This is a guest post by Danish Wadhwa.

The IT sector is awash with data. This means more data is available to us than we can use. Data science is the field related to the development of insights from large sets of unstructured data. Its practical significance lies in the generation and implementation of information the right way. Today, data science techniques are widely used to streamline information models of a dynamic environment that depend on real-time sensor data. When organizations get these information models, they turn them into tailored services to offer to users. Who does this job? From data fetching to organizing to delivering insights, the whole process is accomplished by a data scientist.

Who are data scientists?

Data scientists are data wranglers who deal with vast amounts of data, structured or unstructured and discover insights which help businesses in improved decision making. Their role in data analysis is vital as organizations depend completely on big data and predictive analytics for efficient decision-making. Additionally, most organizations rely on technology, deep learning, machine learning, and artificial intelligence as fundamental parts of their IT strategies.

Unfortunately, the demand for data scientists is much higher than the supply of qualified data scientists. For this reason, companies ensure that their employees enroll themselves in online university courses like the Duke’s Big Data Certification Course. With this course, companies can meet the demand for data scientists while also upskilling their employees.

The role of a data scientist is to manage and analyze different datasets with the help of software designed for the task. The results of a data scientist’s analysis should be easy to understand for all concerned stakeholders — particularly for nontechnical people. Organizations need big data to help improve their processes and profits. If the people who have to make data-driven decisions can’t understand the data, the point of big data is defeated.

A data scientist’s approach to data analysis is not only -specific but also depends on the requirements of the business they work for. Before a data scientist begins his or her analysis of structured and unstructured data, other business stakeholders like executives and managers need to notify their requirements. Furthermore, a data scientist should carry good business domain expertise to turn a company’s goals into data-based deliverables like optimization algorithms, prediction engines, pattern detection analysis, and many more.

Is a Master’s Degree or a Ph.D. important to learn data science?     

Though it is not a prerequisite to getting started in a data science career, a master’s data science degree or a Ph.D. is one way to go in developing a technical data science skill set. Not having a highly quantitative degree should not stop you from understanding data science. A good number of people learn data science without a Master’s degree. Experienced individuals who have practical knowledge of data and technology have no use for a Master’s or a Ph.D. in the data science landscape. Actual data science experience always takes precedence over a Master’s degree or a Ph.D. because getting a Ph.D. can take a lot of time.

Of course, that’s not to say that a Ph.D. is unimportant. A Ph.D. does matter when you apply for a job or when you want to dig deep into the subject of big data. However, for most companies, it isn’t compulsory. If you are looking to apply for senior data scientist position in Google, then the Ph.D. is your best friend, but other companies could do without it.

Are SQL skills necessary to learn data science?

Often a data scientist’s time is taken up by writing SQL or associated scripts. If you want to learn the art of data science, you should have familiarity with how to write a basic SQL query and knowledge of joins, group by, creating indexes, etc. Being a database administrator is not important to become a data scientist; with basic SQL knowledge, you would be able to mine data for analysis. The SQL language layer is always present, irrespective of whether the data is to be recovered from a database or a Hadoop cluster.

What will you learn in a data science program?

When you enroll in a data science program, you will acquire knowledge in various verticals like informatics, mathematics, statistics and data analysis. Moreover,  you will dig deep into these subjects and learn to evaluate real-world problems associated with complex and big data during your data science education. You get to understand an array of application areas of big data in the several areas, including environmental and life sciences, technology, business, and economics.

What can you use this qualification for?

When you are a Data Science expert, you become a valuable asset in the labor market. Big Data challenges are present everywhere – in society, sectors, and research.  Thanks to this growing need, there are several takers for data scientists, like the energy sector, precision agriculture, the communication sector, economy, the construction sector, and the medical sector.

Conclusion:

Big Data benefits everyone. Companies improve their profits, business processes, and efficiency with big data. For big data professionals, specializing in this domain means better jobs and a higher paycheck. There hasn’t been a better time than now to jump into the big data wagon and the best way for professionals to do that is by making use of the multitudes of online certifications out there.

 



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