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 wear many hats. He is a part mathematician, part analyst, part computer scientist, and part trend-spotter.
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Comparing Data Scientist with Data Engineer
|Criteria||Data Scientist||Data Engineer|
|Mostly works with||Statistics and Data Analysis||Databases and ETL|
|Common Tools||R , SAS||MySQL, Hive|
Some of the tasks of a Data Scientist include:
- Collect large amounts of data and analyze it
- Use data-driven techniques for solving business problems
- Communicating the results to business and IT leaders
- Spotting trends, patterns, relationships within data
- Converting data into compelling visualizations
- Artificial Intelligence and machine learning techniques
- Deploying text analytics and data preparation
Some of the technologies that a Data Scientist works with:
- Programming skills in Java, Python, R, 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?
The day to day activities of a data scientist sometimes can be predictable and sometimes they are something out of the ordinary. The 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 through the following.
- Finding out which are the data analytics problems that offer the greatest value
- Getting to know the most appropriate data sets and variables
- Collecting data from large disparate sources of data
- Working with mostly unstructured data like video, images, etc
- Discovering new solutions and opportunities by analyzing data
- Identifying the data-analytics problems that offer the greatest opportunities to the organization
- Determining the correct data sets and variables
- Collecting large sets of structured and unstructured data from disparate sources
- Cleaning and validating the data to ensure accuracy, completeness, and uniformity
- Devising and applying models and algorithms to mine the stores of big data
- Analyzing the data to identify patterns and trends
- Interpreting the data to discover solutions and opportunities
- Communicating findings to stakeholders using visualization and other means
Check this great video to learn what does a Data Scientist actually does?
Becoming a Data Scientist
Most of the time of a data scientist is spent in data collection, cleaning, converting the data into valuable business insight. Cleaning the data is one of the most important aspects. All this needs detailed understanding of working with data and use of 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 the cutting-edge algorithms so as to get clear insight into the product and work with it at a much deeper level.
Data Scientist Requirements
Here are some of the prerequisites to become a Data Scientist:
- Having an educational background preferably in computer science, information technology, mathematics, statistics
- Have the work experience in a related field
- Has a knack for problem-solving and can work individually or in a team
- Is interested in collecting and analyzing data
- Can communicate effectively verbally or using visualizations
- Is 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 should be able to discover hidden opportunities, trends, patterns and more. It all starts with asking the right question, connecting the dots, searching the right answer from the various results available.He 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 Masters degree and nearly half of the data scientists have a PhDs. Being able to think like an entrepreneur is also part of the job skill. Two of the most important programming skills that a Data Scientist is supposed to have is R programming language and Python programming language.Many 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 in a supporting capacity to the data scientist. Hence the data scientist should be able to devise his own methodologies, slice and dice data, come up with value addition through the use of algorithms, statistics, should be able to visualize the data through data visualization tools and more.
What are the various job roles within Data Science?
This is the role that includes understanding the statistical and the mathematical models in order to apply it to the data. They apply their theoretical knowledge in the domains of statistics and algorithms in order 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 and solve the business problem and create the predictive models and answer the pressing problems that the business is facing and do a little bit of storytelling when it comes to manifesting the findings.
On the other hand the statistics are able to create the statistical models and implement it to approach the data in order to parse it. The data scientists are able to be the bridge between the computer programming and those that take the business decision and convert the theory into practical knowledge and apply it for solving real world business problems.
Some of the skills needed include a thorough knowledge of statistics, mathematics and a complete knowledge of the various computer programming languages. He should be able to ask the right questions and structure the data problem so that it can solved and the results can be communicated the to the right stakeholders in the organization.
One of the most important differences between a data scientist and data engineer is that the data engineers are able to handle large amounts of data using their excellent software engineer and programming skills. Thus they more often than not are seeing concentrating on coding, cleaning the data that is available, and work in close coordination with the data scientists. If the data scientist is taking the predictive model and implement the code then they are in effect taking on the role of a data scientist.
The data architects are the professionals who are well adept in coming up with the data model and be the database administrator and focus on structuring the technology and implement the data storage problems and work in close coordination with the data engineers.
Some of the skills that are needed as part of the data engineer is to have a knowledge of data storage and data warehousing skills along with a knowledge of SQL and NoSQL. They should also be adept at other big data framework like the Hadoop or Apache Spark in order to gather the data from various sources and process it and derive meaning out of it.
Data analyst is another of the important roles that falls under the category of data science. This role includes the aspect of analyzing the data and creating the reports and other compelling visualizations in order to help other people easily understand the analysis that have done. If a data scientist helps other people in the organization by creating good charts, maps, 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. 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 in terms of choosing the path A or 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 during the course of a day. These tools fall under the various categories like scripting and programming tools, statistical programming tools, tools for data analysis among a whole host of tools.
The structured query language is one of the most popular tools that a Data Scientist uses. It helps to make sense of the structured data and work on relational database management systems. Along with the data scientists this SQL tool is also used extensively by Data Engineers.
R is one of the most statistical computing tools. It is use extensively by statisticians and data analyst 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 languages is in machine learning domain. Python along with its vast variety of libraries for almost every task is the perfect tool for machine learning and data science.
Hadoop is the most powerful and open source tools that is used for working with big data and make sense of it. It includes a whole ecosystem of tools, and technologies that are used by almost every data scientist during the course of a day.
SAS is an advanced analytics tools 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 analysis in a manner that is easily comprehensible to everyone.
Today the demand for Data Scientists is more than ever. According to McKinsey, by 2018 the US alone could face a shortage of 140,000 to 190,000 people with deep analytical skills and 1.5 million big data analysts and managers. All this shows the amount of demand there is for people with Data Science and Data Analysis skills. With more and more organizations looking to hire qualified Data Scientists, the need for trained and certified Data Scientists will only increase in the future.
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