1597005780

Mastering Mathematics is the most important step in the long journey to be a professional data scientist.

Behind many of the standard data models and structures in data science, there is mathematics that makes them work. It is important to understand the mathematical equations and relations to be a successful data scientist. Actually, in each step of data science, you will touch a wide range of mathematical concepts and approaches and see how they arise in data science.

In this article, I want to encourage you to get familiar with some of the most important and applied subjects of mathematics, and in the next articles, I will give you more information on this mathematical sub-subjects to help you be a self-confident and effective data scientist.

There are many sub-subjects of mathematics that are related in many phases of the data science life cycle. I want to mention some of the most important ones here.

Much of the progress made in mathematics since the 17th century was in the account of differentials and integrals, which were properties of a real number and a function of this set.

The study of these numerous collections led to the emergence of the concepts of coherence and derivation, and for this reason, this mathematics is called continuous mathematics. But in contrast to this kind of mathematics, there are other concepts in mathematics that can be defined on finite sets and yours. The set of these mathematical concepts is called discrete mathematics.

Discrete mathematics has grown in recent years due to the advancement of computer science. The most important topics in discrete mathematics are combinatorics, graph theory, mathematical logic, set theory, and preliminary number theory.

Combinatorics is an area of mathematics primarily concerned with counting, both as a means and an end in obtaining results, and certain properties of finite structures. It is closely related to many other areas of mathematics and has many applications ranging from logic to statistical physics, from evolutionary biology to computer science, etc. I recommend starting with an introduction to combinatorics, which basics of this topic are critical for anyone working in Data Science.

In mathematics and computer science, graph theory is the study of graphs. A graph is a set of vertices connected by edges. In simpler terms, a set of points connected by lines is called a graph. The concept of the graph was proposed by Euler in 1736 with a solution to the Konigsberg bridge problem and was gradually developed. Graphs are widely used in data science today.

Graphs can be found everywhere around us. One of the most well-known types of graphs in data science are the graphs of social networks.

#mathematics #mathematics-education #data-science #data analysis

1618449987

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

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**Introduction and Hypothesis**

I loved to work as a scientist. There is a deep feeling of completion and happiness when you manage to answer ** why**. Finding out

So, back to our **_why. _**In science, in order to answer your why, you will introduce the whole context surrounding it and then formulate an hypothesis. “The timing of the diapause in copepods is regulated through their respiration, ammonia excretion and water column temperature”. Behaviour of subject is the result of internal and external processes.

In marketing, you would have to formulate similar hypothesis in order to start your investigation: “3-days old users un-suscribes due to the lack of direct path towards the check-out”. Behaviour of subject is the result of internal (frustration) and external (not optimized UE/UI) processes.

Although I would have wanted to put that part at the end, as for any scientific paper, it goes without saying that your introduction would present the current ideas, results, and hypotheses of your field of research. So, as a researcher, you need to accumulate knowledge about your subject, and you go looking for scientific articles. The same is true for techs as well. There are plenty of scientific and non-scientific resources out-there that will allow you to better understand, interpret and improve your product. Take this article, for instance, Medium is a wonderful base of knowledge on so many topics! But you could also find passionating articles on PloS One on Users Experience or Marketing Design and etc.

2. **Material and Methods**

As a Marine biologist and later an Oceanographer, I took great pleasure to go at the field and collect data (platyhelminths, fish counts, zooplankton , etc…). Then we needed to translate the living “data” into numeric data. In the technological industry, it is the same idea. Instead of nets, quadrats, and terrain coverage, you will setup tracking event, collect postbacks from your partners and pull third-parties data. The idea is the same, “how do I get the information that will help me answer my why”. So a field sampling mission and a data collection planning have a lot in common.

#ai #data-science #science #tech #data science #from science

1597005780

Mastering Mathematics is the most important step in the long journey to be a professional data scientist.

Behind many of the standard data models and structures in data science, there is mathematics that makes them work. It is important to understand the mathematical equations and relations to be a successful data scientist. Actually, in each step of data science, you will touch a wide range of mathematical concepts and approaches and see how they arise in data science.

In this article, I want to encourage you to get familiar with some of the most important and applied subjects of mathematics, and in the next articles, I will give you more information on this mathematical sub-subjects to help you be a self-confident and effective data scientist.

There are many sub-subjects of mathematics that are related in many phases of the data science life cycle. I want to mention some of the most important ones here.

Much of the progress made in mathematics since the 17th century was in the account of differentials and integrals, which were properties of a real number and a function of this set.

The study of these numerous collections led to the emergence of the concepts of coherence and derivation, and for this reason, this mathematics is called continuous mathematics. But in contrast to this kind of mathematics, there are other concepts in mathematics that can be defined on finite sets and yours. The set of these mathematical concepts is called discrete mathematics.

Discrete mathematics has grown in recent years due to the advancement of computer science. The most important topics in discrete mathematics are combinatorics, graph theory, mathematical logic, set theory, and preliminary number theory.

Combinatorics is an area of mathematics primarily concerned with counting, both as a means and an end in obtaining results, and certain properties of finite structures. It is closely related to many other areas of mathematics and has many applications ranging from logic to statistical physics, from evolutionary biology to computer science, etc. I recommend starting with an introduction to combinatorics, which basics of this topic are critical for anyone working in Data Science.

In mathematics and computer science, graph theory is the study of graphs. A graph is a set of vertices connected by edges. In simpler terms, a set of points connected by lines is called a graph. The concept of the graph was proposed by Euler in 1736 with a solution to the Konigsberg bridge problem and was gradually developed. Graphs are widely used in data science today.

Graphs can be found everywhere around us. One of the most well-known types of graphs in data science are the graphs of social networks.

#mathematics #mathematics-education #data-science #data analysis

1617961560

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

1611381728

**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