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Career Advice: Is Big Data the Right Fit for You?

Every day humans are generating and storing massive amounts of information. There are important secrets to be harvested from those ever-growing stockpiles. If you think that sounds exciting, then a career in data science may suit you.

Big Data concept drowning in documentsPer Google’s Eric Schmidt, mankind created five exabytes of information between the dawn of civilization and 2003, “but that much information is now created every two days.” To be sure, these mountains of information are impressive — in order to also be useful, however, they need to be interpreted. Or as Peter Sondergaard of Gartner Research put it, “Information is the oil of the 21st century, and analytics is the combustion engine.”


This vast accumulation of information is called “Big Data,” and it refers to data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Data scientists are those highly-skilled IT specialists trained to analyse these large data sets to reveal patterns, trends and associations especially as it relates to consumer behaviour.


A successful data scientist can expect to be paid well for his or her knowledge and skills. Before one can be called a data scientist, however, they must complete adequate training in big data.


Big Data Training


Big Data training is all about capturing data and studying it — essentially becoming an expert in analysing data on behavior that are predictive as well as user- and value-based. A course in data science should provide candidates a thorough understanding of the 5Vs that define and create a path for data – volume, velocity, variety, variability, and veracity.”


Additionally, an in-depth course should focus on developing and understanding the role a data scientist plays, realizing stakeholder core deliverables, industry verticals and their respective data analyses, the planning and constructing of analytic models, creating final deliverables for measuring success, and communicating results.




Courses range from beginner- to expert-level and vary according to the sponsoring institution. Some of the more respected courses on the market are (broadly speaking, in some cases) labeled as follows:


● Apache Hadoop
● Apache Hadoop Advanced
● Hadoop Ecosystem Essentials
● Programming with R
● Oracle NoSQL Database
● Data Science as a Career
● Data Science Expert
● Microsoft Data Science
● Data Science and Big Data Analytics


While completing any course will help you in your career, in order to become a valued expert you will need to complete at least one expert-level course and have actual experience working with large data sets.


Software engineers who want to break into the Big Data arena will benefit from an integrated program that includes training in Big Data as well as data science. Several courses that are strongly suggested include:


● Data Science Certification
● Big Data Hadoop and Spark Developer
● Tableau Desktop 10 Qualified Associate Training
● Data Science with Python


There are also a number of more tangential courses that can prove valuable as you get started in big data. These include:


● Data Science with SAS Trainer
● Big Data and Hadoop Administrator
● Impala Training
● Apache Cassandra
● MongoDB Developer and Administrator
● Apache Kafka
● Apache Spark and Scala
● Apache Storm
● Business Analytics with Excel


Many Big Data courses are surprisingly brief, often requiring a commitment of three or fewer months. Additionally, while there are no definitive educational qualifications for becoming a data scientist, a great many hold master’s degrees, and even doctorate degrees, in engineering, information technology and mathematics.


An advanced degree in any of those disciplines, or in other closely related fields, is a definite plus for candidates seeking to excel in this Big Data. While specific courses may have prerequisites, individuals who become data scientists do tend to have strong backgrounds and experience in the following areas:


● Mathematics
● Statistical methods
● Quantitative background with proficiency in the statistical software basics
● Basic programming
● Experience with data conditioning
● Experience managing data for a business which can include SQL a programming language and databases