed for learners who want to enhance their programming and mathematical skills
Taught by Lewis Mitchell, Simon Tuke, and David Suter, who are recognized for their work in the field
Develops skills in cloud-based big data analysis, predictive analytics, and large-scale data analysis
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