Jul 24, 2020
Why you should follow open source Data Science
Investing time instead of money will help you boost your career by just knowing these key tips
Since the beginning of the decade, data science has been accredited as the sexiest job of the 21st century. Many people have been accustomed to the fact that learning data science would help them transition their careers and reach new heights. Many of the institutions and MOOC’s have started teaching it by offering more fees and placement assistance. But having degrees and certifications won't make you happy.
Most people regret their decisions to invest in these institutions because either they land in roles that do not have data science exposure or either they fail to get a job. So before investing yourself into this, common advice would be to follow some of the open-source learning paths, see to it whether it motivates you, and then if you feel that knowledge provided by it is inadequate, research and find the appropriate paid learning path which will meet your needs.
Data science and AI are all about building effective solutions, hence even if you build projects based on open source knowledge, you will end up having an awesome portfolio. The following graph depicts the step by step procedure to get started with data science career.
Exploring Open Source Resources
This is the first and most important step before starting your learning. It's like choosing the right outfit from aisle full of clothes in a shop. It's a time-consuming process, but I will help you with it. I have categorized them according to the domain they fall into.
0. Programming Skills
This is the basic thing to start with. In today’s world, not only programming but reporting tools also have taken a major share in projects. Following are the links to help you learn this tools quicky:
Python
R
SQL
Tableau
PowerBI
GitHub
1. Maths, Stats, and case studies.
This is the foundational and the most important path before building any data science projects. If you skip this part, chances of landing up in a good job are pretty much low.
- Books:
- Youtube Channels
2. Data Science & Machine Learning
Data Mining & Machine Learning is the next step to follow. Building great ML Models is fun, but understanding its inner working is the very toughest task. Hence, in today’s date, the interpretation of ML models is considered important.
* Books:
http://www.dataminingbook.info/pmwiki.php/Main/BookDownload
The First Encounter with Machine Learning https://www.ics.uci.edu/~welling/teaching/273ASpring10/IntroMLBook.pdf
Machine Learning Notebooks: https://github.com/ageron/handson-ml
* Youtube Channels:
* Blogs
3. Data Visualization and storytelling
It's not only model building that is of utmost importance but also consulting and communication that plays an important role in analytics and data science.
* Important Links
4. Business Analytics
For Business Analytics, I would recommend free courses on Insidesherpa. It’s a MOOC platform where many analytics industries work on providing real-world business problems. Below mentioned are the key courses that will help you excel in this domain.
5. Hackathons and Projects
Applied knowledge always comes in handy. To get started with building projects based on real-world data science problems, I would recommend the following resources
* Hackathons
https://datahack.analyticsvidhya.com/contest
* Projects
6. Personalities to follow
Apart from learning by yourself, following the ideologies of these people will help you move one step ahead in this domain. Following are the list of peoples who build their rapport in data science.
End Notes
The list was exhaustive, but it will give you a great starting point to begin your journey. I myself have started utilizing the open-source knowledge and it has helped me a lot in terms of learning and building. I am sharing these resources with you to save your time on exploring and a starting point.
Happy Learning!
