Data Scientist – Is it the Hottest Job in Demand?

Data Scientist – Roles and Responsibilities..

Tweeting is trending! It has emerged as one of the latest manifestations of the human need to socialise, to acknowledge and be acknowledged. But have you ever wondered while tweeting about what importance does your tweet hold? It may be negative or positive carrying multiple meanings, influencing the sales of a company, or its reputation, but for sure, the data collected in the form of your tweets is largely unstructured or semi-structured in its nature. It is in here that a Data Scientist comes in!

A Data Scientist, working in the Twitter Analytics Domain, extracts meaningful data out of thousands of tweets, performs sentiment analysis on the streamlined data, and predicts the patterns of behaviour, interactions, and associations between people which may directly or indirectly influence an organisation and its prosperity. As a matter of fact, the role of a Data Scientist is to analyse, manage, and streamline complicated data sets, and to devise certain tools that enhance information flow to the respective organisations for their business benefits. The task is largely predictive and analytical in its nature.

Multiple Roles in the Data Science Domain.. 

Data Scientist – The Hottest and the Highest paid job across the Globe with an Average Salary of $110,000.

A Data Scientist, with his expertise in Mathematics, Statistics, and Programming, performs to ease out the intricacies behind any data, in order to come up with simplified business strategies which the BIG business houses may not be aware of. A Data Scientist applies the knowledge of statistics, algorithms, and mathematics to find out various predictive models to solve a business problem. In doing so, a Data Scientist analyses and predicts the possibilities that may occur in the near future. World-wide business houses, such as Amazon, hire Data Scientists whose roles are to create predictive models based on the ratings and likes of products, and to make recommendations accordingly.

Data Engineer – Trending with an average salary of $90,000

Data Engineers are usually software professionals having in-depth understanding of programming languages, warehousing solutions such as SQL and NoSQL, and frameworks such as Spark and Scala, and Hadoop. Their area of expertise is coding and programming. They co-ordinate with the data scientists in order to process, manage, and clean up the data sets. They process the predictive models designed by the data scientists and implement those in code. With a steep rise in e-commerce industry, there occurs a high-demand of Data Engineers.

Data Analysts – Future Calling… An average salary of $65,000

With a base skill set comprising of Business Knowledge and Statistics, a Data Analyst makes data accessible in the form of charts and reports. Business Analysts are Data Analysts who try to understand the given data and try to figure out the best business policies for their companies. Being stationed at the entry-level in the domain of Data Science, Data Analysts have promising careers ahead with handsome salaries in their pockets.

Data Science and its Future?

“By 2018, the U.S. alone may face a 50 percent to 60 percent gap between supply and requisite demand of deep analytic talent” – A Study by McKinsey

As per global research, Data Scientists are in great demand, owing to the widened business domain of the e-commerce industry. Their requirement is equally high in other industries such as aviation and aerospace industry, and stock market exchange where they work to analyse and predict flights, and to study the ebb and flow of the stock markets. With its increased demand and highest paid jobs, Data Science is emerging as one of the most exciting domains for career. It is indeed the “hottest career” to dream of and to follow!

One can certainly pursue one’s dream job, for which one requires to undergo hands-on training in Data Science. ETLhive organises extensive training lectures on various concepts of Data Science such as Data Science and Machine Learning, Data Manipulation using R, Machine Learning Techniques Using R, Supervised Learning Techniques and the implementation of various Algorithms, Unsupervised Machine Learning Techniques – Implementation of different algorithms, Regression Methods for Forecasting Numeric Data, and Deep Learning – Neural Networks and Support Vector Machines. During the course the highly-qualified industry-experienced training Professionals at ETLhive impart knowledge on all such concepts and skills associated with Data Science. Get trained at ETLhive and get hired for the hottest job of the century, and become a glamorous and knowledgeable Data Scientist!

 SEPTEMBER 29, 2016

What is Machine Learning

Machine learning is a function of artificial intelligence (AI) Which provides computers the intelligence to automatically learn and develop skills from experience without being explicitly programmed. Machine learning aims at the improvement of computer programs which can access data and use it learn for themselves.

Future of Machine Learning

Most applications will include machine learning.

In only 3-5 years, machine learning will play an important role and become part of almost every software application. Engineers will even insert this efficiency directly into our systems. Think of how good your TV streaming service knows that what to suggest to the user. Expect this level of personalization to become ubiquitous and improve the customer experience everywhere.

Machine learning as a service will become more common.

As machine learning will become more and more valuable and the technology will bloom, almost every enterprise will start using the cloud to deliver machine learning as a service (MLaaS).

This will usher a bigger range of organizations to take advantages of machine learning without making large hardware capital or training their own algorithms.

Systems will get really good at talking like humans using Machine learning.

Before the machine learning, systems were facing a very hard time to understand even simple human language. Machine learning helps computers understand the context and meaning of sentences much better through natural language processing (NLP). As the technology improves, solutions such as IBM Watson Assistant will learn to communicate seamlessly without using code.

Algorithms will constantly retrain.

Currently, most machine learning systems train only once. On that initial training, the systems will then locate any new data or problems in the system. Over time, the training information often becomes dated or imperfect. In a few years, many machine learning systems will be connected to the internet and constantly retrain on the most relevant information on the internet.

Specialized hardware will deliver performance breakthroughs.

Traditional CPUs only had finite success running machine learning systems. GPUs, however, have an advantage in running these algorithms because they have a large number of simple cores. Artificial intelligence (AI) experts are also using field-programmable gate arrays (FPGAs) for machine learning. At times, FPGAs can even outperform GPUs.

As this technology advances, more businesses will embrace the AI revolution.

Conclusion:

Machine Learning is an emerging technology which has widespread benefits. To learn more about this technology and about how to leverage it for your job or business, contact us at etlhive.com