How to Become a Data Scientist in 2017? | Data Scientist Career | Data Science Future
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- Опубликовано: 19 окт 2024
- About the Webinar :
Agenda of this session will include answers to the following questions:
1. Why is it the best time to take up Data Science as a career?
2. How can you take the first step in Data Science? (After all, first step is always the hardest!)
3. How can you become better and progress fast?
4. How is life after becoming a Data Scientist?
About the Host :
Jesse Steinweg-Woods is soon-to-be a Senior Data Scientist at tronc, working on recommender systems for articles and understanding customer behavior. Previously, he worked at Argo Group Insurance on new pricing models that took advantage of machine learning techniques. He received his PhD in Atmospheric Science from Texas A&M University, and his research focused on numerical weather and climate prediction.
Slides: www.slideshare...
Questions Answered:
1. How can a college fresher (say, studying in sophomore or final year) become a data scientist ? What projects can they do ? What skills should they focus on ? How to start applying for jobs ?
2. How can an experienced professional make a career shift into data science ? Let's say, someone has 3 years of experience in Java, and he now wants to become a data scientist. Or, let's say, someone knows Hive, Pig, Flume, Hadoop, what could be natural career progression for him ?
3. How is a Machine Learning Engineer different from Data Scientist ?
4. How is a Statistician different from a Data Scientist ?
5. How is a Data Engineer different from a Data Scientist ?
6. What is the relationship between data science and machine learning ?
7. What are the most commonly used ML algorithms in industry today, so that students can master them first ?
8. What is the future of Data Scientist job ? Will it survive after 5 - 10 years or get automated?
9. Can data science be used in building geological applications ? If yes, what would be the starting point ?
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Only disagree about the educational prerequisite. I have no degree but successfully work as a data scientist and R programmer having taught myself statistics, machine learning and R/Python etc..
That's impressive
It did take me ten years lol. I started as an excel data analyst, just pivoting tables and reporting the numbers. I knew they had to be more however and started to learn stats (my background was quality management which helped) and began applying what I learnt. I then became a statistical analyst using applied statistics to test different hypothesis etc. and later taught myself ML which I'm still learning. I now work for myself as a contractor. I basically see the rise of DS as similar to the rise of web design and development 20 years ago. In other words experience counts for a lot and your portfolio of completed tasks counts as high as any higher degree. Trouble is, in my opinion the universities and academics are trying to ring fence the job description so that only their students can be considered for DS roles. I've worked with MSc and PhDs and to be honest they are nothing special. Very rarely do they have domain specific knowledge and as such make some pretty fundamental mistakes. Experience in a sector plus a portfolio will still take you far.
That's great of you, daarrn your pretty resilient. I on the other hand, having a background in statistics and economics, i got an invite from a start up, www.proolabs.com/ i dont think people are appreciative enough about DS in Africa as much as they are, like in Europe and the U.S but i guess, given a little bit of time the trend will change. Am still perfecting my R skills whilst looking for portfolio building projects and tasks, before i finish campus. btw, I totally agree with you, experience beats papers.
I want to work as a data scientist. What do i need to do/learn to be in the field? Im currently in a software engineering talent segment not yet in a year working. What do you think the career that suit me thats is aligned to being a data scientist?
I agree with you on this. I am a full time undergrad student - data analytics and Stats major. I currently work as data scientist, and right now I am learning scala and spark. Most of the people I know that are in the field have a BA or BS. Now this may not be a large sample but, I believe this Phd requirement has gotten way out of hand for data scientist jobs.
Also, I attended the Strata Data Conference in March -17, and one of the topics that phd students were seeking advice on, concerned itself with leaving there programs to work in the DS field now. Because of the opportunity cost of staying in their program.
The single best explanation of getting into the field that I've heard! He does make it more daunting, though.
I agree
Always the reality is cruel and daunting, But it gives chance to get into reality. Cheer up!
Scott Miller That's a good thing.
ruclips.net/video/vM2lvNJb_5A/видео.html
You dont choose a career for a trend. You choose data Science to see the reality through meaningful data perspective. You want to predict future. Data sciences are a crystal ball.
I have a magic 8 ball. Does that mean I'm a data scientist?
zak00101 that's true Einstein👌
Agreed - this career path has a long learning arc, which is an acquired taste
You can if you have enough data and are able to analyze these data for meaningful information.
I Agree you dont choose a career a career chooses and masters you.
People who see data scientist as the #1 job based on data and decide to become data scientists already have the right mindset.
I can't tell if you are being sarcastic
You don't need a graduate degree. The reason most data scientist have graduate degrees is because it has become increasingly popular in the last few years and people who had degrees in CS, physics, math, biology, etc. saw it as an opportunity to make a lot of money. You won't be able to pay off your debt easily by becoming a researcher at a university. That, and a lot of people realize the difficulties of becoming a professor and look for an alternate career that is somewhat related to their field of study or skills they have acquired. And so, they end up in data science because they already know enough math, statistics, scientific reasoning and how to code in languages like R and Python.
If you want to become a data scientist in a short amount of time (which is most likely ideal), you basically need to build projects and create connections. So, make a blog that takes about what you are up to and keep pushing code online. If you show the work you do, connect online through blogs, twitter, etc with employees of a company you'd like to work it, the job will come.
what websites do you recommend? ( blogs)
Otherwise you can study abroad in a country where the education is free. You'll need to pay for your everyday life's expenses though, but still better I believe ^^
brb building a recommendation engine based on tags and collaborative filtering
Probably one of the best guide to becoming a data scientist
I am a former academic, have a PhD, and have been a data scientist since 2013. Having a PhD especially in a quantitative field definitely can give you an early advantage in the field. Of course, it's not a necessity. It's also not necessarily enough for doing very advanced data science work, for example, developing very sophisticated new machine learning algorithms.
There are quite many approaches and ways under which people analyze careers. At the end of the day, one chooses what they feel best for them. Great guide here. Nice stuff.
I would say that the reason so many MSc's and PhD's in the data science field is that it's a relatively new field. What this means is that the only people who are really in the know about the field are either those seeking to specialise in it with their Masters, or they are researchers who have contributed to the data science field in some way.
Great job demystify the complex topic without strong recommendations loved it
Totally agree that degree shows ability to learn.
Yes, ability to speend 3 to 7 years learning BS in a uni degree at a slow rate. Do you realise how much further you would be if you stated your career leaning yourself
While the programming languages vary at my employer (finance industry), the highest paid jobs often require SAS. While I am learning SAS as a programming language, the presenter is 100% correct when discussing support. All SAS support is scrubbed from open sources (youtube, stack, etc.) and is ONLY available behind their paywall. If you want to SAS, be prepared to shell out 10s of thousands to get fully certified at the SAS Learning Institution in Cary or San Francisco. Do NOT bother trying to teach yourself. We have a senior level manager who volunteers weekly to teach Big Data topics, most of which are in R and (begrudgingly) recapped in SAS as well. I'm just starting out in the big data side, but I will make it a mission to understand and utilize Python. Thanks a ton!
SQL is the first thing they teach in india, abcd.. comes next.
I am very very grateful about your presentation! That really helps me a lot in term of making career path choice and how to become better in data science field!
Your words and time were much appreciated. Thank you
Excellent. Specific. Detailed. Clear. Thanks.
Going to school for this is a huge waste of time. They teach you at such a slow pace, it is ridiculous. There are so many ways to self learn this stuff
Derrik Bosse have any reccomendations?
How do you went through all these?
Thoughts on physicians (MD/DO) entering into data science? I have a Biology B.S., a medical degree, and back in school now for computer programming (also taking additional math, e.g. linear algebra) -- doing Udacity's Data Analysis Nanodegree as well. Does this sound like a viable path to an eventual career transition into health data science?
Very useful information, inspiring people who are confused with their path
Opo gusto kong matuto hilig kopo talaga yan batabpalang ako best science ako sa aming school
i am having bachelors degree and already 10 years into IT.In no mood to do any MS or Phd..seems getting into this field ,one has to compete with higher degrees..
The reason for so many academics in the field is because they all left research jobs for the big money jobs in data science! and they already had all the skills to do it
While I like the talk in general, I'd like to express a difference of opinion with the presenter's comments about SAS. SAS does offer a free version for learning. Also, there is an abundance of freely available documentation/community resources online.
Thanks for the session ... thanks for the long question and answer session that is more helpful
Good job on the Web Scraping Indeed for the Key Data Science Job Skills project, it's what I have been using for guidance and this video was amazing too
The "subscribe" link in the video is spelled "suubscribe." Just an F.Y.I... I found this video very helpful as I ponder entering this field of study! Thanks!
Hi Jesee, overall the presentation is very useful one, at the same time,i would strongly suggest to use bright colours fr ppt background or to the foreground content..Cheers😊😊!!
Great! I feel like you've made this video especially for me. You answered many questions came to mind.
Paano po nyo ba na connect sa data po ba
hi i m a high school graduated guy and i've learned alot of stuff from internet, reading books, lectures etc. i can program in multiple languages but now i wanna jump in to data Science, which is awesome, so do i need a degree for this? i mean if i could be a software engineer at home so why not data scientist?
Is it just MS in Data Science or Analytics that is considered valuable? or MS in Information Science specialization in Big Data Analytics is also equally valuable?
cases like that you really have to compare the core curriculum vs. the title. If you can go and meet the directors of the program as well as teachers and students it will help you decision tremendously.
Thank you for the video and the slides. Very helpful.
Very good and accurate video. Thank you!
(p.s I see many Data Scientists who work on Macs rather than Windows machines - why is that? Are there any benefits in using the mac OS?)
Hello. YES! MacOS is Unix, Windows is ... windows. You can get a Linux Virtual Machine on Windows I guess, but there is something nice about having a first class citizen Linux-like machine ;)
Plus Macs are just so nice to work with (price is a problem tho!).
Example: just doing a Computer Vision course and author didn't even bother creating content for Windows as Terminal (unix shell) is a key requirement.
Incredibly helpful, thanks
Hi @HackerEarth do we need some base level knowledge for this, or can it be done by any1 who has the will and determination ?
Thanks
which is more valuable, more interesting, and more worthwhile to take as a career: Data Scientist or Cybersecurity?
I'm having trouble to choose between the two
Mark AJ Do what you think most useful for the company
Thank you! Very well explained.
I have an Associates in Applied Sciences, but I want to transfer to become a data scientist. I have a nano degree in Basic Android programming along with that.
What similar career path would you recommend for those who don't like the idea of doing data janitor stuff?
You did not recommend any resources for learning Python. Could you please suggest one? (book or a course in coursera ...)
Thanks
Udemy friend
Coursera mate
How do you feel about Stata? To me, its a great package for more complex statistical analysis.
As I said to a friend of mine, "stata is for normies".
A more in depth answer is that python is growing in capabilities and is far more flexible than stata. Python's biggest weakness is that due to its simplicity, it ends up sacrificing a little on speed (but not that much), however, that weakness translates into a huge advantage as people who know MATLAB/Fortran/R/C/etc and create packages based on what these languages do well. This means that Python's flexibility gains it a bunch of packages which combine into a swiss army knife. Given that it's also easy to learn, it inevitably overtook R.
You should learn Python, that's a no brainer and you'd just be hanidcapping yourself necessarily by avoiding it.
If you somehow were banned from using Python, you'd use R instead because it's free so it's easier to gain experience. If you have extensive experience in sata already I'd just pick up one of these languages anyway they are standard and your statistical/analysis knowledge will transfer right over.
The elements of statistical learning link is broken. Great video, learned a lot!
Dane Wilkins I have the book's pdf. And other materials. If you wish I can mail you.
If you don't mind please share it to me raminfo4u@gmail.com
Yash Gupta please mail that stuff at arunsinghyadava@gmail.com
Well explained ....
Thank you
You are a Noble guy.May god bless you.
Very interesting video, each of your word is precious
VERY HELPFUL thanks a lot!
May I know how can I learn data science perfectly. And after complete can I get good easily or I have to struggle in this field I'm from India
Cannot thank you enough for the Video.
Thank you from Kazakhstan :)
Hi I did my Masters in Nursing. Is there any scope for Nursing in Data science. I am looking forward to take this data science as career but I don't how far it is helpful for me. please let me know about this.
please share your ideas on this whether to divert my field from nursing( healthcare) to datascience
Great talk! Thanks
how do i start for the projects?
Is Scala a viable option ?
Nice Explanation Hacker
So helpful thankyou
Aye!!! Aggie here! Whoop!
Thanks!
good luck new job.
nice word of data science
Do you know how to become a Data Engineer? Thanks
even i'm planning to get into data engineering. Please help
Data Engineer (DE) is an advanced title of Database Admin (DBA). If you know how to manage the databases, perform all kinds of ETL and have experience and passion, then get into DBA and move up to DE.
Thanks... then becoming a data engineer is harder than becoming a data scientists since few people have the opportunity to become a DBA.
You don't need to start with DBA to become DE, as long as you understand and know how to create and manage a database under various RDBMS and NoSQL environment, know ELT, ETL etc., basically engineering data well for the data science team usage, then you got a shot. If not DBA, you could start as an entry level Data Analyst (who pulls data from various data warehouses and analyze them using various tools - for example R, SAS, Python), then become Senior Analyst in 1 year, then move to DE after couple years.
Thanks for taking your time to provide so good information.
Gig'em
Learn the building blocks of Data Science in just 3 easy steps - datahub.packtpub.com/insights-opinion/2018-new-year-resolutions-algorithmic-world-part-1-of-3/
I don't get this shit.