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I am a beginner in Data science and Machine learning fields. final year of my graduation i come to know about DS & ML in times of final year project. after coming out of graduation i started focusing on these buzz words.
there will be two ways to learn anything. either self taught(moocs) or class room following.
I preferred to go for self taught but , How? then i started browsing and found plenty of resources out there. along with resources i also found some doubts.
- some times having more resources makes us confused which one to take, which one not to when you have no time.
- can i get a job just by doing these courses?
- what programming languages should i study?
- how to make my portfolio and networking?
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A)Programming language: Python and R?
Earlier there are statistical tools like SAS and R are used more than python. but now python is on the top list as several scientific computing packages are implemented especially for data science and machine learning. if you are a working professional looking for job transition, then its your take to choose one depending on your previous job role. but for beginner i suggest you to take python as it is so easy where R is a statistical concentrated.
take away : i suggest python.
after refering to 2 to 3 websites and books you will be good at python programming as each one teaches you different logics, tricks, methods with same concepts.
there are several websites like codeacademy, udemy (python3 complete bootcamp course), data camp, data quest, w3schools. books like “learn python the hard way”, python for dummies, automate the boring stuff with python.
B)what about math and stats?
math and stats are important for data science and machine learning, but it’s not all. learning each and every concept of math and stats is like a swimming in ocean which is impossible and not required for. but yes, knowing all the basic concepts and their implementations in real life that comes with the practice is good. remaining you will learn in the field.
especially concepts like linear algebra, calculus, statistics and probability.
take away: no data scientist/machine learning professional out there knows every math concepts.
the below links are from kdnuggets, and twowards data science.
15 Mathematics MOOCs for Data Science
Essential Math for Data Science — ‘Why’ and ‘How’
C)Data science : is it so tough to learn?
every minute a huge amount of data been generated. humans has the cognitive ability to process such data to some extent to recognise, to communicate, to predict, to review and to analyse data. but to get the best insights from the data, we need data science. data science is not so tought to learn. for beginners it feels tough , but actually not. its like after studying 7 th standard, 6 th standard is not tough anymore. similarly before learning data science it feels little tough, later you wont feel tough anymore.
take away: before learning it will, but after it’s not.
there are several courses, but i suggest you coursera course
applied data science with python
other courses like udemy(python for data science and machinelearning bootcamp), IBM (cognitiveclass.ai), Edx, udacity, coursera.
D)should i learn Machine learning for data science?
machine learning is not data science. but data science covers some algorithms to bring out the insights, and for predictions. knowing linear regression, logistic regression, k-nearest neighbors,Decision trees and random forests,k-means clustering, principal component analysis is must. for a fresher knowing these concepts and math behind these concepts is good.
for beginner: go for udemy(course1 , course2) courses.
people also suggest this top rated course : Machine learning course by andrewng
E)Can i get a job just by doing these courses?
probably not. no one cares about your certificates and list of courses you did. the only thing that matters is what you can do after taking these courses. a good way to showcase your skills is to do projects, participating in competitions, hackathons, code nights, writing blogs explaining what you learnt .it takes lot of time in little amount of time you have.
take away: projects, participating in competitions, hackathons, code nights, writing blogs will help you to get.
F)Ok i cheklist all above, now what? Networking
even after doing all above , some times it might not work. then networking will help you. linked in will help you in it. try to connect with data scientists, machine learning engineers who are working currently in industry as many as you can. connect with them, talk to them, ask suggestions, get help, show case your projects, get referrals, got placed.
i suggest you to follow these, these are active in data science community, machine learning community, each one have their own platforms, own podcasts, youtube channels to help aspiring data sciencts/machine learning engineers.
Akshay Bahadur(his projects are creative &surprises machine learning folks).
Shivam Panchal(posts list of best open source courses and some insights).
Vincent Boucher(one post in every 6 hours).
Nethra Sambamoorthi, PhD(Pres. and CAO, CRM Portals Inc.)
Tarry Singh(CEO, founder & AI Researcher at deepkapha.ai)
Randy Lao(active, helps to aspiring data scientists)
Kyle McKiou( Help Aspiring Data Scientists Get Jobs)
Favio Vázquez(Changing the World with Data Science)
Avinash Ahuja(data scientist at linkedin)
Sudalai Rajkumar(kaggle grandmaster, competitions expert, data scientist)
Ankit Rathi(Data Science Architect, Kaggle Expert)
Kristen Kehrer, Megan Silvey, Nic Ryan,Dat Tran,Mohammad Shahebaz
Imaad Mohamed Khan and purnasai gudikandula many more all above people are must you can follow on linkedin
G) build portofolio?
1.do projects and upload them into github.
2.write blogs on medium, kdnuggets, analyticsvidhya.
3.connect with working professionals on linkedin.
4.partcipate in kaggle competitions, analyticsvidhya competitions.
5.work on some personal or dream project.
you might ask that this blog is titled as ultimate resources for data science , but where are the links to them. below i will give you some github links(not mine) where you can find nearly 2000 resources all together including coding resources, hackathons, events, internships, clubs, meetups, conferences, People to follow, blogs to follow, data sets, podcasts, libraries, and every thing.
link1
link2
link3
link4
link5
link6
link7
link8
you can download pdf for some of data science and machine learning books here.
Learn Data Science by Coding without deep diving into the theory here.
finally
to keep all above in simple word.
learn python
learn maths(algebra,calculus,statistics, probability)
learn data science(Exploratory data analysis,feature engineering, predictive modeling, data visualisation.)
learn machinelearning(linear regression, logistic regression, k-nearest neighbors,Decision trees and random forests,k-means clustering, principal component analysis).
participate in competitions(most people suggest kaggle, analytics vidhya or your dream projects, and hackathons, bootcamps)
build portfolio(linkedin, projects, github, blogs).
Note 1 : do know about data science when you are in academics, once you are out of university for fresher it takes atleast one year to get knowledge upto some part of data science and then you can try for jobs. for professionals looking for job transition it may be easy to get data science job with 6 months s of time depends on your learning rates.
Note 2: i mentioned few people here and their blogs and their resources without knowing to them.
I know all the struggles that a beginner felt while learning data science. so please like and share this post to reach every beginner who is interested to be the future data scientist in every linkedin connections. be the part of budding data scientist journey and help him to grow .
please like and clap. every clap that you give encourages me to do more on data science and its resources that helps you. and please share this post to evevryone until it reaches to the every beginner or data science enthusiast in your linkedin connections and help them to grow.
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