The world of business includes organizations operating in several different verticals. From automobiles to healthcare to banking (and many more!), big and small firms operate across the world. And given the myriad aspects of the operations of any company – sales, inventory, running expenses, employee hours, dispatches, and more – there is a massive amount of data flowing in every day. This is what gave rise to the term ‘big data’.
What is big data?
According to Investopedia.com, big data is “the large, diverse sets of information that grow at ever-increasing rates”. This includes the following aspects:
- The size of the information flowing in
- The pace of its creation and inflow
- The diversity of the data types and formats
- Variety: data from sources outside and within the organization
- Velocity: rapid incoming pace, continually supplied by machines, mobile devices, networks, and social media
- Veracity: data that is inconsistent, has duplicates, and other uncertainties due to a diversity of sources
- Volume: massive quantities of data
- Value: comes only when the data is analyzed to uncover insights useful for the organization in taking strategic decisions
Among the careers in big data, three of the most common roles are a data analyst, a data engineer, and a data scientist.
A data analyst looks to answer questions for the business by perusing past and current data to generate reports, both on a regular and an ad-hoc basis. He or she typically has studied one among science, technology, engineering, and mathematics (STEM). Strength areas include mathematics, statistics, and big data, and some of the important skills required for the role are coding, extract-transform-load (ETL) tools, and SQL.
A data engineer is responsible for building, testing, and maintaining a complete data ecosystem. These ecosystems are integral to the work of data scientists, who use prediction algorithms based on the data. He or she must be strong with SQL and NoSQL database systems, ETL solutions, data warehousing, coding (in Python, Scala, or Java), and big data tools and Hadoop-based technologies.
A data scientist is a big data professional who takes data, spending time on analysis and interpretation with the goal of facilitating data-driven decision-making for businesses. Such a person typically holds a degree in science-based areas and is an expert in linear algebra, multivariate calculus, or other forms of complex mathematics.
What is the best way to get the right skills for these roles?
Typically, degrees in the STEM subjects are the main criteria for careers in big data. However, a degree is a one-time qualification that may not help the candidate keep pace with evolving technologies and other knowhow in the field.
What a candidate could do is to pick one of the best big data certifications. These are a great way to show a potential employer that the candidate possesses the latest skills and knowhow in the field, and is serious about growing in a career and investing in continuous learning.
One of the top institutions providing such certifications is the Data Science Council of America (DASCA). DASCA researches, designs, and builds platform-independent data science knowledge frameworks, standards, and credentials. DASCA tests individuals along the world's most robust generic data science knowledge framework – the DASCA-EKF™ – to validate their knowledge in 30 profession–critical dimensions, before awarding its certification. DASCA credentials are available in 183 countries, and include complete exam preparation kits, with each credential involving an online 100-minute examination.
What DASCA certifications are available for data analysts?
The following options are available:
Associate Big Data Analyst (ABDA™): This is considered the foremost qualification to start a big data career. The target audience for this credential includes graduating students with majors in business, management, economics, statistics, or allied disciplines, and those who are planning an analytics career in the big data space. Individuals intending to undergo the program should have a prior, strong formal exposure and knowledge of the basic concepts in Statistics, and are hand–on with the tools and techniques of handling problems.
Senior Big Data Analyst (SBDA™): Candidates include experienced analytics, marketing, and research professionals intending to either move into big data or those already working with big data, and looking to grow faster. The existing skill and concept requirements are similar to those for the ABDA™.
What DASCA certifications are available for data engineers?
The following are two of the best big data certifications available:
Associate Big Data Engineer (ABDE™): This is a great choice for graduating tech–school engineering students readying to start their careers in software engineering with the big data industry. Candidates must have a prior, strong formal exposure and knowledge of basic programming concepts and should be hands–on with tools and techniques of object-oriented programming, basics of scripting languages, and a good understanding of Linux and Unix environments.
Senior Big Data Engineer (SBDE™): This is designed for experienced programmers and software engineers who intend to move into the big data space or want to grow faster in their big data development careers. The existing skill and concept requirements are similar to those for the ABDE™.
What DASCA certifications are available for data scientists?
For an aspiring data scientist, DASCA offers the Senior Data Scientist (SDS™) certification. This is targeted at professionals with at least six years in research and analytics, and an undergraduate degree in applied mathematics, business, economics, management, marketing, mathematics, or statistics. The most powerful 3rd–party, vendor–neutral certification in the world, SDS™ requires knowledge of basic concepts of statistics, advanced mathematical and statistical operations, as well as experience in handling databases, spreadsheets, statistical analysis, analytics platforms, quantitative methods, and object-oriented programming and RDBMS.
