In the world of data space, the era of Big Data emerged when organizations are dealing with petabytes and exabytes of data. It became very tough for industries for the storage of data until 2010. Now when the popular frameworks like Hadoop and others solved the problem of storage, the focus is on processing the data. And here Data Science plays a big role. Nowadays the growth of data science has been increased in various ways and one should be ready for the future by learning what data science is and how can we add value to it.
Data science means different things for different people, but at its gist, data science is using data to answer questions. This definition is a moderately broad definition, and that’s because one must say data science is a moderately broad field!
Data science is the science of analyzing raw data using statistics and machine learning techniques with the purpose of drawing conclusions about that information.
So after knowing what data science is and the key pillars of data science, but something else we need to talk about is who precisely a data scientist is? An Economist Special Report says that a data scientist is defined as someone:
“who integrates the skills of software programmer, statistician and storyteller slash artist to extract the nuggets of gold hidden under mountains of data”
But in reality, there are many questions that arise. Some important questions are: what’s the role of a data scientist? What’s the responsibility of a data scientist? How data scientists are different from data analysts and data engineers? So let’s discuss these types of questions to understand who is a data scientist in detail?
Roles & Responsibilities of a Data Scientist
- Management: The Data Scientist plays an insignificant managerial role where he supports the construction of the base of futuristic and technical abilities within the Data and Analytics field in order to assist various planned and continuing data analytics projects.
- Analytics: The Data Scientist represents a scientific role where he plans, implements, and assesses high-level statistical models and strategies for application in the business’s most complex issues. The Data Scientist develops econometric and statistical models for various problems including projections, classification, clustering, pattern analysis, sampling, simulations, and so forth.
- Strategy/Design: The Data Scientist performs a vital role in the advancement of innovative strategies to understand the business’s consumer trends and management as well as ways to solve difficult business problems, for instance, the optimization of product fulfillment and entire profit.
- Collaboration: The role of the Data Scientist is not a solitary role and in this position, he collaborates with superior data scientists to communicate obstacles and findings to relevant stakeholders in an effort to enhance drive business performance and decision-making.
- Knowledge: The Data Scientist also takes leadership to explore different technologies and tools with the vision of creating innovative data-driven insights for the business at the most agile pace feasible. In this situation, the Data Scientist also uses initiative in assessing and utilizing new and enhanced data science methods for the business, which he delivers to senior management of approval.
- Other Duties: A Data Scientist also performs related tasks and tasks as assigned by the Senior Data Scientist, Head of Data Science, Chief Data Officer, or the Employer.
Difference Between Data Scientist, Data Analyst, and Data Engineer
Data Scientist, Data Engineer, and Data Analyst are the three most common careers in data science. So let’s understand who’s data science by comparing it with its similar jobs. Data Scientist Data Analyst Data Engineer
The focus will be on the futuristic display of data. The main focus of a data analyst is on optimization of scenarios, for example how an employee can enhance the company’s product growth. Data Engineers focus on optimization techniques and the construction of data in a conventional manner. The purpose of a data engineer is continuously advancing data consumption. Data scientists present both supervised and unsupervised learning of data, say regression and classification of data, Neural networks, etc. Data formation and cleaning of raw data, interpreting and visualization of data to perform the analysis and to perform the technical summary of data. Frequently data engineers operate at the back end. Optimized machine learning algorithms were used for keeping data and making data to be prepared most accurately. Skills required for Data Scientist are Python, R, SQL, Pig, SAS, Apache Hadoop, Java, Perl, Spark. Skills required for Data Analyst are Python, R, SQL, SAS. Skills required for Data Engineer are MapReduce, Hive, Pig Hadoop, techniques.
Some Inspiring Data Scientists
The variety of areas in which data science is used is embodied by looking at examples of data scientists.
- Hilary Mason: She is the co-founder of Fast Forward labs, a machine learning company recently owned by Cloudera, a data science company. She is a Data Scientist at Accel. Broadly, she works with data to solve questions about mining the web and also learning the method that how people communicate with each other through social media.
- Nate Silver: He is one of the most prominent data scientists or statisticians in the world today. He is the founder of FiveThirtyEight. FiveThirtyEight is a website that applies statistical analysis to tell compelling stories about elections, politics, sports, science, and lifestyle. He utilizes huge amounts of public data to predict a diversity of topics; most prominently he predicts who will win elections in the U.S. and has an extraordinary track record for accuracy in doing so.
- Daryl Morey: He is the general manager of a US basketball team, the Houston Rockets. He was awarded the job as GM based on his bachelor’s degree in computer science and his M.B.A. from M.I.T.