Data Science

Data science is a multidisciplinary blend of data inference, algorithm development, and technology in order to solve analytically complex problems. At the core is data. Troves of raw information, streaming in and stored in enterprise data warehouses. Much to learn by mining it. Advanced capabilities we can build with it. Data science is ultimately about using this data in creative ways to generate business value. Data science combines several disciplines, including statistics, data analysis, machine learning, and computer science. This can be daunting if you’re new to data science, but keep in mind that different roles and companies will emphasize some skills over others, so you don’t have to be an expert at everything.

Types Of Data Science:
1. Data Scientist As Statistician:

This is data analysis in the traditional sense. The field of statistics has always been about number crunching. A strong statistical base qualifies you to extrapolate your interest in a number of data scientist fields. Hypothesis testing, confidence intervals, Analysis of Variance (ANOVA), data visualization and quantitative research are some of the core skills possessed by statisticians which can be extrapolated to gain expertise in specific data scientist fields explained in following section of this article. Statistics knowledge, when clubbed with domain knowledge (such as marketing, risk, actuarial science) is the ideal combination to land a statistician’s work profile. They can develop statistical models from big data analysis, carry out experimental design and apply theories of sampling, clustering and predictive modelling to available data to determine future corporate actions.
2. Data Scientist As Mathematician:

Mathematicians have conventionally been related with extensive theoretical research but emergence of big data and data science have changed that perception. Mathematicians have been gaining more acceptance into the corporate world than ever before, owing to their deep knowledge of operations research and applied mathematics. Their services are sought after by businesses to carry out analytics and optimization in various fields such as inventory management, forecasting, pricing algorithm, supply chain, quality control mechanism and defect control. Defence and military organizations also seek mathematicians to carry out crucial big data assignments such as digital signal processing, series analysis and transformative algorithms.

3. Data Scientists Vs Data Engineers:

These are often confused with data scientists. However, a data engineer’s role is very different from that of a data scientist. A data engineer has the responsibility to design, build and manage the information captured by an organization. He is entrusted with the job of putting in place a data handling infrastructure to analyse and process data in line with an organization’s requirements. Additionally, he is also responsible for its smooth functioning. They need to work closely with data scientists, IT managers and other business leaders to translate raw data into actionable insights which would result in competitive edge for the organization.

4. Data Scientist As Machine Learning Scientists:


Computer systems around the world are increasingly being equipped with artificial intelligence and decision making capabilities. They possess neural networks that are programmed for adaptive learning – meaning they can be trained over a period of time to make same decisions when same set of inputs is given to them. Machine Learning Scientists develop such algorithms which are used to suggest products, pricing strategies, extract patterns from big data inputs and most importantly, demand forecasting (which can be extrapolated for better inventory management, strengthening supply chain networks, etc.).

5. Data Scientist As Actuarial Scientist:


Actuarial Science has been around for a long time. Banks and financial institutions rely a lot on actuarial science to predict the market conditions and determine the future income, revenue, profits/losses from these mathematical algorithms. It is possible to be an actuarial scientist without having to go through data science training. But a data scientist will have a very good grasp over the mathematical and statistical algorithms that are required for actuarial science. A lot of companies are now expediting the process by hiring CFAs to do the work of an actuarial scientist.

This is a very specific position which requires data science professionals to apply mathematical and statistical models to BFSI (Banking, Financial Services and Insurance) and other associated professions. One must possess a globally defined skill set and demonstrate it by passing a series of professional examinations before applying for this job. Preliminary requirement is to know a number of interrelated mathematical subjects such as probability, statistics, finance, economics, financial engineering and computer programming. Unlike other positions, actuarial science has existed and evolved over the past few decades and many universities around the world have relevant courses at undergraduate and postgraduate levels. Job search website CareerCast ranked it as the No. 1 job in United States in the year 2010 and its popularity has grown ever since.

6. Data Scientist As Business Analytic Practitioners:


Businesses make the final use of all the number crunching done by data science professionals. As a business analytic professional it is important to have business acumen as well as know your numbers. Business analysis is a science as well as art and one cannot afford to be driven entirely by either business acumen or by insights obtained based on data analysis. These professionals sit between front end decision making teams and the back end analysts. They work on crucial decision making such as ROI analysis, ROI optimization, dashboards design, performance metrics determination, high level database design, etc.

7. Data Scientist as Software Programming Analysts:


Unlike traditional coders, this class of professionals have a knack for number crunching through programming. Needless to mention, they are adept at logical thinking and as a result, they take to new programming languages as ducks takes to water. A number of programming languages such as R programming, Python, Apache Hive, Pig, Hadoop and the like support data analytics and visualizations. Software programming analysts have the programming skills to automate routine big data related tasks to reduce computing time. They are also required to handle database and associated ETL (Extract Transform Learn) tools that can extract data, transform it by applying business logic and to load it into visual summary representations such as charts, histograms and interactive dashboards.

8. Spatial Data Scientist:


Increasing use of GPS base systems has given rise to a separate category of data scientists – the spatial engineers. Unlike normal big data analysis which largely involves numbers, spatial data needs specialized handling. GPS coordinates need to be stored, mapped and processed differently compared to scalar numbers. They also need a separate database management system for storage. Google maps, car navigation systems, Bing maps and a number of applications, use spatial data for localization, navigation, site selection, situation assessment, etc. Government agencies use spatial data received from satellites to make important decisions related to weather conditions, irrigation, fertilizer usage, etc.

9. Data Scientist As Digital Analytic Consultant:


This is a very popular position and a number of organizations – ranging from Fortune 500s to small non – for – profits – seek digital analytics talent. It is a common misconception that a digital analytic professional only needs technical talent. In addition, one also needs to be sound in business and marketing skills to be successful. Configuring websites using JavaScript tags to collect data and direct it to analytics tools such as Google Analytics and finally visualizing it through filtering, processing and designing dashboards are core skills involved. A Data Scientists needs to be able to define the data in accordance with the business problem – and for this he/she needs to know the business end of the spectrum.

10. Data Scientist As Quality Analyst:


Quality Analyst has for long been associated with statistical process control in manufacturing industry. This position has been included here to emphasize the importance of data science in core industries. Assembly lines involved in mass production have large data sets to be analysed to maintain quality control and meet minimum performance standards. The job has evolved over the years with new analytic tools which are used by data scientists to prepare interactive visualizations that serve as key inputs in decision making across teams such as management, business, marketing, sales and customer service.

Advantages & Disadvantages Of Data Science:


1. It’s in Demand:

Data Science is greatly in demand. Prospective job seekers have numerous opportunities. It is the fastest growing job on Linkedin and is predicted to create 11.5 million jobs by 2026. This makes Data Science a highly employable job sector.

2. Abundance Of Positions:

There are very few people who have the required skill-set to become a complete Data Scientist. This makes Data Science less saturated as compared with other IT sectors. Therefore, Data Science is a vastly abundant field and has a lot of opportunities. The field of Data Science is high in demand but low in supply of Data Scientists.

3. A Highly Paid Career:

Data Science is one of the most highly paid jobs. According to Glassdoor, Data Scientists make an average of $116,100 per year. This makes Data Science a highly lucrative career option.

4. Data Science is Versatile:

There are numerous applications of Data Science. It is widely used in health-care, banking, consultancy services, and e-commerce industries. Data Science is a very versatile field. Therefore, you will have the opportunity to work in various fields.

5. Data Science Makes Data Better:

Companies require skilled Data Scientists to process and analyze their data. They not only analyze the data but also improve its quality. Therefore, Data Science deals with enriching data and making it better for their company.

6. Data Scientists Are Highly Prestigious:

Data Scientists allow companies to make smarter business decisions. Companies rely on Data Scientists and use their expertise to provide better results to their clients. This gives Data Scientists an important position in the company.

7. No More Boring Tasks:

Data Science has helped various industries to automate redundant tasks. Companies are using historical data to train machines in order to perform repetitive tasks. This has simplified the arduous jobs undertaken by humans before.

8. Data Science Makes Products Smarter:

Data Science involves the usage of Machine Learning which has enabled industries to create better products tailored specifically for customer experiences. For example, Recommendation Systems used by e-commerce websites provide personalized insights to users based on their historical purchases. This has enabled computers to understand human-behavior and take data-driven decisions.

9. Data Science Can Save Lives:

Healthcare sector has been greatly improved because of Data Science. With the advent of machine learning, it has been made easier to detect early-stage tumors. Also, many other health-care industries are using Data Science to help their clients.

10. Data Science Can Make You A Better Person:

Data Science will not only give you a great career but will also help you in personal growth. You will be able to have a problem-solving attitude. Since many Data Science roles bridge IT and Management, you will be able to enjoy the best of both worlds.


1. Data Science Is Blurry Term:

Data Science is a very general term and does not have a definite definition. While it has become a buzzword, it is very hard to write down the exact meaning of a Data Scientist. A Data Scientist’s specific role depends on the field that the company is specializing in. While some people have described Data Science to be the fourth paradigm of Science, few critics have called it a mere rebranding of Statistics.

2. Mastering Data Science Is Near To Impossible:

Being a mixture of many fields, Data Science stems from Statistics, Computer Science and Mathematics. It is far from possible to master each field and be equivalently expert in all of them. While many online courses have been trying to fill the skill-gap that the data science industry is facing, it is still not possible to be proficient at it considering the immensity of the field. A person with a background in Statistics may not be able to master Computer Science on short notice in order to become a proficient Data Scientist. Therefore, it is an ever-changing, dynamic field that requires the person to keep learning the various avenues of Data Science.

3. Large Amount Of Domain Knowledge Required:

Another disadvantage of Data Science is its dependency on Domain Knowledge. A person with a considerable background in Statistics and Computer Science will find it difficult to solve Data Science problem without its background knowledge. The same holds true for its vice-versa. For example, A health-care industry working on an analysis of genomic sequences will require a suitable employee with some knowledge of genetics and molecular biology. This allows the Data Scientists to make calculated decisions in order to assist the company. However, it becomes difficult for a Data Scientist from a different background to acquire specific domain knowledge. This also makes it difficult to migrate from one industry to another.

4. Arbitrary Data May Yield Unexpected Results:

A Data Scientist analyzes the data and makes careful predictions in order to facilitate the decision-making process. Many times, the data provided is arbitrary and does not yield expected results. This can also fail due to weak management and poor utilization of resources.

5. Problem Of Data Privacy:

For many industries, data is their fuel. Data Scientists help companies make data-driven decisions. However, the data utilized in the process may breach the privacy of customers. The personal data of clients are visible to the parent company and may at times cause data leaks due to lapse in security. The ethical issues regarding preservation of data-privacy and its usage have been a concern for many industries.


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