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It’s been said that Data Scientist is the “Sexiest Job of the 21st century”. Why? Because over the past few years, companies have been storing their data. And this being done by each and every company, it has suddenly led to data explosion. Data has become the most abundant thing today.

But, what will you do with this data? Let’s understand this using an example:

Say, you have a company which makes mobile phones. You released your first product, and it became a massive hit. Every technology has a life, right? So, now its time to come up with something new. But you don’t know what should be innovated, so as to meet the expectations of the users, who are eagerly waiting for your next release?

Somebody, in your company comes up with an idea of using the user generated feedback and pick things which we feel users are expecting in the next release.

Comes in Data Science, you apply various data mining techniques like sentiment analysis etc and get the desired results.

It’s not only this, you can make better decisions, you can reduce your production costs by coming out with efficient ways, and give your customers what they actually want!

Data Science is a detailed study of the flow of information from the colossal amounts of data present in an organization’s repository. It involves obtaining meaningful insights from raw and unstructured data which is processed through analytical, programming, and business skills.

A Data Scientist is the master of all trades! He should be proficient in maths, he should be acing the Business field, and should have great Computer Science skills as well. Scared? Don’t be. Though you need to be good in all these fields, but even if you aren’t, you’re not alone! There is no such thing as “a complete data scientist”.

If we talk about working in a corporate environment, the work is distributed among teams, wherein each team has their own expertise. But the thing is, you should be proficient in atleast one of these fields. Also, even if these skills are new to you, chill! It may take time, but these skills can be developed, and believe me it would be worth the time you will be investing. Why?

In a world that is increasingly becoming a digital space, organizations deal with zettabytes and yottabytes of structured and unstructured data every day. Evolving technologies have enabled cost savings and smarter storage spaces to store critical data.

Currently, in the industry, there is a huge need for skilled and certified Data Scientists. They are among the highest-paid professionals in the IT industry. According to Forbes, ‘the best job in America is of a Data Scientist with an average annual salary of $110,000’. Only a few people have the capability to process it and derive valuable insights out of it.

In recent years, there is a huge growth in the field of Internet of Things (IoT), due to which 90 percent of the data has been generated in the current world. Every day, 2.5 quintillion bytes of data are generated, and it is more accelerated with the growth of IoT.

This data comes from all possible sources such as:

Sensors used in shopping malls to gather shoppers’ information
Posts on social media platforms
Digital pictures and videos captured in our phones
Purchase transactions made through e-commerce

This data is known as big data.

Companies are flooded with colossal amounts of data. Thus, it is very important to know what to do with this exploding data and how to utilize it.

It is here, the concept of Data Science comes into the picture. Data Science brings together a lot of skills like statistics, mathematics, and business domain knowledge and helps an organization find ways to:

Reduce costs
Get into new markets
Tap on different demographics
Gauge the effectiveness of a marketing campaign
Launch a new product or service

IT organizations need to address their complex and expanding data environments in order to identify new value sources, exploit opportunities, and grow or optimize themselves, efficiently. Here, the deciding factor for an organization is ‘what value they extract from their data repository using analytics and how well they present it’.

Google

Google is by far the biggest company that is on a hiring spree for trained Data Scientists. Since Google is mostly driven by Data Science, Artificial Intelligence, and Machine Learning these days, it offers one of the best Data Science salaries to its employees.

Amazon

Amazon is a global e-commerce and cloud computing giant that is hiring Data Scientists on a big scale. They need Data Scientists to find out customer mindset and enhance the geographical reach of both e-commerce and cloud domains, among other business-driven goals.

VISA

An online financial gateway for most companies, Visa does transactions worth hundreds and millions in a single day. Due to this, the need for Data Scientists is huge at Visa to generate more revenue, check fraudulent transactions, and customize products and services as per customer requirements, etc.

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.

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.

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.

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.

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.

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.

Hadoop

Hadoop is a framework which helps us to store and process large datasets in parallel and in a distribution fashion.

If we use Hadoop as our storage in Data Science it becomes difficult to process the input with R Studio, due to its inability to perform well in distributed environment, hence we have Spark R.

Spark R

It is an R package, that provides a lightweight way of using Apache Spark with R. Why will you use it over tradition R applications? Because, it provides a distributed data frame implementation that supports operation like selection, filtering, aggregation etc but on large datasets.

Some of the prominent Data Scientist job titles are:

Data Scientist
Data Engineer
Data Architect
Data Administrator
Data Analyst
Business Analyst
Data/Analytics Manager
Business Intelligence Manager