Data science is evolving as one of the most promising career paths for skilled professionals. Successful data professionals make advances past the traditional skills of data mining, analyzing large amounts of data, and programming skills.

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The image presents the five stages of the data science life cycle: Capture (data acquisition, signal reception, data entry, data extraction);  Process ( clustering/classification, data modelling, data mining, data summarization); Maintain (data staging, data warehousing, data cleansing, data processing, data architecture); Analyze (exploratory/confirmatory, regression, predictive analysis, text mining, qualitative analysis); Communicate (data reporting,  decision making, data visualization, business intelligence).

The storage of large data was a problem until 2010 when the main focus was to build frames and solutions to store data. However, now Hadoop and other frameworks are now available to solve the problem of storing large data. Now the focus has been shifted to the processing of this data.

Data Science is the secret recipe to this problem while still even today many of the people are unaware of this field. It can prove to be very helpful in various business propositions. It was in 2012 when Harvard Business Review called it “The Sexiest Job of the 21st Century”, it became a buzzword. It is often used interchangeably with concepts like Business Analytics, Business Intelligence, Predictive Modeling, and Statistics. While many universities now offer a data science degree, there exists no defined curriculum to that.

According to the official definition, Data Science is a multidisciplinary tool that uses scientific methods, processes, machine learning principles and algorithms to extract knowledge and discover patterns from the raw data. Therefore, data science is a blend of statistics, mathematics, information science, and computer science.

Why is there a need for Data Science?

As per the data trends, by 2020 most of the data will be unstructured. Data is generated from different sources like financial logs, text files, multimedia forms, sensors, instruments etc. Simple BI tools are not capable of processing large files; therefore we need more advanced analytical tools for processing, analyzing and drawing meaningful results from them. It is all about extracting meaningful insights from the hidden data to draw smarter and beneficial decisions for various businesses.

Domains where Data Science can be used: 

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Data Science can be used to various domains as an analytical, predictive and forecasting tool like in social media, marketing, automation, healthcare, Travel, weather forecasting, self-driven cars, travel, credit and insurance, sales and many others.

Role and uses of Data Science: 

a)    Predictive casual analytics: 

Data Science can predict a particular event in the future. For example, making the prediction of the time of future credit payments by analyzing the history of the payments can be done using data science.

b)    Prescriptive analytics: 

This role of data science is making its own decisions and providing advice through artificial intelligence and the ability to modify it with certain parameters. This field provides advice to stakeholders. That means, Data Science not only predicts but also suggests a range of actions for profitable outcomes.

One of the most popular examples of the use of data science is Google’s self-driving car to make decisions to take the path, take turns, increase speed or apply brakes. 

c)    Machine learning algorithms for making predictions:

This comes under the umbrella of supervised learning. If you already have the data, you can train your machines for the better results. For example, a fraud detection model can be made using historical fraudulent purchases. 

d)    Machine learning for pattern discovery:
 

 It is about making predictions to find out hidden patterns when you don’t have parameters to predict the result.  It is an unsupervised model in which clustering is used as an algorithm for pattern discovery. For example, if you need to build a network of mobile towers, you can use clustering techniques to find those tower locations which will ensure that all the users receive optimum signal strength.

How is Data Science different from Data Analytics?

Data Analytics includes descriptive analytics and prediction, whereas, Data Science includes more Predictive Causal Analytics and Machine Learning.

How is Data Science different from Business Intelligence?

• In Business Intelligence (BI) the data sources are structured whereas, in Data Science it can be both structured and unstructured.
• BI uses statistics and visualization whereas, Data Science will involve all fields like Statistics, Machine learning, Graph Analysis, Neuro-Linguistic programming etc.
• The focus of BI is past and present whereas, the focus of Data Science is present and future.
• BI uses tools like Pentaho. Microsoft BI etc whereas, data Science uses tools like Rapid Miner, Weka, R etc.

How is Data Science different from Statistics?

Data Science is a huge applied field which includes open science and theories and methodologies of statistics.

Conclusion: 

Today, Data Science is one of the most popular and news-making fields. It involves a wide range of applications. Multiple courses are being offered by universities, colleges, and private institutes nowadays to get you on a path of building your career in Data Science. If you think you can pull off logics and numbers, it would be a great choice to stage your bright career. 

Read our next article to know the prerequisite of becoming a Data Scientist