22 Best Big Data Courses & Certifications Online To Take In 2023

Best Big Data Courses & Certifications

1. Introduction to Big Data

This comprehensive course is designed for individuals who are new to data science and are keen to enhance their understanding of the Big Data landscape. By providing an introduction to one of the most common frameworks, Hadoop, the course makes big data analysis more accessible and understandable, allowing participants to explore the potential of data to transform our world. The course covers core concepts and terminology related to big data problems, applications, and systems, allowing students to become conversant with the intricacies of this rapidly growing field.

Upon completing this course, participants will be able to describe the Big Data landscape and its real-world applications, explain the importance of each of the V's of Big Data (volume, velocity, variety, veracity, valence, and value), and extract value from Big Data through a structured 5-step analysis process. Additionally, students will gain a clear understanding of the architectural components and programming models used for scalable big data analysis, and will be able to summarize the features and value of core Hadoop stack components, such as the YARN resource and job management system, the HDFS file system, and the MapReduce programming model. While prior programming experience is not necessary, the ability to install applications and use a virtual machine is essential for completing hands-on assignments. This course is perfect for anyone interested in diving into the field of big data analysis in their business or career.

2. Managing Big Data with MySQL

This course offers a comprehensive introduction to the use of relational databases in the realm of business analysis, particularly focusing on the management of big data with MySQL. Students will gain an understanding of how relational databases function and learn to utilize entity-relationship diagrams to represent the structure of data contained within these databases. This knowledge will prove invaluable in understanding the process of data collection within a business context and identifying key features to be considered when implementing new data collection methods. Furthermore, the course teaches students how to execute the most relevant query and table aggregation statements for business analysts, even providing hands-on practice with real databases.

Upon completion of the course, students will have a solid grasp of how relational databases operate and possess a portfolio of queries to showcase to potential employers. Businesses are rapidly accumulating vast amounts of data with the intention of uncovering unique insights that will lead to improvements and growth. Analysts who are skilled in accessing and managing this data will hold a significant competitive advantage in today's data-driven business landscape. This course prepares students not just to contribute to their companies more effectively but also to stand out in the job market by mastering the tools and techniques needed for managing big data with MySQL.

3. Big Data and Hadoop Essentials

This course offers a comprehensive introduction to the essentials of Big Data and Hadoop, providing valuable insight into the world of Big Data technologies. Designed for those who may find the topic a bit cryptic, the course aims to build a fundamental understanding of Big Data challenges and Hadoop as a viable solution. By covering aspects such as the history and development of Hadoop, its unique and powerful capabilities, and the difference between data science and data engineering, participants will gain a solid foundation in the subject.

Additionally, the course demystifies the Hadoop landscape by discussing major players and vendors such as Cloudera, MapR, and Hortonworks. This essential knowledge will be beneficial for individuals associated with Big Data and Hadoop in various capacities, including large and small businesses, as well as for personal growth and understanding. By participating in this course, you'll be better equipped to untangle the complexities of Big Data and unlock its potential for problem-solving and innovation.

With so much hype around the Big Data world, it's very easy to get lost. My first book on Big Data left me that taste in my mouth... I read 400 pages of process theory and understood nothing on how to apply. The course Big Data and Hadoop Essentials is a great primer to jump into the big data world with some practical understanding. You will learn about what big data means and why it is different from other business data. You will also understand how Hadoop evolved as the need to study big data emerged from industry standards on MapReduce and new File Systems. The video touches lightly on the core components of Hadoop. You will not be able to run an installation or solve a problem, but you will be able to know just enough to assess if Hadoop is right to solve your problem as a Data Engineer, or you want to work on more data science math-like problems as a data scientist. Truly loved it! ^[1] ... Read More

4. The Ultimate Hands-On Hadoop: Tame your Big Data!

This comprehensive course dives deep into the world of Hadoop and Big Data, demystifying the various technologies that form the Hadoop ecosystem. Throughout the course, participants will gain hands-on experience not only understanding the components of these systems but also learning how they can be used together to solve real business challenges. The course is taught by a former engineer and senior manager from Amazon and IMDb, offering a unique perspective on how to master the most popular data engineering technologies and integrate them with various distributed systems.

Participants will have the opportunity to work with a multitude of tools and frameworks including HDFS, MapReduce, Pig, Spark, Apache Flink, Hive, HBase, MongoDB, Cassandra, and Kafka, among many others. This empowers learners to manage big data on a cluster, design real-world systems, handle streaming data in real-time, and more. With over 25 different technologies covered in 14 hours of video lectures, those who complete the course will have a deep understanding of Hadoop and its associated distributed systems, ready to apply their new skills to real-world problems in various companies and industries.

Great course to learn most of Hadoop Ecosystem. Sadly even with HDP 2.6.5, there were some hiccups where things didn't work properly (so far it happened with presto where version mismatch is interfering with following course practice. Downloading and installing different versions of this and that re sulted in corrupting my VM which I had to re-install from scratch; Flink is working, but you can't see the logs as the video shows) It's still working as of May 2022, however, the technology is definitely becoming outdated. What's told in video doesn't always work, and you have to search through lecture Q&A to make things work. If you just need conceptual knowledge and some exercises on Hadoop Ecosystem, this course is for you. If you are looking for real-life experience because you are HDP developer, yeah this still works for you. If you want latest technologies, this may not work best for you. ^[2] ... Read More

5. Financial Forecasting with Big Data

Big data is transforming the world of business. Yet many people don't understand what big data and business intelligence are, or how to apply the techniques to their day-to-day jobs. This course addresses that knowledge gap, giving businesspeople practical methods to create quick and relevant business forecasts using big data.

Join Professor Michael McDonald and discover how to use predictive analytics to forecast key performance indicators of interest, such as quarterly sales, projected cash flow, or even optimized product pricing. All you need is Microsoft Excel. Michael uses the built-in formulas, functions, and calculations to perform regression analysis, calculate confidence intervals, and stress test your results. You'll walk away from the course able to immediately begin creating forecasts for your own business needs.

The content of this course covers a considerable aspect of financial forecasting with Big Data. Also, the presentation was at a pace that was easy to follow. I highly recommend the course to anyone struggling to understand the use of regression in forecasting trends in financial data. ^[3] ... Read More

6. Big Data Analysis with Scala and Spark

This course focuses on big data analysis using functional programming concepts and aims at teaching learners how to manipulate large-scale distributed data. With the growing popularity of MapReduce, Hadoop, and Apache Spark, it is crucial for professionals to understand the industry applications of these technologies. The course dives into the details of Spark's programming model and highlights the crucial distinctions between familiar programming models like shared-memory parallel collections or sequential Scala collections. By using hands-on examples in Spark and Scala, learners will gain an understanding of critical distribution issues like latency and network communication and ultimately learn to address these for improved performance.

Upon completing this course, learners will be able to read data from persistent storage and load it into Apache Spark, manipulate data using Spark and Scala, express algorithms for data analysis in a functional style, and recognize ways to avoid shuffles and recomputation in Spark. While at least one year of programming experience is recommended, proficiency in Java or C# is ideal. However, experience in other languages such as C/C++, Python, Javascript, or Ruby is sufficient. Familiarity with the command line is essential, and it is advised to complete the course on Parallel Programming before enrolling in this one.

Taking into consideration that this was the first edition of the course, I can say that it has been a nice journey. I am glad about the fact that Heather managed to expose a bit of the Spark internals and not only talk about querying data and how easily this can be made by using Spark (as most of th e Spark oriented courses consist of). In addition to this, I could listen to Heather all day long - she's a great presenter and has wonderful teaching skills. However, the homework has outlined some neglected aspects of the course: - vague description or requirements - not strongly related to the presented content (the lectures outlined partitioning mechanism, but the homework 2 did not require it...) - not so meaningful feedback, except for some tests failing/passing - I would have expected something like you did ok, but your job took longer than expected; check out this and that Overall, it's been a highly expected course and it was nice to get a broader outlook on Spark. I hope that there will be more courses (and more detailed) related to Spark ecosystem in the near future. ^[4] ... Read More

7. Big Data Modeling and Management Systems

This course offers an in-depth exploration of big data modeling and management systems, providing students with the knowledge and skills necessary to analyze various data genres and utilize appropriate management tools. Learners will examine the reasons behind the development of new big data platforms from the perspective of management systems and analytical tools. By taking part in guided, hands-on tutorials, students will become familiar with techniques using real-time and semi-structured data examples, and will delve into systems and tools such as AsterixDB, HP Vertica, Impala, Neo4j, Redis, and SparkSQL. The course ultimately aims to equip learners with techniques for extracting value from untapped data sources, as well as discovering new data sources.

Upon completion of the course, students will be able to recognize different data elements in their own work and in everyday life problems; explain the necessity of a Big Data Infrastructure Plan and Information System Design for their team; identify frequent data operations required for various data types; select a data model suitable for their data's characteristics; apply techniques for handling streaming data; differentiate between traditional Database Management Systems and Big Data Management Systems; understand why there are numerous data management systems; and design a big data information system for an online game company. This course is intended for those new to data science, with no prior programming experience needed. However, the ability to install applications and utilize a virtual machine is crucial for completing hands-on assignments. Basic hardware and software requirements must also be met to ensure a smooth learning experience.

8. Big Data in the Age of AI

This course provides an in-depth exploration of big data and its workings, delving into its connections with artificial intelligence (AI), data science, social media, and the Internet of Things (IoT). The content is designed to help learners understand the foundational role that big data plays in the development of cutting-edge technologies such as AI, machine learning, and data science. Furthermore, the course sheds light on how big data shapes our modern data universe, which is characterized by the constant generation and analysis of massive amounts of information.

In addition to providing a comprehensive overview of big data, the course tackles various ethical issues associated with its use and discusses techniques for analyzing big data, including data mining and predictive analytics. Delivered in a nontechnical format, the course is suitable for individuals who are new to the subject or seeking to expand their knowledge of the critical role that big data plays in today's technology landscape. By the end of the course, learners will have gained essential insights into the central role of big data in contemporary technologies and the various methods and tools that can be employed to analyze it.

The Big Data in the Age of AI course offers a distilled summary which outlines the most salient points explaining the subject. There are some sections which seem to be complementary to alternative online course material especially with respect to data warehousing, data lakes, edge computing, fog com puting and architectural differences. ^[5] ... Read More

9. Knowledge Management and Big Data in Business

This course sheds light on the integrative power of knowledge management, Big Data, and Cloud Computing, shedding light on their immense impact on the contemporary business era. As the traditional methods in management, business, and computing struggle to meet the needs of the ever-evolving business world, it is crucial to understand the role of knowledge management in the knowledge era. With the digital universe anticipated to reach 40 zetabytes by 2020, the course emphasizes the significance of digitalization, Cloud Computing, and Big Data in transforming our lives and work.

Offered by the Knowledge Management and Innovation Research Center (KMIRC) of the Hong Kong Polytechnic University, the course covers a wide range of topics including knowledge capture, organization, and creation in business, as well as data analytics and open linked data. The course is designed for participants with varied backgrounds such as humanities, management, social science, physical science, or engineering. By studying this course, students will acquire indispensable skills and insights to navigate the rapidly transforming landscape of the Networked Economy and beyond. No prior technical background is required for enrollment.

10. Big Data Integration and Processing

This comprehensive course aims to equip participants with the practical skills and conceptual understanding needed to handle and process big data. Learners will gain experience in working with databases and big data management systems, while also exploring the relationship between data management operations and the processing patterns utilized in large-scale analytical applications. The course is designed for those who are new to data science, with no prior programming experience required. However, the ability to install applications and use a virtual machine is necessary to complete the hands-on assignments. The course also covers essential big data processing and integration techniques using popular platforms such as Hadoop and Spark.

Upon completion of the course, students will be able to retrieve data from example databases and big data management systems, identify when a problem requires data integration, and execute basic big data integration and processing tasks on Hadoop and Spark platforms. As the course covers several open-source software tools, including Apache Hadoop, learners will be provided with detailed instructions on downloading and installing the required software on their systems. The course is designed to ensure that participants become proficient in handling big data while working with cutting-edge industry tools and frameworks.

11. Machine Learning With Big Data

This comprehensive course is designed to help individuals make sense of the vast amounts of data they collect by providing an in-depth overview of various machine learning techniques that can be utilized for the exploration, analysis, and leveraging of that data. Through the use of widely available open source tools and algorithms, students will be introduced to methods for creating machine learning models that effectively learn from data, and learn how to scale those models up for big data problems with powerful platforms like Spark.

Upon completion of the course, participants will be equipped with the skills to design a data leveraging approach rooted in the steps of the machine learning process, apply machine learning techniques for data exploration and modeling preparation, identify the right machine learning problem to apply the appropriate set of techniques, and construct models that effectively learn from data. Additionally, learners will gain experience in analyzing big data problems using scalable machine learning algorithms on Spark, preparing them to tackle real-world, big data challenges. Software requirements for the course include Cloudera VM, KNIME, and Spark.

12. Big Data Analytics Using Spark

This comprehensive course delves into the world of big data analysis, teaching students how to work with large datasets using Jupyter notebooks, MapReduce, and Spark as a platform. As the field of data science advances, the term "big data" has come to define datasets that are too large to be processed using conventional methods, requiring the use of distributed file systems such as Hadoop Distributed File System (HDFS) and computational models like Hadoop, MapReduce, and Spark.

Throughout this course, students will gain an understanding of the bottlenecks that arise in massive parallel computation and how Spark can be utilized to minimize these issues. Participants will also learn how to perform supervised and unsupervised machine learning on vast datasets using the Machine Learning Library (MLlib). As part of the Data Science MicroMasters program, this course provides hands-on experience with PySpark within the Jupyter notebooks environment, establishing a strong foundation for a successful career in data science.

13. Big Data Analytics

This course is designed to equip learners with key technologies and techniques, including R and Apache Spark, which are necessary to analyze large-scale data sets and uncover valuable business information. The curriculum aims to help individuals gain essential skills in today's digital age, so they can efficiently store, process, and analyze data for making informed business decisions.

As part of the Big Data MicroMasters program, participants will not only develop their knowledge of big data analytics, but also enhance their programming and mathematical skills. The course covers essential analytic tools such as Apache Spark and R, while also discussing topics such as cloud-based big data analysis, predictive analytics, application of large-scale data analysis, and understanding the analysis of problem space and data needs. By the end of this course, students will be able to approach large-scale data science problems with creativity and initiative.

14. Graph Analytics for Big Data

This comprehensive course delves into the rapidly growing field of graph analytics, enabling you to understand the structure of data networks and how they change under varying conditions. As an integral part of modern business analytics, it sheds light on new methods of modeling, storing, retrieving, and analyzing graph-structured data. You will learn how to identify closely interacting clusters within a graph, gaining a broad and valuable perspective on the subject matter.

Upon completion of the course, you will be equipped with the skills to formulate a problem into a graph database and execute analytical tasks over the graph in a scalable, efficient manner. Furthermore, you will be able to apply these acquired techniques to comprehend the implications of your data sets, leading to valuable insights for your respective projects.

15. Big Data Fundamentals

This comprehensive course delves into how big data is driving organizational changes and the essential analytical tools and techniques utilized in the field, including data mining and PageRank algorithms. As organizations amass significant amounts of data, leveraging it for effective decision-making has become a critical aspect of their success. From this course, you will gain valuable insights into the ways big data has transformed a plethora of industries and how it solves complex challenges faced by organizations when handling massive datasets.

The course covers a wide range of fundamental techniques, such as data mining and stream processing, and teaches learners how to design and implement PageRank algorithms using MapReduce - a programming paradigm that enables massive scalability across numerous servers in a Hadoop cluster. Furthermore, participants will become acquainted with the ways big data has enhanced web search capabilities and the inner workings of online advertising systems. Upon completing this course, learners will possess a deeper understanding of the numerous applications of big data methods across various industries and research fields.

16. Big data Internship Program - Foundation

This comprehensive course serves as an introduction to big data and its applications, leading participants through the essential aspects of a big data project life cycle stage. By covering both technical and non-technical aspects of big data, the program ensures a well-rounded understanding of the subject matter. Students will have the opportunity to refresh their knowledge on Unix, explore Java in the context of big data, familiarize themselves with git and GitHub for source control, and learn about hadoop installation.

Additionally, the course emphasizes the significance of non-technical foundation in big data, providing insight into project life cycles, roles in big data implementation, and real-life examples of projects. Moreover, specific big data topics such as the Hadoop ecosystem, HDFS, MapReduce, and the role of Spark are explored in detail, equipping students with the necessary knowledge and skills to venture further into the field of big data and pursue advanced courses.

A small but important beginning into BigData. Tech made as simple as possible without jargon. Thanks for making tech concepts simple, engaging. Hope the further journey will be much more interesting and insightful. Covering end to end Technology components and Business aspects at one place and with a hands-on project deserved much greater appreciation. ^[6] ... Read More

17. SQL Masterclass for Data Analysis with BigData

This comprehensive course offers a deep dive into the world of SQL and mastering the art of database manipulation and data analysis. With a focus on both fundamental SQL techniques and advanced concepts, learners will develop the skills necessary to become in-demand SQL professionals. By engaging with various practical exercises from the globally renowned platform "HackerRank," students will enhance their problem-solving abilities and learn valuable tips and insights that typically come only from years of SQL experience.

Not only does this course cover SQL queries and various DDL and DML commands, but it also includes a section on connecting Python with MySQL, providing a versatile skill set useful for any aspiring Data Analyst or Data Scientist. Moreover, the course features a module on BigQuery, thereby enabling learners to apply their newfound SQL expertise to the analysis of Big Data. With dedicated instructor support available throughout, this SQL Masterclass serves as an excellent resource for anyone looking to advance their SQL knowledge and propel their career in the field of data analysis and database management.

Overall I really enjoyed this course and it helped me gain a good understanding of SQL. I also like that it introduced me to BigQuery and some other data visualization tools, and the SQL interview prep was incredibly helpful. I do think that adding in another lecture or two on nested queries would b e really helpful, as I was a bit lost on some of the later interview prep questions that needed them. Excellent course, and highly recommended for anyone who wants to learn more about SQL! ^[7] ... Read More

18. Big Data Hadoop and Spark with Scala

This comprehensive course is designed to equip students with a strong understanding of big data, focusing specifically on Hadoop and Spark with Scala. By taking this course, students will develop the skills needed to switch careers or enhance their current work in the field of big data. Throughout this program, students will delve into various topics including Hadoop, HDFS, YARN, MapReduce, Python, Pig, Hive, Oozie, Sqoop, Flume, HBase, NoSQL, Spark, Spark SQL, and Spark Streaming.

Designed to be a one-stop resource, this course provides all necessary materials and programs, ensuring students have a smooth learning experience. The instructor is committed to offering support and assistance for any inquiries that may arise during the course. With a focus on both the basics and advanced aspects of the Hadoop eco-system, as well as Spark, this course is perfect for individuals looking to expand their knowledge and excel in the world of big data.

This course is really very good for beginner, I liked his teaching methodology. He has uploaded recorded video lessons he took for some people (in past) and you can hear his students as well so for some it may not work…. But for me it was kind of being in the live course even though its actually sel f-paced. In this course ,he explains every little thing in detail and would not go ahead if his students did not understand or had any doubt which is really a plus point. He encouraged his students to participate actively in the discussion. And all the discussion makes it better to understand the topic and learn everything quickly. He asked his students to thoroughly comprehend the topic and describe it in their own words which makes it easier to revise it. He better knows how to conduct lessons and make the learner pick it quickly. I would highly recommend him. ^[8] ... Read More

19. Big Data Analytics with Hadoop and Apache Spark

The course focuses on the powerful combination of Apache Hadoop and Apache Spark to harness the full potential of big data analytics. As the importance of data-driven decision making continues to grow, the ability to effectively manage and analyze large datasets becomes increasingly valuable. This course provides an in-depth understanding of how to build scalable and optimized data analytics pipelines by utilizing the strengths of both Hadoop and Spark. Through the guidance of an experienced instructor, participants will explore topics such as data modeling and storage optimization on HDFS, as well as scalable data ingestion and extraction using Spark.

A hands-on approach is employed throughout the course as students are given the opportunity to work on a use case project, thereby allowing them to practice and apply the techniques they learn. With the skills developed in this course, students will be well-equipped to optimize data processing in Spark and unlock the true potential of big data in their professional endeavors. This course is ideal for data analysts, data engineers, and anyone seeking to enhance their data analysis capabilities by incorporating the powerful duo of Hadoop and Spark into their pipeline. As the demand for expertise in big data technologies continues to rise, mastering these tools can propel your career forward and create countless opportunities in the rapidly evolving field of data analytics.

Kumaran's knowledge and teaching style makes this stuff super easy to digest. Highly recommend this course and instructor for anyone looking to gain a deeper understanding on Big Data Analytics with Hadoop and Apache Spark! ^[9] ... Read More

20. Big Data, Genes, and Medicine

This course provides critical knowledge and skills obtained by experts in Health Big Data Science and Bioinformatics, including fascinating information about human biology, chemistry, genetics, and medicine. This knowledge will be correlated with the science of Big Data, teaching students how to harness the vast amount of data now available at their fingertips and make sense of it. Throughout the course, students will explore the various steps required to master Big Data analytics on real datasets, such as Next Generation Sequencing data, in a healthcare and biological context. Topics covered include preparing data for analysis, completing the analysis, interpreting the results, visualizing the data, and sharing the findings.

Upon completing the course, students will be well-equipped with in-demand skills that position them to pursue or advance in careers in biomedical data analytics and bioinformatics. This course caters to individuals with varying levels of expertise in biomedical or technical fields, providing valuable new or enhanced skills that will make them stand out as professionals and encourage further exploration of biomedical Big Data. Ultimately, this course aims to inspire students to leverage the vast potential offered by publicly available big data to improve disease understanding, prevention, and treatment.

I do like how in depth this was so that you can get a real taste of how some of this analysis is performed as well as the fundamentals behind it. Balancing all of the biology, informatics, statistics to an audience where you don't know everyone's backgrounds is very challenging but I think this cour se does a fairly good job of it. I do wish some of the answers were updated, as when you go through some of the analysis using the public tools they may have changed because certain answers I got were very slightly off (1-3) and I had thought it was my fault at first until I checked the forums, so that was annoying at worst. I liked all of the cbio portal lessons and the quizzes where we had to go into the code and change some numbers. The work in the jupyter notebooks felt overwhelming sometimes so I wish I all the codes were slightly more thoroughly explained, but otherwise a satisfying course. ^[10] ... Read More

21. Big Data and Hadoop for Beginners - with Hands-on!

The primary goal of this comprehensive course is to demystify Hadoop's complex architectures and components, guiding beginners in the right direction to quickly and effectively start working with Hadoop and related technologies. Covering everything a big data novice needs, this course delves into the big data market, various job roles, technology trends, the history of Hadoop, HDFS, Hadoop ecosystem, Hive, and Pig. With numerous hands-on examples and practical exercises, individuals will be well-equipped to launch their Hadoop journey.

The curriculum is divided into six sections, each focusing on pertinent topics, such as understanding big data job roles and salary trends, exploring Hadoop's architecture, working with Hive and Pig, designing data pipelines, and adopting modern data architecture like Data Lakes. Additionally, the course emphasizes the importance of real-life applications of Hadoop and its components through real-world use cases, allowing students to practice and learn design and optimization techniques using data from actual applications. By the end of the course, participants will have a solid understanding of big data and Hadoop fundamentals, preparing them for a successful career in the field.

Big Data introduction can be daunting with several new keywords and components that one needs to understand. But, this course very clearly explains to a beginner about the architecture and different tools that can be leveraged in a big data project. It also has indications on the scope of big data i n the industry, different roles one can perform in the big data space and also cover various commercial distributions of big data. Overall, a great course for a beginner to get started on the fundamentals of big data. Use Case is a bonus ! ^[11] ... Read More

22. Analyzing Big Data with SQL

This course offers an in-depth exploration of the SQL SELECT statement and its main clauses, with a specific focus on big data SQL engines such as Apache Hive and Apache Impala. Although the material is primarily directed towards these big data engines, much of the content can be applied to traditional RDBMs as well; the instructor takes the time to address any notable differences related to MySQL and PostgreSQL. By the end of the course, learners will be able to navigate databases and tables, grasp the basics of SELECT statements, filter results, apply grouping and aggregation for analytical queries, sort and limit results, and combine multiple tables in various ways.

In order to participate in the hands-on exercises and activities, students will need to download and install a virtual machine, as well as the corresponding software. It is essential that learners have access to a computer that meets specific hardware and software requirements, including a Windows, macOS, or Linux operating system, a 64-bit OS, at least 8 GB of RAM, a minimum of 25 GB free disk space, and enabled Intel VT-x or AMD-V virtualization support. Additionally, Windows XP users must have an unzip utility such as 7-Zip or WinZip installed, as the built-in utility is insufficient for this course.

This is a great course overall! Having taken a couple of SQL courses over the years, they are often quite dry. Somehow, this one was much better. I liked the conceptual aspects covered with the syntax & applications. Rounded the education out quite well. The only thing I would have liked to see mo re of is more practical assignments, like the one in Week 6. Even that was measured down in difficulty; perhaps the assignment can be made as part of the Honors section. Also, definitely one of the better monitored forums in Coursera. With regular/periodic/helpful replies from Staff. In contrast, I have generally found most Coursera courses to be quite poorly monitored comparatively. Its one of my bigger griefs with Coursera, compared to Edx - which generally has a vibrant discussion forum, thanks to the class members & staff/TAs ^[12] ... Read More

Compare Big data Online Courses

Course	Rating	Cost
Introduction to Big Data Best for introducing Hadoop and Big Data		7 days free trial Subscription: $33.25/m annually, $59/m monthly
Managing Big Data with MySQL Best for mastering big data with MySQL		7 days free trial Subscription: $33.25/m annually, $59/m monthly
Big Data and Hadoop Essentials Best for grasping Big Data essentials		Free
The Ultimate Hands-On Hadoop: Tame your Big Data! Best for practical Hadoop and ecosystem mastery		$24.99
Financial Forecasting with Big Data Best for financial forecasting using big data		1 month free trial Subscription: $19.99/m annually, $39.99/m monthly
Big Data Analysis with Scala and Spark Best for Scala and Spark big data analytics		7 days free trial Subscription: $33.25/m annually, $59/m monthly
Big Data Modeling and Management Systems Best for big data modeling and management		7 days free trial Subscription: $33.25/m annually, $59/m monthly
Big Data in the Age of AI Best for understanding big data's role		1 month free trial Subscription: $19.99/m annually, $39.99/m monthly
Knowledge Management and Big Data in Business Best for knowledge management in business		$169 or free without certificate
Big Data Integration and Processing Best for hands-on big data processing		7 days free trial Subscription: $33.25/m annually, $59/m monthly
Machine Learning With Big Data Best for machine learning with big data		7 days free trial Subscription: $33.25/m annually, $59/m monthly
Big Data Analytics Using Spark Best for Spark-based big data analysis		$350 or free without certificate
Big Data Analytics Best for cloud-based big data solutions		$199 or free without certificate
Graph Analytics for Big Data Best for graph analytics in big data		7 days free trial Subscription: $33.25/m annually, $59/m monthly
Big Data Fundamentals Best for big data fundamentals and applications		$199 or free without certificate
Big data Internship Program - Foundation Best for comprehensive big data introduction		Free
SQL Masterclass for Data Analysis with BigData Best for SQL mastery in data analysis		$19.99
Big Data Hadoop and Spark with Scala Best for Hadoop, Spark, and Scala expertise		$69.9
Big Data Analytics with Hadoop and Apache Spark Best for Hadoop and Spark analytics		1 month free trial Subscription: $19.99/m annually, $39.99/m monthly
Big Data, Genes, and Medicine Best for healthcare-focused data analytics		7 days free trial Subscription: $33.25/m annually, $59/m monthly
Big Data and Hadoop for Beginners - with Hands-on! Best for beginner-friendly Hadoop learning		$14.99
Analyzing Big Data with SQL Best for SQL-based big data analysis		7 days free trial Subscription: $33.25/m annually, $59/m monthly

How to choose best Big data online course

When it comes to selecting the best big data course for your needs, there are several key factors to consider in order to make an informed decision. Whether you are looking to enhance your career or gain valuable insights for your business, it's crucial to choose a course that aligns with your goals and objectives. This learner guide will help you navigate the big data course landscape by highlighting important elements to look for during your search.

1. Relevance to your learning objectives: With such a broad spectrum of big data courses available, assess how closely they align with your specific goals, such as understanding big data fundamentals or gaining hands-on experience with Hadoop.
2. Instructor expertise: Ensure that the instructor has a proven track record in the industry with relevant experience and knowledge to impart on students.
3. Reviews and recommendations: Check for testimonials and reviews from peers, as well as any professional recommendations, to gain insight into the quality and credibility of the course.
4. Course structure and coverage: Compare the curriculum, duration, and content delivery of the course to determine if it will suit your learning preferences and timeline. In particular, look for courses that offer hands-on exercises, real-life examples, and practical applications of the theoretical concepts.
5. Flexibility and accessibility: Choose a course that provides the flexibility to learn at your own pace, as well as access to content and materials after course completion, to enhance your learning experience.
6. Certification and recognition: Consider courses that offer certification upon completion, as this may provide a competitive edge and increase your professional credibility in the field of big data.

By carefully considering these factors, you are well-equipped to make an informed decision when selecting the most suitable big data course that caters to your needs and professional growth. Remember, the investment you make in your education is invaluable, and selecting the right course can make all the difference in realizing your big data aspirations.

Conclusion

In conclusion, our comprehensive list of the best big data courses provides an expansive selection for those seeking to enhance their knowledge and skills in this rapidly growing field. We carefully curated these offerings to ensure high-quality content and instruction, thereby enabling learners to achieve their objectives, whether they seek certifications, hands-on experience, or a robust understanding of big data essentials and applications.

Motivated individuals who are eager to embrace the world of big data can confidently choose from the courses listed above to launch or further their careers. By investing in these curated educational opportunities, professionals can stay ahead of industry trends, gain valuable insights, and develop the expertise needed to excel in today's data-driven business climate. Take the first step in exploring big data courses and certifications, and propel yourself toward a successful and rewarding career in this essential domain.