Everything is on Internet! The Internet has a lot of Data! Therefore, everything is Big Data!!

What is Data Science?

Data Science is the art and science of extracting actionable insight from raw data.

Put simply, Data Science is an umbrella term for techniques used when trying to extract insights and information from data.

Data Science uses automated methods to analyze huge amount of data and extract knowledge from them in various forms either structured or unstructured.

Differences

Data is growing faster than ever before and by the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet.

With enormous amount of facts generating each second, the requirement to extract the useful insights is a must for the businesses to stand out from the crowd.

Why Data Science?

Data Science is all about uncovering findings from data. Diving in at a granular level to mine and understand complex behaviors, trends, and inferences.

It’s about surfacing hidden insight that can help enable companies to make smarter business decisions.

  • Netflix data mines movie viewing patterns to understand what drives user interest, and uses that to make decisions on which Netflix original series to produce.
  • Walmart targets customer unique shopping behavior, which helps to guide messaging to different market audiences.
  • E-Commerce Company uses product recommendation engine to end up more purchasing.
  • App based Taxi provider analyse previous data to meet up demand and supply of cab in particular area.
  • Google Maps uses Data Science to predict best routes.
  • Proctor & Gamble utilizes time series models to more clearly understand future demand, which help plan for production levels more optimally.
  • Industries are targeting real time analysis to monetize more customer.

Companies have moved towards digital enterprise and real time environment in the past few years. So, Data Science has becoming the most upcoming field in the 21st century and it has millions of job openings.

Data scientists combine statistics, mathematics, programming, problem-solving, capturing data in ingenious ways, the ability to look at things differently to find patterns, along with the activities of cleansing, preparing, and aligning the data.

Why Data Science as Career?

All of us are contributing to BigData. Data is piling up very fast. We are not even able to process 5 percentage of available data from Social media, manufacturing systems, medical devices, logistic services, and countless others generate petabytes of data on a daily basis.

With a wealth of data available, we are at a point in history, where we can conduct analyses to detect, discover, and, ultimately, better understand the world around us.

Data scientists are responsible for discovering insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals. The data scientist role is becoming increasingly important as businesses rely more heavily on data analytics to drive decision-making and lean on automation and machine learning as core components of their IT strategies.

Dr. Andrew Chamberlain, Glassdoor’s chief economist, told Business Insider. “It’s one of the hottest and fastest growing jobs we’re seeing right now.”

Data science Industry Opportunity

United States of America is considered as the hot spot for the data science and analytics job market. As per PwC, it has been projected that the Data Science and Analytics sector will have the demand of 9L job vacancies in end of 2018.

Differences

The demand of Data Scientists is increasing tremendously in coming years. NASSCOM estimated that there would be 2,50,000 data science professionals job will be available in 2020.

Differences

Data Science in Different Industry

Each industry has its own big data profile for a data scientist to analyze. Here are some of the common responsibilities in different sector.

E-commerce: Now that websites collect more than purchase data, data scientists help e-commerce businesses improve customer service, find trends and develop services or products. Companies are offering real time bank offer, recommending products, fraud detection and many more.

Healthcare: Electronic medical records are now the standard for healthcare facilities, which requires a dedication to big data, security and compliance. Here, data scientists can help improve health services by analyzing patient’s history. Even doctor can connect remotely and monitor patient.

Finance: In the finance industry, data on accounts, credit and debit transactions and similar financial data are vital to a functioning business. But for data scientists in this field, security and compliance, including fraud detection are one of the major concerns.

Government: Lots of data are being hacked from Government websites. Big data helps governments form decisions, support constituents and monitor overall satisfaction. Like the finance sector, security and compliance are a paramount concern for data scientists.

Social networking:  Our life are online now. Social Media are monitoring our likes, dislikes, hobbies, daily activities and monetizing from our data only. Social networking data helps inform targeted advertising, improve customer satisfaction, establish trends in location data and enhance features and services.

Why choose Data Science at LEARNTEK?

We provide 30 Hours on-line session for graduates and experienced professional.

Students here acquire in-depth technical skills in data collection, data transformation and data analysis methods.

They learn how to use and develop business model using suite of tools and technologies that address data capture, processing, storage, transfer, analysis, visualisation, and related concepts.

At the same time, they also acquire extensive business skills by learning how to bring an innovation to the market and developing a successful business model. We try to build up entrepreneurial skills so that our students can excel professionally.

We will help you to provide insight into leading analytic practices, design and lead iterative learning and development cycles, and ultimately produce new and creative analytic solutions that will become part of any business core deliverables.

LEARN DATA SCIENCE

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