Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.

Data Science is also a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data.

Data Science  has fast emerged as a challenging, lucrative and highly rewarding career. While developed countries became familiar with it halfway through the last decade, data science has caught attention on a global scale after the exponential growth of e-commerce in developing economies, especially India and China. In the past decade there has been considerable paradigm shift in the way the world shops, books holidays, makes transactions and pretty much everything else.

Skills Required to Become a Data Scientist

Anyone interested in building a strong career in this domain should gain critical skills in three departments: analytics, programming, and domain knowledge. Going one level deeper, the following skills will help you carve out a niche as a data scientist:

• Strong knowledge of Python, SAS, R, Scala
• Hands-on experience in SQL database coding
• Ability to work with unstructured data from various sources like video and social media
• Understand multiple analytical functions
• Knowledge of machine learning

What is Data Analytics?

A data analyst is usually the person who can do basic descriptive statistics, visualize data, and communicate data points for conclusions. They must have a basic understanding of statistics, a perfect sense of databases, the ability to create new views, and the perception to visualize the data. Data analytics can be referred to as the necessary level of data science. 

Skills Required to Become a Data Analyst

A data analyst should be able to take a specific question or topic, discuss what the data looks like, and represent that data to relevant stakeholders in the company. If you’re looking to step into the role of a data analyst, you must gain these four key skills:

• Knowledge of mathematical statistics
• Fluent understanding of R and Python
• Data wrangling
• Understand PIG/ HIVE

Data Science vs. Data Analytics

Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. While a data scientist is expected to forecast the future based on past patterns, data analysts extract meaningful insights from various data sources. A data scientist creates questions, while a data analyst finds answers to the existing set of questions.

What is difference between data science and data analytics?

Data analytics is more specific and concentrated than data science. Data analytics focuses more on viewing the historical data in context while data science focuses more on machine learning and predictive modeling. … On the other hand, data analytics involves a few different branches of broader statistics and analysis.

TYPES OF DATA SCIENTISTS

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.

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.

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.

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.

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.

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.

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.).

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.

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.

 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.

Here are some advantages of data science in business:

• Mitigating risk and fraud. Data scientists are trained to identify data that stands out in some way. They create statistical, network, path, and big data methodologies for predictive fraud propensity models and use those to create alerts that help ensure timely responses when unusual data is recognized.
• Delivering relevant products. One of the advantages of data science is that organizations can find when and where their products sell best. This can help deliver the right products at the right time—and can help companies develop new products to meet their customers’ needs.
• Personalized customer experiences. One of the most buzzworthy benefits of data science is the ability for sales and marketing teams to understand their audience on a very granular level. With this knowledge, an organization can create the best possible customer experiences.

Here are some advantages of data science in business:

• Mitigating risk and fraud. Data scientists are trained to identify data that stands out in some way. They create statistical, network, path, and big data methodologies for predictive fraud propensity models and use those to create alerts that help ensure timely responses when unusual data is recognized.
• Delivering relevant products. One of the advantages of data science is that organizations can find when and where their products sell best. This can help deliver the right products at the right time—and can help companies develop new products to meet their customers’ needs.
• Personalized customer experiences. One of the most buzzworthy benefits of data science is the ability for sales and marketing teams to understand their audience on a very granular level. With this knowledge, an organization can create the best possible customer experiences.

8 Ways a Data Scientist Can Add Value to Business

Empowering Management and Officers to Make Better Decisions

An experienced data scientist is likely to be a trusted advisor and strategic partner to the organization’s upper management by ensuring that the staff maximizes their analytics capabilities. A data scientist communicates and demonstrates the value of the institution’s data to facilitate improved decision-making processes across the entire organization, through measuring, tracking, and recording performance metrics and other information.

2. Directing Actions Based on Trends—which in Turn Help to Define Goals

A data scientist examines and explores the organization’s data, after which they recommend and prescribe certain actions that will help improve the institution’s performance, better engage customers, and ultimately increase profitability.

3. Challenging the Staff to Adopt Best Practices and Focus on Issues That Matter

One of the responsibilities of a data scientist is to ensure that the staff is familiar and well-versed with the organization’s analytics product. They prepare the staff for success with the demonstration of the effective use of the system to extract insights and drive action. Once the staff understands the product capabilities, their focus can shift to addressing key business challenges.

4. Identifying Opportunities

During their interaction with the organization’s current analytics system, data scientists question the existing processes and assumptions for the purpose of developing additional methods and analytical algorithms. Their job requires them to continuously and constantly improve the value that is derived from the organization’s data.

5. Decision Making with Quantifiable, Data-driven Evidence

With the arrival of data scientists, data gathering and analyzing from various channels has ruled out the need to take high stake risks. Data scientists create models using existing data that simulate a variety of potential actions—in this way, an organization can learn which path will bring the best business outcomes.

6. Testing These Decisions

Half of the battle involves making certain decisions and implementing those changes. What about the other half? It is crucial to know how those decisions have affected the organization. This is where a data scientist comes in. It pays to have someone who can measure the key metrics that are related to important changes and quantify their success.

7. Identification and Refining of Target Audiences

From Google Analytics to customer surveys, most companies will have at least one source of customer data that is being collected. But if it isn’t used well—for instance, to identify demographics—the data isn’t useful. The importance of data science is based on the ability to take existing data that is not necessarily useful on its own and combine it with other data points to generate insights an organization can use to learn more about its customers and audience.

A data scientist can help with the identification of the key groups with precision, via a thorough analysis of disparate sources of data. With this in-depth knowledge, organizations can tailor services and products to customer groups, and help profit margins flourish.

8. Recruiting the Right Talent for the Organization

Reading through resumes all day is a daily chore in a recruiter’s life, but that is changing due to big data. With the amount of information available on talent—through social media, corporate databases, and job search websites—data science specialists can work their way through all these data points to find the candidates who best fit the organization’s needs.

By mining the vast amount of data that is already available, in-house processing for resumes and applications—and even sophisticated data-driven aptitude tests and games—data science can help your recruitment team make speedier and more accurate selections.

In Conclusion

Data science can add value to any business who can use their data well. From statistics and insights across workflows and hiring new candidates, to helping senior staff make better-informed decisions, data science is valuable to any company in any industry.

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