What is Data Science? - Great Learning
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What is Data Science?

Reading Time: 7 minutes

Data Science continues to be a hot topic among skilled professionals and organizations that are focusing on collecting data and drawing meaningful insights out of it to aid business growth. A lot of data is an asset to any organization, but only if it is processed efficiently. The need for storage grew multifold when we entered the age of big data. Until 2010, the major focus was towards building a state of the art infrastructure to store this valuable data, that would then be accessed and processed to draw business insights. With frameworks like Hadoop that have taken care of the storage part, the focus has now shifted towards processing this data. Let us see what is data science, and how it fits into the current state of big data and businesses. 

Broadly, Data Science can be defined as the study of data, where it comes from, what it represents, and the ways by which it can be transformed into valuable inputs and resources to create business and IT strategies. 

what is data science graphical representation(Source: datascience@berkeley)

 

Why businesses need Data Science?

We have come a long way from working with small sets of structured data to large mines of unstructured and semi-structured data coming in from various sources. The traditional BI tools fall short when it comes to processing this massive pool of unstructured data. Hence, Data Science comes with more advanced tools to work on large volumes of data coming from different types of sources such as financial logs, multimedia files, marketing forms, sensors and instruments, and text files. 

Mentioned below are relevant use-cases which are also the reasons behind Data Science becoming popular among organizations:

– Data Science has myriad applications in predictive analytics. In the specific case of weather forecasting, data is collected from satellites, radars, ships, and aircraft to build models that can forecast weather and also predict impending natural calamities with great precision. This helps in taking appropriate measures at the right time and avoid maximum possible damage. 

– Product recommendations have never been this precise with the traditional models drawing insights out of browsing history, purchase history, and basic demographic factors. With data science, vast volumes and variety of data can train models better and more effectively to show more precise recommendations.

– Data Science also aids in effective decision making. Self-driving or intelligent cars are a classic example. An intelligent vehicle collects data real-time from its surrounding through different sensors like radars, cameras, and lasers to create a visual (map) of their surroundings. Based on this data and advanced machine learning algorithm, it takes crucial driving decisions like turning, stopping, speeding etc. 

 

What is data science

 

Why you should build a career in Data Science? 

Now that we have seen why businesses need data science in the above section, let’s see why is data science a lucrative career option through this video:

 

Who is a Data Scientist?

A data scientist identifies important questions, collects relevant data from various sources, stores and organizes data, decipher useful information, and finally translates it into business solutions and communicate the findings to affect the business positively. 

Apart from building complex quantitative algorithms and synthesizing a large volume of information, the data scientists are also experienced in communication and leadership skills, which are necessary to drive measurable and tangible results to various business stakeholders. 

 

What is the prerequisite skill sets to Data Science?

Data Science is a field of study which is a confluence of mathematical expertise, strong business acumen, and technology skills. These build the foundation of Data Science and require an in-depth understanding of concepts under each domain. The three requisite skills are elaborated below:

Mathematical Expertise: There is a misconception that Data Analysis is all about statistics. There is no doubt that both classical statistics and Bayesian statistics are very crucial to Data Science, but other concepts are also crucial such as quantitative techniques and specifically linear algebra, which is the support system for many inferential techniques and machine learning algorithms. 

Strong Business Acumen: Data Scientists are the source of deriving useful information that is critical to the business, and are also responsible for sharing this knowledge with the concerned teams and individuals to be applied in business solutions. They are critically positioned to contribute to the business strategy as they have the exposure to data like no one else. Hence, data scientists should have a strong business acumen to be able to fulfil their responsibilities. 

Technology Skills: Data Scientists are required to work with complex algorithms and sophisticated tools. They are also expected to code and prototype quick solutions using one or a set of languages from SQL, Python, R, and SAS, and sometimes Java, Scala, Julia and others. Data Scientists should also be able to navigate their way through technical challenges that might arise and avoid any bottlenecks or roadblocks that might occur due to lack of technical soundness.

 

Other roles in the field of data science:

So far, we have understood what is data science, why businesses need data science, who is a data scientist, and what are the critical skill sets that are required to enter the field of data science. Now, let us look at some other data science job roles apart from that of a data scientist:

– Data Analyst: This role serves as a bridge between business analysts and data scientists. They work on specific questions and find results by organizing and analyzing the given data. They translate technical analysis to action items and communicate these results to concerned stakeholders. Along with programming and mathematical skills, they also require data wrangling and data visualization skills. 

– Data Engineer: The role of a data engineer is to manage large amounts of rapidly changing data. They manage data pipelines and infrastructure to transform and transfer data to respective data scientists to work on. They majorly work with Java, Scala, MongoDB, Cassandra DB, and Apache Hadoop. 

 

Data Science Salary trends across job roles:

what is data science

(Source: Analytics India Magazine – Salary Study 2019)

 

Who can become a data scientist/analyst/engineer?

Data Science is a multidisciplinary subject and it is a big misconception that one needs to have a PhD in science or mathematics to become a data science professional. Although a good academic background is a plus when it comes to data science profession, it is certainly not an eligibility criterion. Anyone with a basic educational background and an intellectual curiosity towards the subject matter can become a data scientist. 

 

Critical tools in Data Science Domain:

SAS – It is specifically designed for operations and is a closed source proprietary software used majorly by large organizations to analyze data. It uses the base SAS programming language which is generally used for performing statistical modelling. It also offers various statistical libraries and tools that are used by data scientists for data modelling and organising. 

Apache Spark – This tool is an improved alternative of Hadoop and functions 100 times faster than MapReduce. Spark is designed specifically to manage batch processing and stream processing. Several Machine Learning APIs in Spark help data scientists to make accurate and powerful predictions with given data. It is a highly superior tool than other big-data platforms as it can process real-time data, unlike other analytical tools which are only able to process batches of historical data.

BigML – BigML provides a standardized software using cloud computing, and a fully interactable GUI environment that could be used for processing ML algorithms across various departments of the organization. It is easy to use and allows interactive data visualizations. It also facilitates the export of visual charts to mobile or IoT devices. BigML also comes with various automation methods that aid the tuning of hyperparameter models and help in automating the workflow of reusable scripts. 

D3.js – D3.js is a javascript library that makes it possible for the user to create interactive visualizations and data analysis on their web browser with the help of its several APIs. It can make documents dynamic by allowing updates on the client-side, it actively uses the change in data to reflect visualization on the browser. 

MATLAB – It is a numerical computing environment that can process complex mathematical operations. It has a powerful graphics library to create great visualizations that help aid image and signal processing applications. It is a popular tool among data scientists as it can help with multiple problems ranging from data cleaning and analysis to much advanced deep learning problems. It can be easily integrated with enterprise applications and other embedded systems. 

Tableau – It is a Data Visualization software that helps in creating interactive visualizations with its powerful graphics. It is suited best for the industries working on business intelligence projects. Tableau can easily interface with spreadsheets, databases, and OLAP (Online Analytical Processing) cubes. It sees a great application in visualizing geographical data. 

Matplotlib – Matplotlib is developed for Python and is a plotting and visualization library used for generating graphs with the analyzed data. It is a powerful tool to plot complex graphs by putting together some simple lines of code. The most widely used module of the many matplotlib modules is the Pyplot. It is an open-source module that has a MATLAB-like interface and is a good alternative to MATLAB’s graphics modules. NASA’s data visualizations of Phoenix Spacecraft’s landing were illustrated using Matplotlib.

NLTK – It is a collection of libraries in Python called Natural Language Processing Toolkit. It helps in building the statistical models that along with several algorithms can help machines understand human language. 

Scikit-learn – It is a tool that makes complex ML algorithm simpler to use. A variety of Machine Learning features such as data pre-processing, regression, classification, clustering, etc. are supported by Scikit-learn making it easy to use complex ML algorithms. 

TensorFlow – TensorFlow is again used for Machine Learning, but more advanced algorithms such as deep learning. Due to the high processing ability of TensorFlow, it finds a variety of applications in image classification, speech recognition, drug discovery, etc. 

If you are interested in pursuing a career in Data Science, check out Great Learning’s postgraduate program in Data Science and Business Analytics. The Analytics and Data Science course from Great Learning has been ranked No.1 consistently since 2014 by Analytics India Magazine. The program provides international recognition and dual certificate from the University of Texas at Austin, McCombs School of Business and Great Lakes, India. You will get to learn from the top-ranked data science faculty along with the flexibility to learn at your time and space with the online learning and weekend classes format.

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