For a better understanding of ‘What is Data Science?’, let’s explore its life cycle. Suppose, Mr. X is the owner of a retail store and his goal is to improve the sales of his store by identifying the drivers of sales. To accomplish the goal, he needs to answer the following questions:

• Which are the most profitable products in the store?
• How are the in-store promotions working?
• Are the product placements effectively deployed?

His primary aim is to answer these questions which would surely influence the outcome of the project. Hence, he appoints you as a Data Scientist. Let’s solve this problem using the Data Science life cycle.

Data Discovery

The first phase in the Data Science life cycle is data discovery for any Data Science problem. It includes ways to discover data from various sources which could be in an unstructured format like videos or images or in a structured format like in text files, or it could be from relational database systems. Organizations are also peeping into customer social media data, and the like, to understand customer mindset better.

In this stage, as a Data Scientist, our objective would be to boost the sales of Mr. X’s retail store. Here, factors affecting the sales could be:

• Store location
• Staff
• Working hours
• Promotions
• Product placement
• Product pricing
• Competitors’ location and promotions, and so on

Keeping these factors in mind, we would develop clarity on the data and procure this data for our analysis. At the end of this stage, we would collect all data that pertain to the elements listed above.

Data Preparation

Once the data discovery phase is completed, the next stage is data preparation. It includes converting disparate data into a common format in order to work with it seamlessly. This process involves collecting clean data subsets and inserting suitable defaults, and it can also involve more complex methods like identifying missing values by modeling, and so on. Once the data cleaning is done, the next step is to integrate and create a conclusion from the dataset for analysis. This involves the integration of data which includes merging two or more tables of the same objects, but storing different information, or summarizing fields in a table using aggregation. Here, we would also try to explore and understand what patterns and values our datasets have.

Do you know, all Data Science projects have certain mathematical models driving them. These models are planned and built by the Data Scientists in order to suit the specific need of the business organization. This might involve various areas of the mathematical domain including statistics, logistic and linear regression, differential and integral calculus, etc. Various tools and apparatus used in this regard could be R statistical computing tools, Python programming language, SAS advanced analytical tools, SQL, and various data visualization tools like Tableau and QlikView.

Also, to generate a satisfactory result, one model might not be enough. We need to use two or more models. In this scenario, a Data scientist will create a group of models. After measuring the models, he/she will revise the parameters and fine-tune them for the next modeling run. This process will continue until the Data Scientist is pretty sure that he/she has found the best model.

In this stage, as a Data Scientist, you will build mathematical models based on the business needs of Mr. X, i.e., based on if product A or product B is the most profitable in the store, whether the product placements are effectively working in the store, etc.

Getting Things in Action

Once the data is prepared and the models are built, it is time to get these models working in order to achieve the desired results. There might be various discrepancies and a lot of troubleshooting that might be needed, and thus the model might have to be tweaked. Here, model evaluation explains the performance of the model.

In this stage, you as a Data Scientist will gather information and derive outcomes based on the business requirements of Mr. X.

Communication

Communicating the findings is the last but not the least step in a Data Science endeavor. In this stage, the Data Scientist needs to be a liaison between various teams and should be able to seamlessly communicate his findings to key stakeholders and decision-makers in the organization so that actions can be taken based on the recommendations of the Data Scientist.

In our example, based on the findings, you will communicate and recommend certain changes in the business strategy so that Mr. X can earn the maximum profit.

Data Science Components

Now, in this ‘What is Data Science?’ , we will discuss some of the key components of Data Science, which are:

• Data (and Its Various Types)

The raw dataset is the foundation of Data Science, and it can be of various types like structured data (mostly in a tabular form) and unstructured data (images, videos, emails, PDF files, etc.)

• Programming (Python and R)

Data management and analysis is done by computer programming. In Data Science, two programming languages are most popular: Python and R.

• Statistics and Probability

Data is manipulated to extract information out of it. The mathematical foundation of Data Science is statistics and probability. Without having a clear knowledge of statistics and probability, there is a high possibility of misinterpreting data and reaching at incorrect conclusions. That’s the reason why statistics and probability play a crucial role in Data Science.

• Machine Learning

As a Data Scientist, every day, you will be using Machine Learning algorithms such as regression and classification methods. It is very important for a Data Scientist to know Machine learning as a part of their job so that they can predict valuable insights from available data.

• Big Data

In the current world, raw data is compared with crude oil, and the way we extract refined oil from the crude oil, by applying Data Science, we can extract different kinds of information from raw data. Different tools used by Data Scientists to process big data are Java, Hadoop, R, Pig, Apache Spark, etc.