7 Careers in Big Data and Data Science

Companies create an ocean of data every year. The true value of that data lies in how to we see, collect and preserve it. The data revolution happening around us is helping industries from business to government, health care to academia. Businesses have realized the criticality of "data" and woken up to the fact that long-term sustainability is not possible without effectively analyzing the huge amounts of data at their disposal.

In the current scenario, the technology arena alone accounts for 41% of the jobs in Data Science. But it is equally significant to other areas too, including marketing, corporate, consulting, healthcare, financial services, government and gaming. If you are trying to break into the field or planning to make a career switch, Big Data and Data Science could be just the type of career you have been trying to find. According to Glassdoor, 5 out of the top 10 US jobs are related to Analytics, Big Data and Data Science. The salary figures ($116,000) for experienced professionals and ($92,000) freshers are undoubtedly amusing.

Top Big Data and Data Science Profiles

1. Data Scientist: Data Scientists are extremely scarce and the demand is huge. They use their analytical and technical capabilities to convert data-based scientific inference into accessible, actionable insights for business and upper level management.

Skillset Required: Expertise in Machine Learning, Hive, Python, SQL, MatLab, R, SAS and Spark. Higher degrees in quantitative subjects like statistics and mathematics and proficiency in Big Data technologies and analytical tools is typically expected from professionals in this role.

2. Data Engineer/ Data Architect: They are computer engineers responsible for the uninterrupted flow of data between servers and applications. Data Engineers/ Data Architects are the real designers, builders and managers of the information or "big data" infrastructure, and maintain data architectures.

Skillset Required: Expected to be an expert in SQL, R, Python, Ruby, C++, Perl, Java, SAS, SPSS and Matlab. They should also have knowledge about Pig, Hadoop, MapReduce, Hive, MySQL, Cassandra, MongoDB, NoSQL, Data streaming and programming.

3. Data Analyst: Often called the junior data scientists, they job role is vast and crucial. They analyze gathered data in order to tell a meaningful story while focusing on producing actionable insights. Analysis is mostly done based on charts, models and visualizations, which help businesses make important business decisions.

Skillset Required: Knowledge of skills like R, Python, HTML, SQL, C++ and JavaScript. They should have more than just the understanding of business intelligence concepts, Hadoop-based analytics, data visualization and data warehousing using ETL tools, and data retrieval and storing systems.

4. Machine Learning (ML) Engineer: The demand for ML Engineers is going to keep increasing exponentially. What separates a Machine Learning Engineer from a Data Analyst is the end goal. Here, the final “output” is the working software, and their “audience” for this output consists of other software components that run autonomously with minimal human supervision.

Skillset Required: Focus on Python, Java, Scala, C++ and Javascript. Proficient understanding of the machine learning concepts, know how to build highly-scalable distributed systems, familiarity with big datasets. Such professionals usually have a strong foundation in mathematics and statistics.

5. Business Intelligence (BI) Engineer: BI Engineers are individuals with BI analysts and customers to turn data into critical information and knowledge that is then used to make intelligent business decisions.

Skillset Required: Should have expertise at data analysis and experience at setting up reporting tools, querying and maintaining data warehouses. Proficiency with Big Data concepts and ability to take a data driven approach to solve complications.

6. Big Data Engineer: Big Data Engineers builds what a big data solutions architect designs. They develop, maintain, test and evaluation big data solutions within enterprises.

Skillset Required: Must-be experienced with Hadoop based technologies such as MapReduce, Hive MongoDB or Cassandra. They are expected to work with the latest (NoSQL) database technologies. Experience with object-oriented design, coding and testing patterns as well as experience in engineering (commercial or open source) software platforms and large-scale data infrastructures is also required for efficiency.

7. Statistician: Statisticians are responsible for collecting, organizing, presenting, analyzing and interpreting data, which help businesses make intelligent decisions by spotting trends and making predictions.

Skillset Required: These professionals are required to possess higher degrees in statistics, mathematics, or any quantitative subject. They should be well-equipped in Python, Stata, Pig, Hive, SQL, Perl, MATLAB, SAS and R programming.

Increasing Demand & Unfilled Skill Gap

Data Science and Big Data are attracting a lot of young professionals with the field's sterling career prospects. But, at the same time, there is still a lot of confusion among the folks about how to break into “the field” and move forward in their careers. As companies are constantly looking for ways to exploit the power of Big Data, the demand for quality data professionals is going up every passing year in the industry. The current situation suggests an extreme scarcity on the supply side. A significant number of the job openings across the globe go unfilled due to shortage of required skillset in data professionals.

As of now, only 0.5% of the data at disposal is ever analyzed or utilized according to a research from MIT Technology Review.

As the job opportunities in Big Data and Data Science are zooming, this is a no-brainer: Big Data means big bucks. Acknowledging the fact, many IT professionals are convinced to invest time and money for the training.

Discover your IT skills learning marketplace

Data Science and Big Data are changing as technology is advancing. QuickStart helps data professionals stay up to date with the changing technology. Dedicated to transforming career growth & IT project performance, QuickStart is a pioneer in providing Big Data and Data Science online training and certification courses.

The Data Science bootcamp specialization training courses are designed by industry experts to provide in-depth learning on diverse Data Science concepts. These Data Science training courses at QuickStart can be taken by experienced data professionals as well as graduates and undergraduates eager to learn the latest Big Data technology.

QuickStart helps professionals make a sterling Data Science career without having to rely on the burgeoning and exorbitantly priced degrees. Professionals can enroll in a self-paced learning or virtual instructor-led course, wherein they have access to a massive library boasting of 100,000+ hours of in-depth, industry leading knowledge and topics. The personalizing learning experience through online courses, instructor lead training, high impact mentoring and hands-on lab experience in various areas of Data Science make QuickStart a one-stop solution for Data Science certification training.

Moreover, QuickStart is a lifelong learning solution where you may retake any public course in the future, without any additional charge. You also get substantial discounts on courses that have been upgraded within 9 months from the date of enrollment. The certified Data Science professionals from QuickStart have been hired and employed by top technology giants in the industry for multiple job roles.