What is Data Science? The realities of Data Science is revealed!

Traditionally, the data that we had was mostly structured and small in size, which could be analyzed by using the simple BI tools. Unlike data in the traditional systems which was mostly structured, today most of the data is unstructured or semi-structured. Use of the term Data Science is increasingly common, but what does it exactly mean? What skills do you need to become Data Scientist? What is the difference between BI and Data Science? How are decisions and predictions made in Data Science? These are some of the questions that will be answered further.

Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. How is this different from what statisticians have been doing for years? The answer lies in the difference between explaining and predicting.

Data Scientist not only does the exploratory analysis to discover insights from it, but also uses various advanced machine learning algorithms to identify the occurrence of a particular event in the future. Data Science is primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics (predictive plus decision science) and machine learning.

  • Predictive causal analytics: If you want a model which can predict the possibilities of a particular event in the future, you need to apply predictive causal analytics. Say, if you are providing money on credit, then the probability of customers making future credit payments on time is a matter of concern for you. Here, you can build a model which can perform predictive analytics on the payment history of the customer to predict if the future payments will be on time or not.
  • Prescriptive analytics: If you want a model which has the intelligence of taking its own decisions and the ability to modify it with dynamic parameters, you certainly need prescriptive analytics for it. This relatively new field is all about providing advice. In other terms, it not only predicts but suggests a range of prescribed actions and associated outcomes.
    The best example for this is Google’s self-driving car which I had discussed earlier too. The data gathered by vehicles can be used to train self-driving cars. You can run algorithms on this data to bring intelligence to it. This will enable your car to take decisions like when to turn, which path to take, when to slow down or speed up.
  • Machine learning for making predictions: If you have transactional data of a finance company and need to build a model to determine the future trend, then machine learning algorithms are the best bet. This falls under the paradigm of supervised learning. It is called supervised because you already have the data based on which you can train your machines. For example, a fraud detection model can be trained using a historical record of fraudulent purchases.
  • Machine learning for pattern discovery: If you don’t have the parameters based on which you can make predictions, then you need to find out the hidden patterns within the data set to be able to make meaningful predictions. This is nothing but the unsupervised model as you don’t have any predefined labels for grouping. The most common algorithm used for pattern discovery is Clustering.
    Let’s say you are working in a telephone company and you need to establish a network by putting towers in a region. Then, you can use the clustering technique to find those tower locations which will ensure that all the users receive optimum signal strength.

Let’s see how the proportion of above-described approaches differ for Data Analysis as well as Data Science. As you can see in the image below, Data Analysis includes descriptive analytics and prediction to a certain extent. On the other hand, Data Science is more about Predictive Causal Analytics and Machine Learning.

What is data science – the requisite skill set

Data science is a blend of skills in three major areas:

  • Mathematics Expertise: At the heart of mining data insight and building data product is the ability to view the data through a quantitative lens. There are textures, dimensions, and correlations in data that can be expressed mathematically. Finding solutions utilizing data becomes a brain teaser of heuristics and quantitative technique. Solutions to many business problems involve building analytic models grounded in the hard math, where being able to understand the underlying mechanics of those models is key to success in building them.
  • Technology and Hacking: First, let’s clarify on that we are not talking about hacking as in breaking into computers. We’re referring to the tech programmer subculture meaning of hacking – i.e., creativity and ingenuity in using technical skills to build things and find clever solutions to problems. Core languages associated with data science include SQL, Python, R, and SAS. On the periphery are Java, Scala, Julia, and others. But it is not just knowing language fundamentals. A hacker is a technical ninja, able to creatively navigate their way through technical challenges in order to make their code work.
  • Strong Business Acumen: It is important for a data scientist to be a tactical business consultant. Working so closely with data, data scientists are positioned to learn from data in ways no one else can. That creates the responsibility to translate observations to shared knowledge, and contribute to strategy on how to solve core business problems. This means a core competency of data science is using data to cogently tell a story. No data-puking – rather, present a cohesive narrative of problem and solution, using data insights as supporting pillars, that lead to guidance.


Analytics and Machine learning – how it ties to data science

There are a slew of terms closely related to data science that we hope to add some clarity around.

What is Analytics?

Analytics has risen quickly in popular business lingo over the past several years; the term is used loosely, but generally meant to describe critical thinking that is quantitative in nature. Technically, analytics is the “science of analysis” — put another way, the practice of analyzing information to make decisions.

Is “analytics” the same thing as data science? Depends on context. Sometimes it is synonymous with the definition of data science that we have described, and sometimes it represents something else. A data scientist using raw data to build a predictive algorithm falls into the scope of analytics. At the same time, a non-technical business user interpreting pre-built dashboard reports (e.g. GA) is also in the realm of analytics, but does not cross into the skill set needed in data science. Analytics has come to have fairly broad meaning. At the end of the day, as long as you understand beyond the buzzword level, the exact semantics don’t matter much.

What is Machine Learning?

Machine learning is a term closely associated with data science. It refers to a broad class of methods that revolve around data modeling to (1) algorithmically make predictions, and (2) algorithmically decipher patterns in data.

  • Machine learning for making predictions: Core concept is to use tagged data to train predictive models. Tagged data means observations where ground truth is already known. Training models means automatically characterizing tagged data in ways to predict tags for unknown data points. E.g. a credit card fraud detection model can be trained using a historical record of tagged fraud purchases. The resultant model estimates the likelihood that any new purchase is fraudulent. Common methods for training models range from basic regressions to complex neural nets. All follow the same paradigm known as supervised learning.
  • Machine learning for pattern discovery: Another modeling paradigm known as unsupervised learning tries to surface underlying patterns and associations in data when no existing ground truth is known (i.e. no observations are tagged). Within this broad category of methods, the most commonly used are clustering techniques, which algorithmically detect what are the natural groupings that exist in a data set. For example, clustering can be used to programmatically learn the natural customer segments in a company’s user base. Other unsupervised methods for mining underlying characteristics include: principal component analysis, hidden markov models, topic models, and more.

What is the difference between an analyst and a data scientist?

“Analyst” is somewhat of an ambiguous job title that can represent many different types of roles (data analyst, marketing analyst, operations analyst, financial analyst, etc). What does this mean in comparison to data scientist?

  • Data Scientist: Specialty role with abilities in math, technology, and business acumen. Data scientists work at the raw database level to derive insights and build data product.
  • Analyst: This can mean a lot of things. Common thread is that analysts look at data to try to gain insights. Analysts may interact with data at both the database level or the summarized report level.

Thus, “analyst” and “data scientist” is not exactly synonymous, but also not mutually exclusive. Here is our interpretation of how these job titles map to skills and scope of responsibilities:


For any company that wishes to enhance their business by being more data-driven, data science is the secret sauce. Data science projects can have multiplicative returns on investment, both from guidance through data insight, and development of data product. Though, hiring people who carry this potent mix of different skills is easier said than done. There is simply not enough supply of data scientists in the market to meet the demand (data scientist salary is sky high). Thus, when you manage to hire data scientists, nurture them. Keep them engaged. Give them autonomy to be their own architects in how to solve problems. This sets them up in the company to be highly motivated problem solvers, there to tackle the toughest analytical challenges.