Data Science is an interdisciplinary field whose primary objective is the extraction of meaningful knowledge and insights from data. These insights are extracted with the help of various mathematical and Machine Learning-based algorithms. Hence, Machine Learning is a key element of Data Science.
Alongside Machine Learning, as the name suggests, “data” itself is the fuel for Data Science. Without the availability of appropriate data, key insights cannot be extracted from it. Both the volume and accuracy of data matters in this field, since the algorithms are designed to “learn” with “experience”, which comes through the data provided. Data Science involves the use of various types of data, from multiple sources. Some of the types of data are image data, text data, video data, time-dependent data, time-independent data, audio data, etc.
Data Science requires knowledge of multiple disciplines. As shown in the figure, it is a combination of Mathematics and Statistics, Computer Science skills and Domain Specific Knowledge. Without a mastery of all these sub-domains, the grasp on Data Science will be incomplete.
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Artificial intelligence (AI) and machine learning is already shaping our future, and the demand for talented engineers is skyrocketing. According to the Market Research Future report, the machine learning market is projected to be worth almost $31 billion by 2024.
At SkillUp 2021, Nitin Gupta, technology head for digital innovations at India Today and Great Learning mentor and alumni, spoke in detail about artificial intelligence as a career.
With 14 years of experience in engagement and delivery, technical program management and agile software development, Gupta has worked with companies like Lenskart and Senior World. He also co-founded Zercross, a mobile and web application startup.
#events #artificial intelligence career #artificial intelligence jobs #career in artificial intelligence #data analyst #data scientist #machine learning career #machine learning engineer #machine learning researcher
A couple of days ago I started thinking if I had to start learning machine learning and data science all over again where would I start? The funny thing was that the path that I imagined was completely different from that one that I actually did when I was starting.
I’m aware that we all learn in different ways. Some prefer videos, others are ok with just books and a lot of people need to pay for a course to feel more pressure. And that’s ok, the important thing is to learn and enjoy it.
So, talking from my own perspective and knowing how I learn better I designed this path if I had to start learning Data Science again.
As you will see, my favorite way to learn is going from simple to complex gradually. This means starting with practical examples and then move to more abstract concepts.
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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.
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There are a lot of great online resources and websites on data science and machine learning that one can leverage in order to learn something new or maybe work on an existing skill now. The Age of Internet as they say, has made it extremely easy to access information on the go.
One of the hardest things to do in technology is disrupt yourself
- Matt Mullenweg
I have been approached on LinkedIn in through messages and connection requests regarding the same question countless times. And it is always something like this,
“ What exactly should I do to learn Data Science and Machine Learning….which courses to do…… and basically how to start? “.
The purpose of this article is to answer this question and along with it to give the readers a list of the most popular courses in this field currently.
Below you will find online courses which will help you accelerate your growth in the field of data science. But know that watching videos is just going to give you a seat on the ML council, it is not going to grant you the rank of an ML master if you know what I mean, for that you will have to work on practical real-world problems and get your hands dirty with data.
Photo by Author from SWU
What I can say though is that these courses will take you to that level from where you will be capable enough to figure out what you should do next. It is just a matter of starting out now!
And that is when you will find this table helpful. These course teach you the basics all the way to the advanced topics, and it is advisable that you take your time with each and everything and understand at least the basic concepts properly before you jump in on the code — something I am sure you would want to do from the word go!
#machine-learning #artificial-intelligence #deep-learning #data-science #data #data science
In the digital era that we live in, data has become the biggest and most valuable asset for most organisations. Data is rapidly transforming the way we live and communicate, and it is by collecting, sorting and studying this data, that organisations across the world are looking for ways to impact their bottom lines.
When working with all terminology related to data, it is essential to have a clear understanding of the different scope of work related to it. In this article, we’ll discuss the differences between Big Data and Data Science. Though these terms are interlinked and often used interchangeably, there’s a vast underlying difference between them in all aspects.
Let us begin by defining the two terms.
Big Data is a standard way to define it is as an assortment of data which is too large to be stored or processed using the traditional database systems within a given period. A common misconception while referring to it is when the term is used to refer to data whose size of the volume is of the order of terabytes or more. However, it is a purely contextual term. For example, even a file of 250MB is Big Data in the context of an email attachment.
Data exhibits key attributes that must be taken into consideration when processing a dataset. They are most commonly known as the 5 Vs. Each of the Vs has specific implications in terms of handling them, but, when all of them are seen in combination, they present even bigger challenges.
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