Applied Data Science with Python

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Below are the top discussions from Reddit that mention this online Coursera specialization from University of Michigan.

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language.

Text Mining Python Programming Pandas Matplotlib Numpy Data Cleansing Data Virtualization Data Visualization (DataViz) Machine Learning (ML) Algorithms Machine Learning Scikit-Learn Natural Language Toolkit (NLTK)

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Taught by
Christopher Brooks
Associate Professor
and 27 more instructors

Offered by
University of Michigan

This specialization includes these 5 courses.

Reddit Posts and Comments

0 posts • 132 mentions • top 50 shown below

r/learnpython • comment
25 points • ahonnet

I can't say whether it's the best since I only have taken the one, but I took the Applied Data Science with Python from University of Michigan.

I was happy with the course and I learned a lot. I would say the courses would be on the difficult side if you're only a month or two into Python (unless that is on top of of other programming experience). However, if you're looking for a data science focused group of classes, I feel it is a good foundation.

Note: this is a certificate course, so the scenario with your GF enrolling for you, the certificate would likely be in her name.

r/datascience • comment
91 points • InterdisciplinaryBid

A question I'm qualified to answer. For context - I have a master's degree in Economics and made the break into a Data Science role a few years ago. The way I'd break down how you approach the job hunt is into three categories - Technical Skills, Data Science Skills, and Positioning.

Technical Skills - Most Data Science roles out there require knowledge of Python on SQL. R is rarely used in production settings so I'd be biased to ramping up on Python if you're not on that path already. I taught myself enough python to scrape by interviews using this specialization on Coursera specialization - Since you've already worked in STATA and R - it's just a matter of figuring out how the syntax works and this specialization is super hands-on so it gives you just what you need. I learnt SQL in a weekend - it sounds like an intimidating thing to do, but it's nothing you can't do if you got yourself through a masters degree in economics. Do all the tutorials on here ( and you can add to your resume in a week.

Data Science Skills - This is two-fold - ML algorithms and the ability to work smartly with data. I think the latter is a skill you already have if you are able to clean data and come up with a Fixed Effects model and make a thesis of it. In terms of ML algorithms - what I found most confidence-inspiring when I was trying to learn this stuff is starting from first principles. All the fancy buzz words in DS (Neural Networks, Regularization - you have it) can be mapped back to mathematical concepts that underpin a linear regression that you already have from Econometrics classes. For instance a Logistic Regression - the core building block of a Neural network for a classification task is the Probit and Logit models that you already have to have done in an Econometrics class. A lot of data scientists have no idea what they're doing from a mathematical standpoint when they are trying out DS algorithms and you have an advantage here. Long story short - find connections between what you already know and what is Machine Learning and learn all the buzz words. Trust me, you already know a lot of this stuff.

Positioning - As someone with a masters degree in Economics you already come with a lot of the skills required to be a good Data Scientist. You need to sell your skills in a way that is attractive to people in Industry. For instance - what most of Econometrics boils down to is finding smart ways to work with data and assumptions to arrive at the causal effect of an intervention on an outcome variable (Causal Inference). Take the fixed effects thesis for example - reframe it as working with observational data to arrive at a causal interpretation of an intervention. You need to speak industry language - not many people know what Fixed Effects is, so tell them what they want to hear. For example, you could sell yourself as someone who can work with observational data to prove causal interpretation of an intervention. Experimentation is another area where an Economics background is particularly helpful - talk about this. Natural experiments happen all the time and companies have data about it, talk about how you know what to do with a natural experiment. Econometrics + Machine Learning has a lot of research happening right now, Susan Athey at Stanford is at the forefront of this and her papers will give you good ideas as to how to blend the two disciplines.

Well, that was a long answer. As an Economist, you come with the right-thinking required to get a job in Data Science. Ramp up on technical skills - it should take you a month of dedicated studying. While prepping on the technical side start reaching out to people on LinkedIn - 'Economist + Data Science' in the search bar is a good place to start. In addition to online applications, talking to other people is a great way to get yourself a referral and jump the usual online application/rejection cycle (this is what worked for me in the end). As with most DS jobs you will learn most things on the job - so demonstrate a willingness in these conversations to want to ramp up on SWE skills which are required in the real world for sure

Hang in there - with a little more work and time I'm confident you'll be successful.

r/datascience • post
44 points • rushjustice
Just Finished Coursera's ML Class | Next Steps

Hey all,

As per one user's great advice from a post about two weeks ago, I began my journey into ML and data science. I completed Andrew Ng's course on ML and found it extremely interesting. I loved every bit of it. I was on coursera every day, and completed everything in that course. It was very cool to go on Kaggle, read some tutorial kernals, and just find myself noting what the provider should have done differently as per Prof. Ng's advice. I feel like I have a solid understanding of the fundamentals of some of the most basic and widely used ML algorithms today, and how to use them properly.

I'd now like to contribute on Kaggle, but I really do not have the skills to do ML (or really any data science) in Python/R. Though I probably could mash up some code from some popular kernals, I really wouldn't know what I was doing, and so that would be pointless. I've discovered two courses (specializations) that focus on deep learning / general data science using Python, that seem pretty good. At this point, I'd like to learn Python over R.

Has anyone taken these courses? Does anyone have an opinion on what are some good ways to learn Python with data science? Sometimes I think I could be overcomplicating this, but I really don't think it's wise to jump into Kaggle, only to possibly burn myself out because I don't know Python. Perhaps someone has been in a similar situation and can help guide me? Again, I could just jump into the above two courses, but if anyone can help optimize my solution so that I start in a better direction, that would be huge!

Thanks you, everyone! As it stands, my game plan is to get on Kaggle, build up a portfolio, and use that to help me land a job in the ML realm. I've actually found some interesting jobs that combine both my collegiate background with ML. Pretty neat.

r/EmDrive • post
11 points • Eric1600
For Experimenters here is a free Introduction to Data Science in Python

Check out courses 1 and 2 for the basics on how to analyze data with python.

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have basic a python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.

Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization.

r/GameDeals • comment
11 points • plumber_craic

Doesn't look like it. But I found this course to be a good intro to the subject.

r/litecoin • comment
7 points • forseti_

If you cannot find out this yourself you might not be of much use to them.

But I'd say learn python. This here sounds pretty good:

You could analyze and visualise market data or even write a trading program in python.

r/IWantToLearn • comment
4 points • russybooboo

Check out:

r/learnpython • comment
8 points • nomowolf

I began learning from MOOCs (specifically: 1,2) which are primarily video lectures with assignments and tests.

I found it especially useful at that stage, when you really have no clue, to see examples worked through and have an actual human explain fundamentals and say things like: "you may be surprised by that result, but don't worry, it's because....". Then as you start to build the vocabulary and know how to express what it is you actually want to achieve, you become more independent.

I still go to youtube now and then. Someone explaining as they write the code adds another dimension of understanding, and you can pause - try yourself - rewind.

r/learnprogramming • post
12 points • running4beer
Aspiring Data Analyst here, I'd like some input on my learning plans for the next 10 months.

I'm currently an English teacher in South Korea, but I've set a goal to accomplish by Aug 2017, the end of my 1 year contract when I return to the States: have all the skills to be hired as a junior data analyst or developer (leaning towards analysis)

So, I just finished the Python course in on Codeacademy. It was good for getting used to syntax, but I personally would have liked more explanation of some of the theory behind things. As it is I've ordered the book Fluent Python, and I think I will try to get as far as I can through it in the next 8-10 weeks or so, and hopefully get a few projects up on GitHub. Around February I want to move beyond just Python. I am considering a few options at the moment:

  • Taking one of the data science courses on Coursera, such as Applied Data Science with Python or Data Science. If I go this route, my decision will rest on whether I want to stick to Python, or branch and learn R.
  • Learning how to use Google Analytics with the goal of becoming certified by next August. Then becoming familiar with MySQL.
  • Diving into Javascript and learning as much as I can.

Please let me know if I'm being overly ambitious. I currently devote about 10 hours a week to this, but am willing to ramp it up.

Also, I'd love some input on which route I should pursue more: Developer or Analyst. I am leaning towards data analysis, because I like the idea of being able to tell a story with data. It would be nice to get into the visualization side of things too. I also see it as an opportunity to use many of the soft skills I got from my B.A. in Philosophy.

The cons I see with this path are that I'm not really interested in generic marketing/business analytics, such as SEO and whatnot.

Thanks in advance, any comments are welcome!

r/datascience • comment
7 points • redouad

Andrew Ng's course by itself takes 11 weeks, and it's quite challenging if you're new to the field. Adding R, Python, and SAS on top of that is likely to make any candidate burn out. Don't get me wrong, it's doable if you decide to dedicate 15+ hours of your week to it. If you're efficient during the whole process you might get enough knowledge to pass this Codility test (never heard of it).

If you feel like you're ready for that kind of time commitment, I'd suggest:

  • Do the ML course over 11 weeks.
  • Do as many DataCamp courses as you can to learn R and Python quickly (the "Data Scientist with R" and "Data Scientist with Python" career tracks would be what you need). Alternatively you can do the R specialization on Coursera ( and the Python one as well (, but they're supposed to span multiple months.
  • Indeed try to find some information about what Codility tests are, so you know what to expect!
  • With the little time you'll have left, try to do some passive learning by listening to podcasts. Listening through past episodes of Data Skeptic would be nice for example - it'll get you familiar with various data science topics and issues, algorithms, practical cases, etc.

r/coursera • post
3 points • elkend
I thought coursera was free?

Been a few years since I’ve been in the site. Trying to sign up for a course here:

But can’t seem to without paying. Is coursera all paid now? Thought payments were just for certificates.

r/datascience • comment
2 points • hibbly

This is a spam link which redirects to LinkSynergy. The post should go directly to the Coursera Data Science page instead of a spammy redirect link.

r/cscareerquestions • comment
2 points • ranalytica
r/india • comment
2 points • ShortLastingErection

Well thats why I mentioned my final tldr, If your priority is basically stats and don't have much time , don't waste your time winding up in tutorial hell and specializations and go along with Andy Field's R book in R (doing it in python is time consuming and R is far far better for stats than python). Also it's very easy book to follow along and yet a very solid book and is recommended almost everywhere.

If your priority is data science but not stats and company don't use or recommends R , this specialization in Python hopefully contains the solid foundation of all the bare minimum you need.

r/datascience • comment
2 points • brendanmartin

Applied Data Science with Python on Coursera is a pretty good track. It's all free if you audit each course individually.

r/labrats • comment
4 points • FlavaFlavivirus

I'm working through the series on R now, but plan on doing this next:

r/learnpython • comment
1 points • rndm_reddit_profile

r/datascience • comment
1 points • Tanren

Look at this

it covers about the same material its $49 a month. There are also many other relevant data science courses on coursera because if you don't have a technical background I think this course or the one you posted would not be enough.

r/umsimads • comment
1 points • AustinMartin07

I’m in the same boat as you. Got admitted for January but haven’t had any follow-up yet.

For prep I’m actually working on UM’s Applied Data Science with Python course. I figured I would just get through as much of it as I can before classes start. I imagine that it would be pretty good prep.

r/learnpython • comment
1 points • peaceful_creature

There is a specialization in applied data science on Coursera. It is offered by University of Michigan. Slightly costly but pretty effective:

r/AskWomen • comment
1 points • youactsurprised

I run a team of data scientists and data analysts, this is a discipline distinct from comp sci and coding, and is focused on deriving insights from large data sets. Yes, coding is involved, but only in the sense that SQL will allow you to read data from a database, and python\/r/etc will let you clean, format, and present it.

The Coursera Applied Data Science with Python course is excellent. You only have to pay if you want the certification, Coursera will give you access to the course materials for free.

There are some super cool data sets out there to play with, and I recommend participating in subs like /r/dataisbeautiful for inspiration on projects, visualizations, and resources.

Good luck!

r/italy • comment
1 points • pigliamosche

>Uh io ci sto lavorando sopra, ma sono ancora all'uni, quindi non ho ancora avuto esperienze dirette col mondo del lavoro. Da quello che so, qualcosa si trova, però non è facilissimo. Poi c'è anche la possibilità di lavorare da remoto per compagnie estere, tipo USA.

Ci stai facendo una tesi sopra? O segui un corso sul ML?

Comunque sembra una bella specializzazione come tante altre ma mi interessava capire quanto fosse spendibile nella realtà italiana.

>Se sei interessato all'ambito ML secondo me ti converrebbe fare un corso di data science (seguibile a gratis su coursera) che sembra che abbia un mercato più vasto del machine learning puro.

Io su coursera trovo [questo] (, ma mi risulta essere a pagamento, come tutti gli altri corsidella piattaforma.

r/india • comment
1 points • iwannastudy

Check out this course too. Has some of the stuff mentioned above.

r/softwaredevelopment • comment
1 points • inhumantsar

Python. It's not going to be as performant as other langauges, but it's probably the best language to learn programming on. University of Michigan runs a great set of Python for Data Science programs on Coursera. They'll introduce you to Python and then throw you right into data science and machine learning work.

r/datascience • comment
1 points • somerandompersonne

Through Coursera, the "Applied Data Science with Python" specialization has been pretty good so far if you have basic coding experience and want a bit of a crash course in some applied data science.

r/options • comment
3 points • iamnatetorious

My largest conclusion: when making a trade assume it's either instant exit OR will sit on books for 3 months.

  1. If you don't want to inventory for 3mos then don't do it.

  2. While we get frustrated and want to exit it's better to bag hold until next quarter.

  3. The inverse is also true- and our pride and joys will be mean reverting against us. Take profits and look to get back in

This more/less aligns with TT market measures (search drawdowns).

The stocks selected were ToS scan of: - has weekly options (.. is tradable) - price over 20$ (.. is commission efficient) - volume over 1m (.. is liquid) - has dividend (... has +cash flow)

For each I examined whatever came back in daily last 20 years. If stock only 3yrs old then obviously only 3yrs.

Total ohlc points= 15m

An example cycle would be - Day 1 bought XYZ at 100 - day 2 fell 1$ - day 3 fell 1$ - day 4 collect 1$ dividend, up 50c - day 5 up 51c .. cycle complete (net >0)

Commission assumed zero because at scale it's less than bid/ask slippage.

Inclusion of options

I haven't gotten historically options pricing to work (yet) via [tda streaming API](] and I'm too cheap too actually pay someone.

Why did I do this?

Nothing to do for weekend, so took and then playing around with jupyter notebooks

r/datascience • comment
1 points • SomeCanadaGuy

Through Coursera, the "Applied Data Science with Python" specialization has been pretty good so far if you have basic coding experience and want a bit of a crash course in some applied data science.

r/cscareerquestions • comment
2 points • lolski_

How about you take a couple of programming courses at Coursera and see if you like it:

r/learnmachinelearning • comment
2 points • Aaraeus

Disclaimer - I'm new to ML too, and from a data background (SAS/SQL in banking).

I had the same question around a month ago and like you, realised a lot of contemporary industry relies on ML in Python. I wanted to hit two birds with one stone (ML & Python practice), so I opted against Andrew Ng's course (despite the glowing recommendations from other Redditors) and opted for a different course.

Originally my plan was to complete the Data Science Specialisation from the University of Michigan, which is the Applied Data Science with Python Specialisation. However, my company decided to stop offering these courses and said they'd bring back licenses at a later date, potentially in the new year. The course costs £38/month until you complete it, but offers a gradual step into Python and really helps with getting to understand the detail.

However, I already knew VBA and had dabbled in Python already so I thought I'd start with Udacity's Introduction to Machine Learning (UD120). I'm planning on completing this, then jumping straight into Kaggle competitions.

So far I'm really enjoying it! There's plenty of quizzes to provide positive reinforcement (I'm such a child), and the two instructors are warm and friendly. The only down side is it uses Python 2.7 by default, BUT there's some bloke on GitHub who's converted all the code to Python 3 and honestly I've had minimal problems, if any.

From what I've read (and listened to), most of industry uses Python for ML. Academia is using R I think, but even that seems to be moving towards Python.

r/datascience • comment
1 points • KlutzyCoach

Hello, I am looking for an online course at coursera for Machine learning. I have completed Jose Portilla course from udemy and many online youtube courses. Now I am looking for a course that I can add in my resume. Please suggest a course that you find really helpful for Machine learning that uses Python.

I am looking at following two courses:

  1. University of michigan course:

  2. IBM course:

As coursera needs investment I want to invest in a good course as I am limited on my budget too. Thanks!!

r/coursera • post
3 points • LexD1vina
IBM Data Science vs UM Applied Data Science?

Hello people,


Has anyone done any of these (or both) of these courses? I want to do one of them however I can't seem to decide. The goal is to improve in Python (Pandas) and Data Sciences.





r/uofm • comment
1 points • Madigan37

I took most of the data science classes on coursera offered by Umich, and I thought they were pretty good. I would recommend the introduction to Python class taught by Dr. Chuck, and if you have time the intro to Data science class. I think that most people should be able to complete the into to Python class, and would recommend it if you have no python experience. The Data Science class was actually very tricky - I am an EECS upperclassmen and it felt like a full, 200-300 level 4 credit class. That being said if you have the time and have completed the introduction to Python class, it is definitely worth it.

r/learnpython • comment
1 points • Mcmatt90

sorry for the late reply! Had work and class. But I can't vouch for Automate the boring stuff, as I took python course as a graduate course. Here is a free book that gets into more complex data structures and useful applications with python. I know you said you know data structures, but python has certain data structures unique to the language (i.e tuples, dictionaries). It also covers web scraping, regex, and data visualization basics.

Also, since you will be doing analytics it would be definitely helpful to learn the numpy and pandas library which used a lot in data science and analytics.

Here is a free specialization on coursera for data science form the university of michigan. It gets into machine learning which may be more than you want but it covers pandas and even data visualization.

Hope this helps!

r/AskAcademia • comment
1 points • kokomoinmyheart

Yeah - it might be an academia thing to be concerned with credentials.I like being self tought (and feel like I have made the most progress in philosophy through self-study) and the general culture that comes with it, however I still feel like I should go for a basic certificate or somehting similar so that I know what can be known and should be learnt - to have some basis on which I can then built through self-teaching. I have been looking at this Python for everybody course as well as this Applied Data Science with Python course on Coursera to do just this. Any opinions?

r/datascience • comment
1 points • WarioBrega

No, I meant the University of Michigan Specialization (wrong link, here's the correct one: and I'll edit my original post). Do you have any experience you can share on it?

r/Python • comment
5 points • Old_Kat

In case anyone wants to check out the classes I posted about:



The EDX platform is better. The individual courses are easy to access. Coursera is trying really really hard to get your money, so they want to sell you the "Specialization" subscriptions. You have to search the individual course names to find them

If you are taking any of these courses and installing Python with a desire to get into data science, forget installing the standalone Python from and get the Anaconda package. It includes Python + the best data science working environments in an easy installer for any OS.

Anaconda Downloads

If you prefer working online, and need Jupyter notebooks, which they teach in the Applied Data Science course series on Coursera and the USCD micromasters series, you can use Azure's. They are free. If you already have a Hotmail/Outlook account or any other Microsoft service, you're already in. If not, just sign up for one. It's one of the rare and wonderful things Microsoft has done for the open source and data science community.

You will use Jupyter Notebooks a ton if you get into Py for DS, so learn them and get familiar. I don't do a lot of DS work (really ANY), but I do a lot of testing out bots and small ideas on the fly.

Azure Notebooks

Have fun!

r/learnprogramming • comment
4 points • my_password_is______

all of

course 3 in

course 8 in

all of

various courses by Andrew Ng (Co-founder, Coursera; Adjunct Professor, Stanford University)

any of

r/learnmachinelearning • comment
2 points • Zhultaka
r/datascience • comment
2 points • lucas50a

I don't know about the courses you mentioned, but I'm doing . I'm finishing the first course next week ("Introduction to Data Science in Python"). Mostly the course shows you how to use Pandas and some NumPy. It's mostly applications of Pandas to data and you also need to learn from books, documentation and Stackoverflow to pass the assignments.

The other courses on the specialization are:

2 - Applied Plotting, Charting & Data Representation in Python

3 - Applied Machine Learning in Python

4 - Applied Text Mining in Python

5 - Applied Social Network Analysis in Python

I think that is too expensive

r/environmental_science • comment
2 points • zutnn

Hey there,

I think you already have quite a good background and should be suitable for a lot of jobs. Finding a position that overlaps both policy making and data analyses is probably difficult to find, but most likely exists in research institutes and think tanks. When you decide between those two it is probably the decision between higher impact (impact) and better pay/more secure job (data analysis). If you want to deepen your understanding in data science online coures like this one here might be helpful:


When looking on job advice and what to do with your life I found 80.000 hours extremly helpful:


Good luck!

r/belgium • comment
1 points • bananensoep

De cursus waar ik het meest aan gehad heb, is deze:

Een andere bekende in data science is deze: Die vond ik echter minder goed, vooral omdat er in R gewerkt wordt en ik heb hem dan ook nooit afgemaakt.

Deze is ook heel bekend; ik heb hem destijds gratis kunnen doen, maar voor het certificaat moest je wel betalen:

r/coursera • comment
2 points • feedtwobirds

I recommend the following specializations/courses offered by UofM. They have some of the best content and tools I have seen. The interactive python textbook is so helpful. It makes it so easy to write small snippets of code to really reinforce the concepts without the overhead of download and set up of different applications. It keeps you focused on the concept at hand and moving forward fast. The tiny exercises, questions and practice tools really keep the brain engaged. I have found many times where I go to type of the couple lines of code to do something to realize I forgot the a colon or used [] instead of () or used function() instead of .function() because when I was reading thru the text or watching the videos my mind did not commit to memory all of the relevant details and syntax specific to this language.

Curious as to where you applying? I am looking for an all online masters in Data Science. I am actually thinking about UofM because I really like the format of all of their coursera content so far.

r/learnpython • comment
1 points • slidedish

Sorry it's no free.I'm beginer to , I did not find free resources, to be as explicit as possible, for my beginner level

I learned from here :


I do not advertise them but these have helped me

r/unimelb • comment
1 points • bankingBrah

Coursera python for everyone

Then do python 3 on coursera:

Then do python for data sci:

I'm on the last one rn and I really do feel my python is pretty solid but not out of this world. Also I began (from python for everyone) this winter and studied thru the semester while overloading so its defs not super time consuming

r/labrats • comment
1 points • AlchemicalAle

I was actually in your shoes a little while back (starting my PhD with no real coding experience). Funnily enough, I also wanted to learn about coding with an emphasis on bioinformatics (Python & R). For that, I've been working through a couple of Coursera specializations. The main reason I chose Coursera was so that I could put the completion certificates on my LinkedIn, which I'm hoping gives me at least some minor legitimacy over someone just *claiming* to have experience.


If you're looking for specifics, here is a list of the specializations I'm using:

r/datascience • comment
1 points • apoptoticalex

I was looking at these programs off Coursera:

  • Hopkins (

  • IBM Intro (

  • Michigan (

  • IBM DS (

They have various estimated time lengths, which is understandable since they seem like they're pretty intense courses. I think I'd be able to handle finishing them faster than the estimated (or at least some more immediately relevant sections) since I've been juggling working full-time and being a full-time MS student... But idk.

Are there other Intro to DS courses you'd recommend? There are some other I've found here-and-there online, but I don't know how well they prepare people...

r/finance • comment
2 points • romper_el_dia

The Applied Data Science with Python specialization offered by UMichigan on Coursera is a great place to get started learning Python, which I recommend as it is industry standard and has so many sweet packages (e.g. Apple’s Turi Create and Google’s TensorFlow ).

Thereafter, check out to learn how to code structural economic models. And, finally, John Cochrane’s webpage and blog are also great resources. Note that John Cochrane literally wrote the textbook on asset pricing.

Hopefully this helps!

r/datascience • comment
1 points • RexLaurus

Hello everyone,

I'm trying to get into a data science from from a Electrical Engineering background.

I recently graduated and have experience coding in matlab and python (and others). I've completed a couple of the 365datascience courses that were free for a while as well as some of the kaggle micro courses.

I'm trying to find something a little more challenging and have been looking at Coursera specializations for way too long now, seems like everyone of those has one major drawback and I'm having a lot of trouble deciding between them. In short:

- John hopkins : Uses OCTAVE and from what I understand Python is the way to go.

- Standford (Andrew NG) : Said to be very theoretical, doesn't prepare you to make your own projects.

- U Michigan: Said to be outdated, problems with exercises scoring, bad reviews all around.

(This information is based of reading reviews and reddit posts so I may well be wrong. )

I've also found University of Washington's and IBM's that seem to have good reviews but I'm not sure nonetheless.

Can anyone point me towards the best one of these (or others) considering I have previous coding experience and know some of the very (very) basics of Python DS libraries?

r/datascience • comment
3 points • prashant9321

r/OMSA • comment
1 points • okamilon


I took Python for Everybody and it was a very good introduction. There's a Python for Data Science (also from U Mich) that apparently is pretty good too:

Another good class I took prior to the OMSA was Mathematics for Machine Learning (a three-course specialization): It was very helpful to have a general idea of what Machine Learning is and a good refresher of Linear Algebra and Calculus.

I have heard pretty good comments from this class: They use Matlab (which is one of the two programming languages you can use in the ML class, so it would be nice to have that skill).

Apparently the hardest compulsory class of the program is Data and Visual Analytics which has 4 50-60-hour projects your are meant to finish in 3 weeks each. So you will need to quickly understand tools such as SQL (, Linux Command Line (, D3.js (, among others in the cloud.

In general I agree that most of what is taught during the master can be learned on each class as their are self-contained, but being prepared in advance can help you have a better experience and learn more in-depth content.

All the best!