online course big data analytics

Last Updated on August 8, 2022 by Team College Learners

10 Best Big Data Analytics Courses Online in 2021

Today, data science is no longer the buzzword as the rise of the data-driven market. IBM report estimates that data-related job postings will rise to 2.7 million by 2020. That said, the demand for data-related professional skills like machine learning and AI are must-haves for analytic talents. 

This article recommends the 10 best big data analytics online courses for beginners, especially those who plan to make the transition to data analytic jobs.  

Coursera

1. Data Analysis and Presentation Skills: the PwC Approach Specialization

Provider: Price Waterhouse Coopers LLP

Commitment: 21weeks, 3-4hours/week

This specialization includes 5 courses, from data-driven decision making, problem-solving with basic functions of Excel, data visualization with advanced excel, to the business presentation with PowerPoint, and a final project.

  • Course 1: Data-driven Decision Making
  • Course 2: Problem-solving with Excel
  • Course 3: Data Visualization with Advanced Excel
  • Course 4: Effective Business Presentations with PowerPoint
  • Course 5: Data Analysis and Presentation Skills: the PwC Approach Final Project

Average Rating of 4.6

The data analysis specialization is designed for employees by PWC, which undoubtedly focuses more on business application than theory. It’s suitable for people without a programming background.   

2. Data Science Specialization  

Provider: John Hopkins University

Commitment: 43 weeks, 4-9 hours/week

Composed of 10 courses, this specialization covers the concepts and tools you’ll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results.

Average Rating 4.6

This is one of the longest data science specializations on Coursera. Unlike the PWC one, it focuses more on theories related to statistics, algorithm and data analysis. Besides, these courses are based on the R programming language. As a result, a basic knowledge of programming is recommended before taking the courses.

10 Best Big Data Analytics Courses Online in 2021

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3. Big Data Specialization

Provider: University of California, San Diego

Commitment: 30 weeks, 3-6 hours/week

With a total of 6 courses, it covers the main aspects of big data, from the basic introduction, modeling, management systems, integration, and processing, to machine learning and graph analytics.

  • Course 1: Introduction to Big Data
  • Course 2: Big Data Modeling and Management Systems
  • Course 3: Big Data Integration and Processing
  • Course 4: Overview of Machine Learning
  • Course 5: Graph Analytics for Big Data
  • Course 6: Big Data – Final Project

Average rating 4.3

This is a great introduction to big data for beginners which doesn’t delve too much into programming. No prior programming experience is needed. It involves several open-source software tools including Apache Hadoop.  

4. Statistics with R

Provider: Duke University

Commitment: 27 weeks, 5-7 hours/week

With the 5 courses in this specialization, you will learn to analyze and visualize data in R. You will be able to create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference, and modeling.

  • Course 1: Introduction to Probability and Data
  • Course 2: Numerical and Categorical Data
  • Course 3: Linear Regression and Modeling
  • Course 4: Bayesian Statistics
  • Course 5: Statistics with R Capstone

Average Rating  4.5

The course is all about R programming. Please make sure you are fully prepared with programming skills. 

EDX

5. Microsoft Professional Program in Data Science

Provider: Microsoft

Commitment: 56-58 weeks, 2-4 hours/week

Made up of 4 units (10 courses total) and a Final Project. This specialization covers the basic introduction of data science, essential programming languages and advanced programming languages in applied data science.

  • Unit 1 – Fundamentals
  • Unit 2 – Core Data Science
  • Unit 3 – Applied Data Science
  • Unit 4 – Capstone Project

Average Rating  N/A

Unsurprisingly, it has a high connection with Microsoft software, including Excel, Power BI, Azure, and R server. These courses also involve R and Python. 

6. Marketing Analytics

Provider: University of California, Berkeley

Commitment: 16 weeks, 5-7 hours per week.

With the 5 courses in this specialization, you are able to get a certificate & credit Pathways upon graduation. The program is designed and taught by industry expert Stephan Sorger, who held a leadership role in marketing and product development at organizations like Oracle, 3Com, and NASA. 

  • Course 1: BerkeleyX’s Marketing Analytics MicroMasters® Program
  • Course 2: Marketing Analytics: Marketing Measurement Strategy
  • Course 3: Marketing Analytics: Price and Promotion Analytics
  • Course 4: Marketing Analytics: Competitive Analysis and Market Segmentation
  • Course 5: Marketing Analytics: Products, Distribution, and Sales

Average Rating  N/A

This program focuses more on utilizing data on marketing planning and decision, including Marketing Measurement Strategy, Price and Promotion Analytics, Competitive Analysis and Market Segmentation, Products Distribution and Sales. Personally speaking, it’s a good course for a digital marketer who wants to improve his/her numerical ability.

Cognitive Class

7. Big Data Fundamentals

Provider: IBM

Commitment: 13 hours

It only consists of 3 courses. These courses give a brief introduction to Big Data, Hadoop and Spark. Cognitive class is Known as Big Data University before. Now they rebranded it a MOOC provider backed by IBM.

  • Course 1: Big Data 101
  • Course 2: Hadoop 101
  • Course 3: Spark Fundamentals 1

Average Rating  N/A

As a Big Data 101 program, the courses mainly introduce the core concepts about big data and how it immerses in our everyday lives and work. Meanwhile, lots of big data tools are presented to show how data are being captured, processed and visualized.Alternative course: Master of Science in Data ScienceCreator: Maryville UniversityThis program is a 100% online, 36 credit data science program designed to allow you to develop the skills, knowledge, and experience to succeed in the data science field. The courses delve into machine learning, data mining, big data, and deep learning, as well as coding skills in Python, SQL, R, and SAS.MIT Open Courseware 

8. Advanced-Data Structures

Instructor:  Prof. Erik Demaine+

Commitment: 22 sessions, 90mins/session

This course serves as a broad overview of the many different types of data structures, including geometric data structures, like a map, and temporal data structures, as in storage that happens over a time series. It covers the major directions of research for a wide variety of such data structures.

  • Session 1: Persistent Data Structures
  • Session 2: Retroactive Data Structures
  • Session 3~4: Geometric Structures I ~ II
  • Session 5~6: Dynamic Optimality I ~ II
  • Session 7: Memory Hierarchy Models
  • Session 8~9: Cache-Oblivious Structures I ~ II
  • Session 10: Dictionaries
  • Session 11: Integer Models
  • Session 12: Fusion Trees
  • Session 13: Integer Lower Bounds
  • Session 14: Sorting in Linear Time
  • Session 15: Static Trees
  • Session 16: Strings
  • Session 17~18: Succinct Structures I ~ II
  • Session 19~20: Dynamic Graphs I ~ II
  • Session 21: Dynamic Connectivity Lower Bound
  • Session 22: History of Memory Models

Average Rating  N/A

This is an advanced course on explaining the different data structures. To help every learner master the lesson easier, a one-page assignment is provided on a weekly basis to help get rid of the difficulties during the whole learning process.

9. Python

Instructors: Austin Bingham, Robert Smallshire, Terry Toy, Bo Milanovich, Emily Bache, Reindert-Jan Ekker

Commitment: 3 sessions, 28 hours totally

This path will take you from the basics of the Python language all the way up to working with web frameworks and programming.

Python is an interpreted object-oriented programming language. It is open-source, so the interpreter and source are freely available and distributable in binary form. This contributes Python to become a popular programming language in data analysis.

Average Rating  N/A

There are 3 sessions for the beginner, the intermediate and the advanced separately. You could either choose one of the suitable courses or you will grow from zero to hero after you finish all the courses.

Udemy

10. Java Tutorial for Complete Beginners 

Instructor: John Purcell

Commitment: 75 lectures, 16 hours totally

A beginner course to learn the Java programming language. No prior programming knowledge is required. The key reason for recommending this course: Hadoop is Java-based, which is one of the hottest open-source software utilities that paves the ground for big data analysis.

Octoparse

Traditional approaches to extract data online manually are no longer used. You need a much more efficient web-scraping tool to extract information on the Internet.

Octoparse is an automatic web scraping tool recommended by many data experts. It is easy to use, fast to learn and does not require prior programming knowledge. Millions of data online will turn into structured datasheet (Excel, CSV, SQL, API) at your fingertips in seconds.

Abundant tutorials can be found on Octoparse’s website, such as scraping leads from directories (Yellowpages) and scraping product information from an online marketplace (Amazon).

The biggest challenge for you is not how difficult the courses would be, but taking your career to the next level. 

Happy Learning!

Frequently Asked Questions about Big Data Analytics

What are big data analytics, and why are they important to learn about?‎

Big data analytics refers to the application of advanced data analysis techniques to datasets that are very large, diverse (including structured and unstructured data), and often arriving in real time. The ability to process data at this scale is increasingly essential to navigating today’s business world, and it is at the core of important applications such as machine learning, business intelligence, financial engineering, and other software tools to enable data-informed decision-making.Computer programs have been used to assist with data analysis for decades, but tools like Microsoft Excel and traditional relational database management systems (RDBMS) queried with SQL are not capable of handling today’s high-volume, high-velocity datasets. Instead, today’s data management professionals rely on high-powered data infrastructure designed to work with distributed file systems and cloud computing resources – particularly the open-source Apache Hadoop ecosystem, including high-speed data processing with Apache Spark and distributed SQL engines like Apache Hive.‎

What kinds of careers can I get with a background in big data analytics?‎

Organizations of all types and sizes are seeking ways to leverage the possibilities of big data to improve operations through reduced costs and faster decision-making, create new products and services, or advance our knowledge about the world. Big data analytics skills can thus open up a wide range of career opportunities, from working as a “quant” on Wall Street to developing navigation systems for autonomous vehicles to helping to discover more effective medicines and drugs in health science.Two of the most broadly-applicable roles in this field are data engineers, who build the data infrastructure needed to deliver big data-scale datasets efficiently and reliably, and the data scientists responsible for analyzing them. These roles are in high demand, and are highly-paid as well; according to Glassdoor, data engineers earn an average annual salary of $102,864, and data scientists earn an average annual salary of $113,309.‎

Can I learn about big data analytics by taking online courses on Coursera?‎

Absolutely! Data science is one of the most popular topics to learn about on Coursera, and there are a variety of options to build your skills in big data analytics. You can take online courses and Specializations from top-ranked schools like the University of Pennsylvania and the University of California San Diego, as well as leading companies like IBM, PwC, Cloudera, and Google Cloud. And regardless of where you choose to learn from, Coursera gives you the ability to access course materials and complete assignments on a flexible schedule, making this a great fit for students and mid-career professionals alike.‎

What skills or experience do I need to already have before learning about big data analytics?‎Before you start learning big data analytics, it’s helpful to have an understanding of database management and the fundamentals of how programming languages work. Specifically, experience with SQL, Python, Java, or R can be useful when studying big data analytics. You also may find it beneficial to know how to work with Hadoop and Linux and use basic math and statistics. Additionally, strong analytical skills and a curiosity about playing with data come in handy when you learn big data analytics.‎

What kind of people are most suited for work in big data analytics?‎

The right people for roles in big data analytics are inquisitive problem solvers who like working with numbers and using statistics to sort through large amounts of data. They typically have work experience or coursework in high-level math or computer programming. A background in behavioral analysis can also be useful for roles in big data analytics because individuals often seek to understand or predict what influences the behavior represented by data. Big data analysts may often need soft skills, such as communication and collaboration skills they use when explaining what they see in the data and working with team members on projects.‎

How do I know if learning big data analytics is right for me?

‎If you like working with numbers and are comfortable using statistical techniques, learning big data analytics may be right for you. The amount of data collected on a daily basis is already massive and continues to grow, so organizations need analysts who can curate and prepare data for businesses, governments, and other groups to use. Learning big data analytics may interest you if you possess strong analytical and problem-solving skills and want to apply those skills to sorting and analyzing data to find what’s useful for a client. You may be able to use the knowledge you gain to land an internship or seek a career in data science filling roles in a variety of industries.