What is Data Science


Data Science is study of raw data, thus providing meaning to the complex or big data. Data is collected through different channels and is used not to execute rather learn and develop new business capabilities. Data Science is the most recent wave in the new upcoming technologies in today’s scenario. In a study, it has been observed that increase in profit and return on investment on data science is increasing day by day. Companies apply data science to everyday activities to bring value to their customers. Most of the industries, such as banking institutions, are counting on data science for fraud detection successes. Companies like Netflix also use algorithms to analyse user preferences and therefore to determine what to deliver to its users.

Data science is a separate field and is close to computer science. It involves creating programs and algorithms to record and process data. Data science covers all types of data analysis which may or may not use computers. Data Science is quite closely related to the statistical science, which includes the collection, organisation, analysis, and presentation of data.

Data Science covers the entire scope of data collection and processing.

As increasing amount of data becomes more accessible, large tech companies are no longer the only ones needing a data scientist. There is a growing demand of data scientists which is not equal to the supply of the data scientists, hence, there is shortage a of the qualified candidates available to fill the open positions.  So, choosing Data Science as a career option has a lot of scope and potential and will remain so in the near future.

Data science is evolving and its application will continue to bring change. Data science may save money and improve efficiency of business process, but these technologies can also destroy business value. The risk of inability to identify and manage data can lead some managers to delay the adoption of the technologies and thus preventing them to realize their full potential.

Data science in risk management has always been a matter of measuring; it quantifies the frequency of loss and multiplies it by severity of the damage. Any forward thinking organisation asses and tracks its risk factors and tackles complex challenges using Data Science as it provides analytical tools.

So a separate vertical is required to manage and use data science.

Generally speaking, the data science workflow looks like this:


  • Ask a question;
  • Gather data that might help you to answer that question;
  • Clean the data;
  • Explore, analyze, and visualize the data;
  • Build and evaluate a machine learning model;
  • Communicate results

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Scope of data Science

The demand for Data Scientists has increased manifold over the period of time and there is a huge scope for Data Scientists who want to make a career in this field. As Data science is being used in almost every sphere of society, be it industries, education, entertainment, health etc, it makes this a very promising career.

10 applications of DATA Science in various domains or fields is as below:

  1. Fraud and Risk Detection
  2. Healthcare
  3. Internet Search
  4. Targeted Advertising
  5. Road Travel
  6. Government
  7. Website Recommendations
  8. Advanced Image Recognition
  9. Speech Recognition
  10. Gaming

  1. Fraud and risk detection:- Banking and Financial services industry has a separate segment for data analysis. The earliest application of Data Science was in finance. Data Science was brought in order to rescue the organisations out of losses. It helped them to segment the customers on the basis of past expenditure, current credits and other essential variables to analyse the probability of risk and default. It also helped them to push their financial products based on customer’s financials.

  1. Healthcare:- Healthcare database of individuals who have been using healthcare systems for a long time helps in identifying and predicting disease and personalized healthcare recommendations. For e.g. some individuals are diagnosed with diabetes and a subset have developed the complications. Data Science becomes useful in drawing patterns of the complications and probability of the complications therefore advising the necessary precautionary steps.

  1. Targeted Advertising:- Thedigital advertisement get a higher click through ratings rather than traditional advertisements. It is targeted based on user’s previous behaviour. Automating digital ad placement is the reason the wife sees an apparel advertisement and the husband sees a real estate deal advertisement at the same place and same time.

  1. Internet Search:- We have many search engines such as Yahoo, Bing, Ask, AOL, and Google of course. All these search engine use data science algorithms to deliver the best results and it is their responsibility to verify the resource and deliver the correct result.

  1. Website recommendations:- E-commerce provides a personalised digital mall to everyone. Using data science, Companies have become intelligent enough to push and sell products as per customer’s purchasing power and interest through previous product searches or purchases. In Amazon, we get suggestions about the similar products that we had earlier looked for.

  1. Road Travel:- A perfect example is Google maps in which Google uses the road maps data to update the app. The biggest challenge is to keep the map updated on real time basis as it has to be updated as per the traffic in the particular area as well as any ongoing construction, road blocks, bad weather etc with an alternative route.

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  1. Government:- Government is maintaining the records of the citizens in their database including the photographs, fingerprints, addresses, phone numbers etc in order to maintain law and order in the country. This data helps the government in taxation, passing on financial benefits to the needy, and even tracking down the lost people.

  1. Advanced Image Recognition:-  When we upload an image on Facebook, we get suggestions to tag friends. These automatic suggestions uses face recognition algorithms. Apple uses the same kind of software to segregate photo in the photo gallery. Online payment app uses QR code to make the payments successful.

  1. Speech Recognition:-  Best example of speech recognition products are Siri, Alexa, Google voice, Cortana etc. Now days, it is an added feature in almost every electronic product which uses graphic user interface to take commands from its users. Speech recognition is being used to type messages on almost every message sharing applications.

  1. Gaming:- Electronic games are designed using machine learning algorithms which improves and upgrade themselves as the player moves up to the next level. In motion gaming too the opponent (computer) analyses previous moves and accordingly shapes the games.

With each passing day the volume of data is increasing and it demands analytical tools for storing, processing and analysing data.

What skills are needed to be data scientists?

  • One of the key requirements for data scientist is to have analytical mindset
  • Domain knowledge of the field of work. For example banking is a field of work, so the data scientist is to have the basic knowledge of how the banking industry functions
  • Problem solving skills
  • Statistical and programming skills (Technical skills)

The top 5 data science career opportunities to explore?

Data Scientists: – Data scientist give advice to businesses on potential of data, providing new insights into business using statistical analysis. Data scientists not only understand the language of data, but they can also analyze it and draw actionable insights from it. Moreover, they’ve mastered the art of data storytelling to a level that makes both management and stakeholders nod in agreement and plan their strategy accordingly.

Data Analyst: – Data analysts are the real soldiers of data science. They’re the ones who are involved in collecting the data, structuring the databases, creating and running models for data centre, and also preparing advanced types of analyses to explain the patterns and trends  in the data that have already emerged. A data analyst also looks after the basic part of predictive analytics.

BI Analyst: – Business analysts assist the company with planning and monitoring by eliciting and organizing requirements. They approve resource requirements and create cost-estimate models by designing informative, actionable and repeatable reporting.

Data Engineer: – Data engineers finds trends in data sets and develop algorithms to help make raw data more useful to the business. This role requires a significant set of technical skills. A deep knowledge of SQL database design and multiple programming languages is must for data engineer . Data engineers need communication skills to acknowledge the work across departments to understand what business leaders want from the company’s large database.

Data Architect: – Data Architects are technical experts who adapt dataflow management and data storage strategy to a wide range of businesses and solutions. They are in charge of continually improving the way data is collected and stored. Furthermore, data architects control the access to data thereby ensuring data sanctity and safety from corporate spies.

Job description of a Data Scientist

Data science projects and tasks  varies from enterprise depending on the business, there are some primary job functions that tend to be common among all data science positions such as:

  • Collecting massive amounts of data and converting it to an analysis-friendly form
  • Problem-solving business-related challenges while using data-driven techniques and tools.
  • Using a variety of programming languages, as well as programs, for data collection and analysis.
  • Having a wealth of knowledge with analytical techniques and tools.
  • Communicating the findings through data visualizations
  • offering advice through comprehensive reports.
  • Identifying patterns and trends in data;
  • Providing a plan to implement improvements.
  • Predictive analytics; anticipate future demands, events, etc.
  • Contribute to data collection, processing, architectures, modelling standards, reporting and data analysis methodologies.
  • Invent new algorithms to solve problems and build analytical tools.
  • Recommend cost-effective changes to existing process and forecasting strategies.

Tips for people starting a career in Data Science.

  1. Choose the right role :- Choosing a career in data science is not simple, there are lot of varied roles available that include machine learning expert, data engineer, data visualization expert, a data architect and many more. Choice of role depends on the work experience and background. It is important to understand what is required for every role before deciding what to opt for. Talk to people who are already working in the industry to identify what roles are available and what each of them entails. Figure out what the strengths and what role closely aligns with the field of study and interests. It is important to fully understand what each role requires, rather than hastily jumping into applying for it and finding that it is not a good match for where we want to go in our career.

  1. Focus on practical applications and not just theory or do not spend too much time on theory; there has to be balance with theory and projects. As projects allows to practice the tools that is learned and it will improve the portfolio and builds confidence. The theory is important, but to set up for getting a job, it also need to set time aside to work on projects. They will allow to practice what will be creating in a data science job, help to improve your portfolio and build your confidence when attempting to score an interview.

  1. Take up a Course and complete it: – Once the role is decided, the next step comes as what should be the requirements of the role and qualifications needed. Do note that there are multiple courses and material available to help out with this.

  1. Choose a Tool / Language and stick to it. Choose any mainstream tool or language to give a start to the data science journey as tools are for implementation but understanding the concept is important.

  1. Learning Multiple Tools at Once: -Because of the different features, uses and unique quality each tool offers, people tend to attempt learning all the tools at once. This is a very bad idea, as one will end up mastering none of them. Going behind multiple tools will create lots of confusion and will severely affect the problem solving skills at the beginning stage.

  1. Join a peer group: – It is important to have industry peers to lean on for advice and support. As peer group keeps motivated, overcome hurdles and can avoid some pitfalls. If you are new to the industry it is hard to find like minded people, so it becomes necessary to set time aside to find meet-ups and events that are relevant to the career. It also gives the chance to mingle with leading technology companies who are searching people. 

  1. Follow the right resources: – Learning is a continuous process, and data scientist requires trapping every source of knowledge they can find. The most useful source of this information is latest updates. Read about the people, subject and recent updates related to data science. As technology keeps on changing and its necessary to be updated with the pace of time.

  1. Work on your communication skills: – It is not easy to describe and communicate the technical and mathematical topics. It needs the practice to describe an algorithm or technical concept to a colleague. The ability to articulate complex concepts in a clear and concise manner is a must-have. It also includes ability to understand what other people need. A data scientist needs to practice explaining technical concepts to non-technical audiences

  1. Domain Knowledge: – We have very little chance of solving a problem if we do not understand what we need to solve. And domain knowledge gives that understanding where data scientist has to ask and solve the questions.  It is very important to have domain knowledge as it gives us the structure of the task that needs to be done. Structured thinking provides a framework where an unstructured problem can be defined. Having a structure helps an analyst understand the problem at a macro level, it also helps by identifying areas which require more attention. Without structure, an analyst does not know where to start.

  1. Don’t use too much jargon on a resume: – Do not simply write the jargons or list of the programming language you know instead write how it is used to achieve the result s. Do not mention too many skills rather write fewer skills that you have mastered with the help of projects.

  1. Find a mentor:- On top of regular networking, one of the best tips you can follow for getting a job as a data scientist is to find a mentor. A mentor guides through projects and academic courses and can even help you figure out exactly what kind of skills employers are looking in a data scientist candidate.

  1. Working Consistently As the technology is continuously changing and to keep up with this,continuous learning and growth at a personal and professional level is required. Which will not only continue to place  strong importance on learning new skills and sharing knowledge base It will also assist in all areas of life, from creating more meaningful personal relationships, to better organisation and time management skills. Remember that practicing for 2 hours daily is much better than practicing for 14 hours on weekends

  1. Stick to organisations that value individual contributors. A good data scientist has who work individually contribute are recognized more at par to experienced or more competent than others, and their pay check and task assignments often reflect that. As we get more senior, we are given more decision-making power over what gets implemented and how it gets implemented. Managers are focused on business need prioritization, inter-personal relationships, ensuring access to resources, and things like that.

  1. Build your profile : Show off your work.

Sharing your work can not only generate traffic to your blog but can also result in recommendations about how to improve your work. Potential leads from recruiters who are searching for candidates. Direct opportunities from companies who are searching for people like you. Industry peers for you to turn to for support throughout your career.

There is a huge demand of data science and employers are investing time and money in data scientists. It is necessary to take steps to lead an exponential growth. The above are a few tips that will help shape the career formation as data scientist.

I hope it broadens your perspective; anything that is worthwhile takes time and hard work to happen.  Learn with curiosity and optimism. And don’t be afraid to make mistake but do try to avoid.

The demand for individuals with skills of data scientist will continue to increase, and those already in data science roles are sure to see their salaries increase in the future. As technology continues to develop at a rapid rate, the skills needed to work with that technology need to evolve faster or they become obsolete. One job that has grown in leaps and bounds is that of a data scientist. With the rise of big data numbers comes the need for more analytical and highly skilled people to analyse process interpret and mine that data for businesses. 

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