A Data Scientist is a person who assumes multiple roles over the course of a day. He/she is a Software Engineer, Data Analyst, Troubleshooter, Data Miner, Business Communicator, Manager, and a key Stakeholder in any data-driven enterprise and helps in decision-making at the highest levels.
- Updated on: 02nd Jan, 18
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A Data Scientist is a professional who extensively works with Big Data in order to derive valuable business insights from it. Over the course of a day, the Data Scientist has to assume many roles: a mathematician, an analyst, a computer scientist, and a trend spotter.
Check out this Data Scientist vs Data Analyst vs Data Engineer video:
Comparing Data Scientists with Data Engineers
|Criteria||Data Scientists||Data Engineers|
|Mostly work with||Statistics and Data Analysis||Databases and ETL|
|Common tools used||R and SAS||MySQL and Hive|
|The language used||Python||Java|
Some of the tasks of a Data Scientist are:
- Collecting large amounts of data and analyzing it
- Using data-driven techniques for solving business problems
- Communicating the results to business and IT leaders
- Spotting trends, patterns, and relationships within data
- Converting data into compelling visualizations
- Working with Artificial Intelligence and Machine Learning techniques
- Deploying text analytics and data preparation
Some of the technologies and skills that a Data Scientist works with:
- Programming skills in Java, Python, R, and SQL
- Reporting and data visualization techniques
- Big Data Hadoop and its various tools
- Data mining for knowledge discovery and exploration
- Communication and interpersonal skills
What does a Data Scientist do?
Day-to-day activities of a Data Scientist sometimes can be predictable, and sometimes they are something out of the ordinary. Requirements for becoming a Data Scientist are many. If you are interested in becoming a Data Scientist, then you should have the skills for crunching data, making new inferences, ability to look at the same problem from a different angle, and so on.
‘Learning from data is virtually universally useful. Master it and you’ll be welcomed nearly everywhere!’ – John Elder, Elder Research
A Data Scientist’s job is to analyze data for actionable insights by doing following tasks:
- Identifying data analytics problems that offer the greatest value for the organization
- Getting to know the most appropriate datasets and variables
- Working with unstructured data like video, images, etc.
- Discovering new solutions and opportunities by analyzing data
- Collecting large sets of structured and unstructured data from disparate sources
- Cleaning and validating data to ensure accuracy, completeness, and uniformity
- Devising and applying models and algorithms for mining big data
- Analyzing the data to identify patterns and trends
- Communicating findings to stakeholders using visualization and other means
Check out this great video on ‘What does a Data Scientist do?’:
Becoming a Data Scientist
Most of the quality time of a Data Scientist is spent in data collection, cleaning, and converting the data into valuable business insights. Cleaning the data is one of the most important aspects among them. However, this task needs detailed understanding of working with data and using various tools and techniques like statistics, computer programming skills, and more. It is important to understand the bias in the data which could be used for the purpose of debugging output from the code.
Once the data is cleansed, then the data exploration part starts wherein the Data Scientist will be converting the data into visual insights through the tools of data visualization. It is all about finding the right patterns, building the optimal model, and having cutting-edge algorithms so as to get a clear insight and work with it at a much deeper level.
Data Scientist Requirements
Here are some of the prerequisites to become a Data Scientist:
- Have an educational background preferably in Computer Science, Information Technology, Mathematics, and Statistics and work experience in a related field
- Have a knack for problem-solving
- Be able to work individually or in a team
- Be interested in collecting and analyzing data
- Have effective verbal and visual communication skills
- Be interested in learning new and cross-disciplinary skills
‘Data Scientists are kind of like the new Renaissance folks, because Data Science is inherently multidisciplinary’ – John Foreman, VP MailChimp
For a Data Scientist, there is a need to have very good grasp of mathematical computation, an analytical bent of mind, curiosity, and creative thinking. He/she should be able to discover hidden opportunities, trends, patterns, and more. It all starts with asking the right question, connecting the dots, and searching for the right answer from various results available. He/she should be able to devise the right model and computer algorithms that can answer the most pressing business questions. A big majority of Data Scientists have a master’s degree, and nearly half of them have PhDs. Being able to think like an entrepreneur is also part of the job skill.
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Two of the most important programming languages that a Data Scientist is supposed to know are R and Python. Most of the times, the Data Scientist has to work in an inter-disciplinary team consisting of Business Strategists, Data Engineers, Data Specialists, Analysts, and other professionals. Most of these other roles work as a supporting panel to the Data Scientist. The Data Scientist should be able to devise his own methodologies. He/she should slice and dice data and come up with value addition through the use of algorithms. He/she should also know how to visualize the data through data visualization tools and more.
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What are the various job roles in Data Science?
This is the role that includes understanding the statistical and mathematical models in order to apply them to the data. They apply their theoretical knowledge in the domains of statistics and algorithms to find the best way to solve a certain problem.
There are Data Scientists who fine-tune the statistical and mathematical models that are applied onto data. When somebody is applying their theoretical knowledge of statistics and algorithms to find the best way to solve a Data Science problem, they are filling the role of Data Scientist. The Data Scientist is able to build a data question into a business proposition, solve the business problem, create the predictive models, answer the pressing problems that the business is facing, and do a little bit of storytelling when it comes to manifesting the findings.
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When, Statisticians are able to create statistical models and implement them to approach the data to parse it, Data Scientists are able to bridge between the computer programming and those that take the business decision, convert the theory into practical knowledge, and apply it for solving real-world business problems.
Some of the skills needed by a Data Scientist here include a thorough knowledge of statistics, mathematics, and a complete knowledge of various computer programming languages. He/she should be able to ask the right questions and structure the data problem so that it can be solved and the results can be communicated to the right stakeholders in the organization.
One of the most important differences between a Data Scientist and a Data Engineer is that Data Engineers are able to handle large amounts of data using their excellent software engineering and programming skills. Thus, they are more often than not concentrating on coding, cleaning the data that is available, and working in close coordination with Data Scientists. If a Data Scientist is taking the predictive model and implementing the code, then they are in effect taking on the role of a Data Engineer.
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Data Architects are the professionals who are well adept in coming up with the data model. They are database administrators focusing on structuring the technology, implementing the data storage problems, and working in close coordination with the Data Engineers.
Some of the skills that are needed for a Data Engineer are to have a knowledge of data storage and data warehousing skills and an understanding of SQL and NoSQL. They should also be adept at other Big Data frameworks like the Hadoop or Apache Spark in order to gather data from various sources, and they should process big data and derive meaning out of it.
Data Analyst is another important role that falls under the category of Data Science. This role includes the aspect of analyzing the data and creating reports and other compelling visualizations in order to help others easily understand the analysis that has been done. If a Data Scientist helps other people in the organization by creating good charts, maps, etc., then they are in effect fulfilling the role of a Data Analyst.
The role of a Business Analyst comes within the purview of the Data Analyst job role. The Business Analyst is more concerned with the business implications of a data analysis process. It is more about giving the right data-driven implication of showing which is the best path forward for any organization, like choosing between path A and path B. The Data Analyst is supposed to know about data manipulation using various tools like MS Excel and communicate the findings through the right visualization.
What are the various tools that a Data Scientist uses?
There are a huge set of tools that a Data Scientist uses every day. These tools fall under various categories like scripting and programming tools, statistical programming tools, and tools for data analysis, among a whole host of other tools.
The structured query language is one of the most popular tools that a Data Scientist uses. It helps make sense of the structured data and work on relational database management systems. Along with Data Scientists, this SQL tool is also used extensively by Data Engineers.
- R Programming
R is one of the most important statistical computing tools. It is used extensively by Statisticians and Data Analysts in order to make a detailed analysis of the data and derive valuable inferences from it.
Python is one of the most versatile object-oriented programming languages that is being used by Data Scientists. One of the most important applications of Python programming language is in the Machine Learning domain. Python, along with its vast variety of libraries, which can be used for almost every task, is the perfect tool for Machine Learning and Data Science.
Hadoop is the most powerful and open-source tool that is used for working with Big Data and making sense of it. It includes a whole ecosystem of tools and technologies that are used by almost every Data Scientist.
SAS is an advanced analytics tool that is used by a lot of Data Analysts. It has powerful features for extracting, analyzing, and reporting on a whole host of data. It has a huge set of analytics tools, along with statistical functions and an excellent GUI (Graphic User Interface), for Data Scientists to convert their data into valuable business insights.
This is the most popular Business Intelligence and data visualization tool that has excellent reporting capabilities. It is being used by Data Analysts for showing the results of their analyses in a manner that is easily comprehensible to everyone.
Today, the demand for Data Scientists is more than ever. According to McKinsey, the US alone would face a shortage of 140,000 to 190,000 people with deep analytical skills and 1.5 million Big Data Analysts and Managers in the next two years. All this shows the skyrocketing demand for people with Data Science and Data Analysis skills in the world, today. With more and more organizations planning to hire qualified Data Scientists, the need for them to get trained and certified will only increase in the future. Hence, it has become almost mandatory for candidates aspiring to become Data Scientists to acquire training and certification in this cutting-edge technology.
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