Are you a data science and machine learning enthusiast who wants to create a data science learning plan with important checkpoints/milestones? If yes then this GitHub repo is for you. This repo is inspired from a roadmap of data science skills by Swami Chandrasekaran
By: Fatih Aktürk, Hüseyin Mert & Osman Ungur, Recep Erol
Pic source and credits: : Awesome Data Science
This Github repo is super helpful for budding data scientists and considered as the gold mine of Data Science
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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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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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Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.
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If we only learn data science through a rigid curriculum created by universities or online courses from Coursera or Udemy, we may find the learning process too boring. If you ever find yourself losing motivation in this long journey of studying data science, you may just need some podcasts to break the routine and get some inspiration. The major difference between these two approaches of learning is that the former focuses on theory and concepts, whereas the latter introduces more practical experience and projects that add flesh to the bones.
Listening to podcasts is a great way to absorb knowledge while you are commuting or doing the chores. One of the amazing apps that I recommend using is called “Airr” which allows users to highlight the content of the podcast and transcribe the highlight into notes. This tool is especially useful for technical podcasts since information is more easily erased if you are more of a visual learner than an auditory learner. Therefore, putting it into notes somewhere would assist in transforming them into long term memory.
On the other hand, if you are more of a visual person who prefers to learn through reading, then have a read of the Data Science website list that I curated :)
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With recruiters listing a myriad of “preferred skills” in their job postings, learning Data Science can get quite overwhelming at times. Dividing the journey up into five chapters can provide a clearer picture of what lies ahead.
#machine-learning #learn-data-science #data-science-training #python-for-data-science #data-science-courses