Hertha  Mayer

Hertha Mayer


Data Science Makes Analytics Easy

The analysis is considered an important component of the planning process. We do analysis before we make any decision whatsoever. It’s not like we never think about it. Some amount of analysis goes into everything we do. Similarly big corporate giants need analysis for planning and proper decision making. And when you are talking about millions of data instances, we need this process to be feasible as well. This is where data science comes to rescue.

Data Science as a whole can be seen as important tools and procedures that are used to convert raw data instances into something that is easy to understand and decipher. Why do we want to be easy to understand? Well, the answer is easy. It reduces the time and effort in planning as they can now actually focus on what to do next rather than sitting and pondering over the data trends and behaviour. Data Science teaches us the conversion and processing of data in such a way so that the output clearly depicts the different behaviours and trends that were previously hidden in it. That’s why people who have appropriate experience in this are called data science professionals or data scientists.

A Data Science professional has lots of tasks at his hands. Most importantly, he has to work according to the client’s needs irrespective of what he thinks. They have to start with gathering the required input data sets with the help of mining or other means. These raw data sets that are collected are then analysed and then appropriate tools and procedures are applied to them so as to make them as per the needs. The output is then a more refined and processed form of data that suits the clientele and their requirements.

Data Science courses have really lots of stuff in them. Most of them are actually very important topics which really are needed for mastering data science. There are many theoretical concepts such as that of linear and logistic regression, regression models, statistical inference theories, Data extraction, handling, wrangling et cetera. It includes languages and tools like R and R studio, Hadoop, Spark, PROC SQL et cetera. These tools and languages are very necessary for adequate data handling and management.

These Data courses aim for the perfection of the student in at least this sector alone. They have devised a great curriculum along with a capstone project at the end to judge yourselves of the things you learned. Data science training in Hyderabad has lots of prospects regarding good jobs. There is a scarcity of data science professionals out there and that means you can easily use these certified program courses to actually strike one of such jobs in these big companies. This actually shows that data analytics can actually become easy with the help of data science and also that this can actually be great for a career as well.

So you want to jump-start your career in fields related to data science but do not know where to start. Fear not, you are not alone, and help is available.

We have had a data explosion

A lot of people say that the sexiest job to have in the twenty-first century is to be a data scientist. Why is that? The reason for this is that companies have been accumulating a lot of data in the last few years. This phenomenon is not only true for big companies, even medium and small size companies have a lot of data. It is so much in volume that some people refer to it as a data explosion. If anything is in a lot of abundance today, it is data.

Data Science Enters

Now you have all this data, but what is it good for? Let us take an example to get a better understanding. Let us take the example of a mobile phone manufacturing company. The company put its first product in the market and it became hugely popular and a big hit. Now, as we all know every technology is popular only for a certain time and then it fades away as something else comes in. So, the company now has to think of something new to put on the market. The problem is that the company does not know what innovation they should undertake that will meet market expectations. The company has already created a market for itself, but now they have to satisfy this market’s hunger for new products. Someone in the company gets an idea of using feedback from the users on what they would like to see in the next upcoming release. This is where data science enters.

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Data Science Makes Analytics Easy
Uriah  Dietrich

Uriah Dietrich


How To Build A Data Science Career In 2021

For this week’s data science career interview, we got in touch with Dr Suman Sanyal, Associate Professor of Computer Science and Engineering at NIIT University. In this interview, Dr Sanyal shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.

With industry-linkage, technology and research-driven seamless education, NIIT University has been recognised for addressing the growing demand for data science experts worldwide with its industry-ready courses. The university has recently introduced B.Tech in Data Science course, which aims to deploy data sets models to solve real-world problems. The programme provides industry-academic synergy for the students to establish careers in data science, artificial intelligence and machine learning.

“Students with skills that are aligned to new-age technology will be of huge value. The industry today wants young, ambitious students who have the know-how on how to get things done,” Sanyal said.

#careers # #data science aspirant #data science career #data science career intervie #data science education #data science education marke #data science jobs #niit university data science

 iOS App Dev

iOS App Dev


Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

'Commoditization Is The Biggest Problem In Data Science Education'

The buzz around data science has sent many youngsters and professionals on an upskill/reskilling spree. Prof. Raghunathan Rengasamy, the acting head of Robert Bosch Centre for Data Science and AI, IIT Madras, believes data science knowledge will soon become a necessity.

IIT Madras has been one of India’s prestigious universities offering numerous courses in data science, machine learning, and artificial intelligence in partnership with many edtech startups. For this week’s data science career interview, Analytics India Magazine spoke to Prof. Rengasamy to understand his views on the data science education market.

With more than 15 years of experience, Prof. Rengasamy is currently heading RBCDSAI-IIT Madras and teaching at the department of chemical engineering. He has co-authored a series of review articles on condition monitoring and fault detection and diagnosis. He has also been the recipient of the Young Engineer Award for the year 2000 by the Indian National Academy of Engineering (INAE) for outstanding engineers under the age of 32.

Of late, Rengaswamy has been working on engineering applications of artificial intelligence and computational microfluidics. His research work has also led to the formation of a startup, SysEng LLC, in the US, funded through an NSF STTR grant.

#people #data science aspirants #data science course director interview #data science courses #data science education #data science education market #data science interview

Ananya Gupta

Ananya Gupta


What Are The Advantages and Disadvantages of Data Science?

Data Science becomes an important part of today industry. It use for transforming business data into assets that help organizations improve revenue, seize business opportunities, improve customer experience, reduce costs, and more. Data science became the trending course to learn in the industries these days.

Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction. In online Data science course you learn how Data Science deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions.

Advantages of Data Science:- In today’s world, data is being generated at an alarming rate in all time lots of data is generated; from the users of social networking site, or from the calls that one makes, or the data which is being generated from different business. Because of that reason the huge amount of data the value of the field of Data Science has many advantages.

Some Of The Advantages Are Mentioned Below:-

Multiple Job Options :- Because of its high demand it provides large number of career opportunities in its various fields like Data Scientist, Data Analyst, Research Analyst, Business Analyst, Analytics Manager, Big Data Engineer, etc.

Business benefits: - By Data Science Online Course you learn how data science helps organizations knowing how and when their products sell well and that’s why the products are delivered always to the right place and right time. Faster and better decisions are taken by the organization to improve efficiency and earn higher profits.

Highly Paid jobs and career opportunities: - As Data Scientist continues working in that profile and the salaries of different position are grand. According to a Dice Salary Survey, the annual average salary of a Data Scientist $106,000 per year as we consider data.

Hiring Benefits:- If you have skills then don’t worry this comparatively easier to sort data and look for best of candidates for an organization. Big Data and data mining have made processing and selection of CVs, aptitude tests and games easier for the recruitment group.

Also Read: How Data Science Programs Become The Reason Of Your Success

Disadvantages of Data Science: - If there are pros then cons also so here we discuss both pros and cons which make you easy to choose Data Science Course without any doubts. Let’s check some of the disadvantages of Data Science:-

Data Privacy: - As we know Data is used to increase the productivity and the revenue of industry by making game-changing business decisions. But the information or the insights obtained from the data may be misused against any organization.

Cost:- The tools used for data science and analytics can cost tons to a corporation as a number of the tools are complex and need the people to undergo a knowledge Science training to use them. Also, it’s very difficult to pick the right tools consistent with the circumstances because their selection is predicated on the proper knowledge of the tools also as their accuracy in analyzing the info and extracting information.

#data science training in noida #data science training in delhi #data science online training #data science online course #data science course #data science training

Sasha  Lee

Sasha Lee


Data Analytics vs Data Science: What Better Suits your Needs?

Analytics Insight takes you through the basics of data analytics vs data science to give a broad outlook.

What makes the 21st century different from the 20th century? Just 21 years have beaten the past hundred years. Yes, the main driver of this shift is the debut of data. Big data has become a major component of everyday life, thanks to the actionable insights and results in which businesses can glean. From big data emerged the big two trends: Data analytics and data scienceData analytics vs data science, the two sides of the technology are fighting over dominance. Even though both are very important trends in the digital sphere, people can’t choose both at a time.

The evolution of big data has moved out of the technology sector long back. Today it is everywhere. Without the help of big data, almost all the industries will get jeopardized. The World Economic Forum stated that by the end of 2020, the daily global data generation will reach 44 zettabytes, and it will further surge to 463 exabytes in 2025. However, the creation of such large datasets also requires understanding and having proper tools on hand to parse through them to uncover the right information. They can’t be directly used in any sector.

Data undergoes many routine processes before it is used effectively in an organization. To better comprehend big data, the fields of data science and data analytics have escalated their stance. The duo technologies have moved out of the academic, to instead become a core element of business intelligence and big data analytics tools. But the war between data analytics vs data science is still on. While some organizations can afford to choose both data analytics and data science for their routine functionalities, some others can’t do that. When people are bound to select and support one of the technologies, the conflict breaks out. Picking a career option between these two is also a pain point. Henceforth, this article will take you through the basics of data analytics vs data science and let you know which one will better suit your strategy.


Data analytics: Data analytics is the concept of processing and performing statistical analysis of existing datasets. It is seen as the initial step that analysts create to capture, process, and organize data to uncover actionable insights for business problems. In a nutshell, data analytics provides an answer for complicated data-based or data-related questions which could lead to immediate improvement. It also encompasses a few different branches of broader statistics and analysis which help combine a diverse source of data and locate connections while simplifying the results. Some of the main purposes why the technology is leveraged are listed below,

  • By assessing a company’s historical revenue, sales, and costs with its goals, an analyst could identify the budget and investments required to make those goals a reality.
  • A data analyst can make cost-effective recommendations to help mitigate business risks.
  • With the help of data, marketing analysts can identify the number of leads their efforts must generate to fill the sales pipeline.

Data science: Data science is a versatile and multidisciplinary field focused on finding actionable insights from large sets of raw and structured data. The technology unearths answers for complicated business questions. Data scientists use several different techniques to obtain answers, incorporating computer science, predictive analytics, statistics, and machine learning to parse through massive datasets in an effort to establish solutions to problems that haven’t been thought of yet. Some of the reasons to pick data science are listed below,

  • Data scientists identify and avoid mistakes that commonly arise while interpreting datasets, metrics, and visualization.
  • The technology embraces data-driven decision-making and ensures that the business decisions are backed by numbers.
  • Data science understands the market size, buyer trends, competition, and opportunities, and risks your business faces.

Data analytics: Data analysts are expected to show much importance to a single or a couple of topics and reflect on them with data. Some of the other skills are,

  • Data wrangling
  • Understand PIG/HIVE
  • Fluent understanding of R and Python
  • Knowledge of mathematical statistics

Data science: Since data science revolves around analytics, programming, and domain knowledge, the professionals are expected to be experts in the three departments. Some of the other mandatory skills are,

  • Hands-on experience in SQL database coding
  • Strong knowledge of Python, SAS, R, Scala, etc
  • Understanding multiple functions
  • Mandatory knowledge about machine learning

#data science #latest news #data analytics vs data science #data science #data analytics