What Makes Data Science the Ideal Choice

Data Science Training Chennai

Data science is the buzzword today in the IT industry, and it has every reason to be son. A data science training is your gateway to a promising and well-paying career. At the core of any business enterprise lies troves of raw data that needs to be explored and deduced to arrive at strategic solutions. Data science is about using this data in creative ways to generate business value. Here are some reasons that makes data science an ideal choice:

  1. Hottest job – Harvard Business Review has voted Data Science as the hottest job of the 21st century. Data science has the potential of adding tremendous value to business by its data wrangling abilities. Over the decade, the consumption of information online has shot up remarkably and has led to a stage where all our basic activities are carried out online. With so much data produced every day, data science is the field that can help businesses uncover crucial business data and set them on track.
  2. Demand – There is a huge demand for data scientists today. The US leads the market with a total of 190,000 posts to be created by 2019.  India follows close behind with 1,00,000 posts across various industries. By 2025, the Big Data analytics sector in India is estimated to grow eightfold, reaching $16 billion.
  3. It’s all about data – this job is all about manipulating massive chunks of data, processing and analyzing them for meaningful information that can help businesses get insights on concerns, customer experience, supply-chain and other prime aspects that would complement their business operations.
  4. Preparing the data for dissection – Before you get down to the analytics, you will have to clean and sort the data appropriately. The data that is accumulated from sources is raw and unsorted. This usually takes more time than the analysis.
  5. Skills – Data science calls for skills in statistics, algorithm building and a dash of business acumen. It is a multidisciplinary field.
  6. Qualifications – A PhD is not an absolute necessity. But the fundamentals of analytics is crucial. You need to be capable of working on analytics tools and understand the basics of data processing to get started.
  7. Coding is key – Programming languages like  SAS/R, Python coding, SQL database and even Hadoop are used to write algorithms in data science. R is one of the most popular programming languages here without which you can’t learn data science.
  8. Well-paying – Data scientists earn more than the  IT employees. The average salary can range anything from rupees 2,00,000 to 3,50,000 per annum.
  9. Career prospects – Startups as well as tech companies are vying to hire the best data scientists. Corporates and tech companies are reinvesting on analytics and data scientists. One of the biggest causes of tech companies laying off employees is the massive difference between the evolving technology and the lack of manpower to work on it. Data science requires niche skills and only the talent pool that has failed to upskill to the in-demand skills has been laid off.
  10. Classification of analytics – Analytics is classified into three broad categories – descriptive analytics, predictive analytics and prescriptive analytics. Descriptive analytics describes the pieces of information uncovered. Predictive analytics is the forecast that is obtained from the historical data. Prescriptive analytics is to ward off an ominous situation or make use of a promising opportunity.
  11. Classification of data – Not all data gathered is of use. Dark data is data that can never offer meaningful insight. The rest is classified as structured and unstructured data. Structured data is the data that can be categorized, segmented and stored. Unstructured is the one that cannot be segmented.
  12. Collaboration with machine learning – Data science and machine learning are a winning combination. Machine Learning refers to the application of artificial intelligence to make computers learn and respond to situations by learning from data fed to them. Machine learning requires you to master crucial algorithms. Some of the algorithms include Random Forest, Neural Networks, SVM, Logistic Regression and more.
  13. Collaboration with IoT –  IoT refers to the ecosystem of devices connected to each other via the internet. Data science is very closely associated with IoT because IoT is all about data generation and Data Science is about analysing it.
  14. Do not ignore communication – Apart from technical skills, a career in data science requires excellent communication skills. You should be able to express your ideas clearly and be skilled with presentations, spreadsheets and documents.
  15. Rewarding career – Data Science is a rewarding career. At the time when employees are getting pink slips, pay cuts, and laid off, it is one field that’s welcoming talent. You will not just have an authoritative role in your business or organization but receive good paychecks and enjoy a perfect work-life balance.

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