Yes folks we have our ear to the ground. Many of you embarking on a career in Data Analytics and Big Data, are sometimes confused and unsure about which of these courses to do. Well, a lot would depend on your career goals, as well as your competencies. To help you better understand the difference between these courses, our in-house Big Data expert Kiran P.V has taken the time to list out what each of these courses entails and even goes further to explain which course would better suit your individual career aspirations.
Many IT experts around the globe would agree that we live in the age of Big Data. Data Science and Big Data are the two terms commonly referenced in all literature while discussing the potential benefits of enabling data-driven decision making. Importantly these latest trends are creating new job opportunities and the demand for people with the right set of data skills is on the rise. In order to meet the growing need for Big Data and Data Science talent, we are witnessing the emergence of training programs across worldwide universities, MOOCs, and other niche analytics institutes. At Jigsaw Academy, we have specially created Data Science and Big Data courses with the help of industry experts to guide aspiring students and working professionals to pursue successful careers in a fascinating data world.
Though these courses fall under the broad category of the data analytics field, some major differences exist between them in terms of the technologies involved and the vast possibilities of end applications. The Data Science course involves the execution of different phases of analytics projects such as data manipulation, visualization, and predictive model building using R software. This course also provides training on general programming with R, using in-built data objects, and also on writing custom functions and programs.
On the other hand, the Big Data course majorly deals with processing and analyzing massive amounts of data using Hadoop technology. Traditional database systems fall short in dealing with Big Data effectively and thus the adoption of NoSQL-based systems such as Hadoop and others across many industry verticals is increasing. Apart from providing both theoretical and hands-on aspects of working with Hadoop, this course also covers performing data analysis using software such as R and Tableau. One other key module of the Big Data course would be on integration of R and Tableau with Hadoop cluster to make the best of both worlds. In Hadoop infrastructure enables smooth handling of big data whereas R and Tableau inbuilt functions help in generating insights from data through summary statistics, dashboards, and visualizations.
In the next sections, I will discuss in more detail some of the key differences between Data Science and Big Data courses in terms of tool exposure, coverage of topics related to statistics and advanced analytics. Additionally, various aspects related to the course choice in terms of career fit will be discussed including comparisons of the existing Big Data course offered by EMC and Cloudera Hadoop certification. Also want to understand what is the difference between Big Data vs Data Science, we have a relevant article.
To better understand the differences between these courses, one should try to look at some of the key dimensions such as the kind of tools and technologies that can be learned and the extent of big data concepts that will be covered in each of them. Building a comprehensive working knowledge and expertise around various analytical and database tools is a key step to excel in Big Data and Data Science fields.
The Data Science course is entirely taught in R software which is an open-source statistical programming language and one of the essential tools that are a part of any Data Scientist’s Tool Kit. Due to its extensive package repository around statistical and analytics applications, R is tremendously growing in popularity around the world and many firms are on the lookout for R programmers.
Take a look at what some of our students have to say about the Data Science course.
On the other hand Jigsaw’s Big Data course provides extensive training on Hadoop and its components such as Hive, HBase, Sqoop, and Flume to process and analyze large amounts of data. This course also covers installation aspects of Hadoop along with its components and trains students on Java-based MapReduce programming. Apart from Hadoop concepts, the Big Data course also contains training modules on the integration of R and Tableau software’s with Hadoop cluster using RHadoop library and Tableau-Hadoop connectors, to perform data analysis tasks and further generate dashboards and visualizations.
Find out more about the topics covered in the 5 modules of the Big Data Training using Hadoop course.
Statistics and advanced analytics techniques knowledge are crucial for implementing successful data analytics projects. The Data Science course covers these topics in a comprehensive manner with applications of R programming. Typically an analytics project consists of various phases such as manipulation, preparation, exploration, and visualization of different kinds of business data. Along with training modules on these phases, predictive analytics techniques like regression models, clustering, and decision trees are covered using real-time case studies. Additional training modules around time series techniques and text analytics are also covered which helps in processing specific kinds of data such as text and social media content.
In the Big Data course, the emphasis will be more on handling and analyzing huge volumes of data to generate insights through summarization and visualization techniques. Instead of advanced analytics techniques, this course puts more emphasis on BI aspects such as exploratory analysis, building dashboards, and visualizations. Since Big Data technologies like Hadoop is a complex system compared to traditional SQL based systems, most of the learning modules will focus on data handling and processing using various components of Hadoop ecosystem such as MapReduce programming using Java, querying using HiveQL or scripting using Pig.
Since Big Data skills are a hot skill to have now and every business is actively looking out for the right talent, both the Big Data and Data Science courses consist of learning modules specific to working with Hadoop. The Data Science course provides an overview of Hadoop technology and writing MapReduce programs through R and Hadoop integration using RHadoop library. These libraries are designed and developed by Revolution Analytics majorly for R programmers who can interact with the Hadoop cluster through R syntax whenever such a need occurs. On the other hand, the Big Data course is all about Hadoop data processing and further how one can integrate tools such as R and Tableau with Hadoop for performing data analysis. More than half of the learning modules provide both technical and hands-on knowledge related to configuration and data processing using Hadoop and its various components such as HBase, Hive, Flume, and Sqoop.
Regards the case studies dealt with in the courses, both the Big Data and Data Science courses differ in terms of end applications, with the former focusing more on massive datasets and the latter focusing more on predictive analytics problems. In the Data Science course, the concepts of predictive analytics techniques using R software across industry verticals such as retail, finance, and telecom are used. Some of the examples of these business problems are a prediction of telecom churn, the sale price of cars, credit risk behavior, and marketing mix modeling. Even in the Big Data course, the emphasis would be more on analytics-related problems across various domains such as exploratory analyses and visualizations along with installation and configuration aspects of the Hadoop cluster. On the analytics side, text mining would be covered extensively as Hadoop technologies are more popular in dealing with unstructured data problems. Some of the problems that will be covered are social media analytics with Twitter data, web analytics on clickstream data, and financial analysis using stock data.
In terms of career fit, the Data Science course would be beneficial for those who want to learn extensive R programming to use it for executing analytics projects, whereas the Big Data course is for those who are looking at building Hadoop expertise and further using it in collaboration with R and Tableau for performing standard data analysis tasks and building dashboards. If you are looking to build stronger expertise around implementing statistical and predictive analytics techniques then the Data Science course would be the right choice whereas the Big Data course would benefit those looking to become competent in processing data using Hadoop and also work with R and Tableau to create BI reports and dashboards.
Generally, the Data Science course would best suit professionals working as data analysts, business intelligence engineers, business analysts, and IT application engineers who want to build advanced skills for making a successful career in the data analytics field. Even the Big Data course would be a good fit for these professionals if they are planning on building big data skills around working with Hadoop and move ahead in their existing career paths. Alternatively someone working as a database administrator, or IT software and application engineer, or data warehousing professional can do the Big Data course to learn about big data technologies and also understand the concepts around working with Hadoop using analytics software’s such as R and Tableau and to develop a comprehensive data product or data flow meeting the end-to-end business needs. On the other hand, final year engineering or MBA students planning to learn about predictive analytics can consider taking up a Data Science course and if the focus is more on big data skills around Hadoop, then a Big Data course would be a better option.
EMC offers Data Science and Big Data Analytics course for working professionals and students to provide relevant skills that are needed to meet the big data challenges faced by businesses worldwide. This course covers topics such as an overview of big data technologies, introduction to analytics, R programming, working with Hadoop, machine learning algorithms, and big data solution engineering. After completing this course, one can also take up the certification exam for Data Science Associate offered by EMC. In comparison with the EMC course, Jigsaw’s Big Data and Data Science courses together cover all the topics but these provide more depth in terms of content, use cases, and assignments. Additionally, the virtual lab capabilities provided as part of Video led EMC course is limited whereas Jigsaw offers 24×7 lab access for students to practice on sample problems and case studies throughout the entire course. EMC also has an Instructor-Led course for the same topics which is priced at $5,000 that is completely taught by the best of the industry experts, but the important question is whether or not one should spend so much when there are good alternatives to build a similar kind of skills.
Cloudera Certified Developer for Apache Hadoop (CCDH) and Cloudera Certified Administrator for Apache Hadoop (CCAH) are the two most popular Hadoop certifications offered by Cloudera across the world. As Hadoop is one of the key skills required to become a big data professional, these certifications hold a lot of value and provide expert credibility when one is looking for big data jobs. One of the questions often posed by our prospective students is whether the Big Data course offered by Jigsaw would help to complete Cloudera Hadoop certification successfully. The answer to this would be yes as the content being covered in the Big Data course has about 70% overlap with Cloudera certification requirements. Jigsaw’s Big Data course is more designed on the objective of analyzing data sitting in the Hadoop cluster using analytics tools such as R and Tableau. In order to extract maximum value and insights from big data, the analysts also need to have a good understanding of the Hadoop ecosystem from an IT perspective and should have an in-depth understanding of various Hadoop components. So someone taking up the Big Data course will get a good amount of theoretical and hands-on exposure to installation, configuration, and processing of data using Hadoop and its components. However, few topics on managing a Hadoop cluster and streamlining MapReduce workflows are not covered in great detail as they are more associated with Hadoop database management concepts. Overall, one can surely take up the Cloudera Hadoop certification exam after completing the Jigsaw’s Big Data course and is willing to put in some effort to cover additional topics. If you have more questions or need to talk with our faculty, connect with us at firstname.lastname@example.org or call us at +91 9243522277, +91 9008017000.
Take a look at our Data Scientist Course & Big Data Course :