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)

Accessible for free. Completion certificates are offered.

Affiliate disclosure: Please use the blue and green buttons to visit Coursera if you plan on enrolling in a course. Commissions Reddsera receives from using these links will keep this site online and ad-free. Reddsera will not receive commissions if you only use course links found in the below Reddit discussions.

Taught by
Christopher Brooks

and 3 more instructors

Offered by
University of Michigan

This specialization includes these 5 courses.

Reddit Posts and Comments

2 posts • 134 mentions • top 60 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. https://www.coursera.org/specializations/data-science-python

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 - https://www.coursera.org/specializations/data-science-python. 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 (https://sqlzoo.net/wiki/SQL_Tutorial) 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/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/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/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: https://www.coursera.org/specializations/data-science-python

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

r/IWantToLearn • comment
4 points • russybooboo

Check out: https://www.coursera.org/specializations/data-science-python

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/learnpython • comment
3 points • nomowolf

pandas dataframes... can be a little tricky to get used to as they're not an exact analogue of spreadsheets. But if you're doing anything that's repetitive or has a large amount of data in excel, you can set up pandas to do it all for you... and then more.

Course 1 and 2 of the Applied Data Science with Python were intense but very enjoyable and got me to this stage.

> How do you feed data into your python script?

import pandas as pd

df = pd.read_csv('file.csv') #or pd.read_excel('file.xlsx')
#do stuff
df.to_csv('file.csv') #output to csv if needed.

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 (https://www.coursera.org/specializations/jhu-data-science) and the Python one as well (https://www.coursera.org/specializations/data-science-python), 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:

https://www.coursera.org/specializations/data-science-python?authMode=login&ranEAID=vedj0cWlu2Y&ranMID=40328&ranSiteID=vedj0cWlu2Y-RlLXk1hRor095XtLFTlosA&siteID=vedj0cWlu2Y-RlLXk1hRor095XtLFTlosA&utm_campaign=vedj0cWlu2Y&utm_content=10&utm_medium=partners&utm_source=linkshare

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

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/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/labrats • comment
4 points • FlavaFlavivirus

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

https://www.coursera.org/specializations/data-science-python

r/programming • post
1 points • internetdigitalentre
Software Developers: Gain new high-demand skills while boosting your income potential - Applied Data Science with Python Specialization
r/datascience • comment
1 points • WarioBrega

Hello everyone, and sorry if this may sound a naive threads or if it has been replied before, but I did not find any relevant suggestion to my situation outside of this sub and I hope that I can find some useful advice here!

I'm a Bioinformatician that just finished his PhD in Systems Biology, with a particular focus on graph theory applications to Omics data. I was born a Molecular Biologist and later started to love coding and programming (mostly Python). During these years, while developing automated pipelines and my own methods, I started learning some basic statistics (real basic, sadly I wasn't given the right preparation during college) and I got acquainted some of the most basics concepts regarding data analysis and the way to visualize them properly.

During these years I started getting tired/bored of the Bio part of bioinformatics and I have come to get more and more interested in data science (data wrangling, ML basics, clustering and other exploratory data analysis techniques such as PCA, I think you know this much much better than me").

I always got a "glimpse" of what was under the hood of the methods and the techniques I used, although most of the times I used a leap of faith approach to many techniques and topics I would have liked to know more in detail (and alas I did not, mostly because of time and deadlines, but I can't say I have not been lazy sometimes).

Specifically, I found that I love math and statistics more than I thought, although, as I mentioned, I mostly understand it after I use some method rather than studying it from scratches.

Not that I finished my duties as a Bioinformatician (a job I'm still doing as a postdoc), I'm thinking to switch career and get more into Data Science. I'm not looking for jobs in the field (still), as I know I still have a lot to learn and to experience and at the moment my profile is not very "suitable" for Data-science related jobs outside my field, but I'd really like to and this times I'm committed to change path.

At the moment I've applied to the Data Science Specialization on Coursera and I'm struggling to follow it. I also bought some books mostly related to data wrangling to sharpen my knowledge of pandas and some of the R libraries I "underused" in these years.

I am still wondering what I'm missing and how long it will really take to start applying for DS jobs, and what I can possibly expect. Do I need to apply for Internships first? Or can I apply to "Junior" positions? Is my knowledge already enough for covering some areas of expertise or I've just started? I'm very confused (and a little bit overwhelmed) by the amount of information and the huge diversity I found online, so I hope that you can help me clarify some of my doubts and concerns.

If you need more information I'll be glade to give them to you, although for the moment I'd like to stay anonymous (I'm a little bit shy, I admit.)

Thanks for your time!

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/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](https://developer.tdameritrade.com] and I'm too cheap too actually pay someone.

Why did I do this?

Nothing to do for weekend, so took https://www.coursera.org/specializations/data-science-python and then playing around with jupyter notebooks

r/india • comment
1 points • iwannastudy

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

r/learnpython • comment
1 points • rndm_reddit_profile

https://www.coursera.org/specializations/data-science-python

r/datascience • comment
1 points • Tanren

Look at this https://www.coursera.org/specializations/data-science-python

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/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] (https://www.coursera.org/specializations/data-science-python), ma mi risulta essere a pagamento, come tutti gli altri corsidella piattaforma.

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. https://www.coursera.org/specializations/data-science-python

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. https://www.coursera.org/specializations/data-science-python

r/learnprogramming • post
1 points • ogyk24
Data Science and Machine Learning

I’m very interested in things like machine learning and artificial intelligence and would really like to learn how to program an AI, so I enrolled in the Intro to Data Science with Python course by UMich on Coursera. I just wanted to know if it’s beneficial for me to be taking this data science specialization because I heard that it’s best to learn data science prior to diving into the world of AI and machine learning. Or am I better off just taking courses on TensorFlow and AI? This is the course I’m currently taking.

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/cscareerquestions • comment
2 points • lolski_

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

https://www.coursera.org/specializations/data-science-python http://deeplearning.ai/

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: https://www.coursera.org/specializations/data-science-python

  2. IBM course: https://www.coursera.org/professional-certificates/ibm-data-science

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

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.

https://www.py4e.com/book

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.

https://www.coursera.org/specializations/data-science-python

Hope this helps!

r/datascience • comment
1 points • WarioBrega

No, I meant the University of Michigan Specialization (wrong link, here's the correct one: https://www.coursera.org/specializations/data-science-python and I'll edit my original post). Do you have any experience you can share on it?

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/datascience • post
1 points • KlutzyCoach
Courses on Coursera

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: https://www.coursera.org/specializations/data-science-python

  2. IBM course: https://www.coursera.org/professional-certificates/ibm-data-science

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

r/MachineLearning • post
1 points • nille_peter
my first approach/plan for learning ML

hey guys,

i want to learn something about ML and i thought i ask for your opinions about my resources/plan.

i've heard so much positive about the stanford course on coursera created by andrew ng (this) . so i want to take this course and since this course doesnt use python, but matlab, i want to take the 'Applied Data Science with Python' from the michigan university on coursera as well (this). i think i will take both courses parallel.

what do you think? is this a good combination? or would you recommend some complete other stuff for the beginning?

r/datascience • post
1 points • WarioBrega
Suggestions to step into Data Science (and data Analysis) for a Bioinformatician

Hello everyone, and sorry if this may sound a naive threads or if it has been replied before, but I did not find any relevant suggestion to my situation outside of this sub and I hope that I can find some useful advice here!

​

I'm a Bioinformatician that just finished his PhD in Systems Biology, with a particular focus on Graph theory applications to Omics data. I was born a Molecular Biologist and later started to love coding and programming (in , bu mostly, in Python). During these years, while developing automated pipelines and my methods, I started learning some basic statistics (real basic, sadly I wasn't given the right preparation during college) and I got acquainted some of the most basics concepts regarding data analysis and the way to visualize them properly.

​

During these years I started getting tired/bored of the "Bio" part of bioinformatics and I have come to get more and more interested in data science (data wrangling, ML basics, clustering and other exploratory data analysis techniques such as PCA, I think you know this much much better than me").

I always got a "glimpse" of what was under the hood of the methods and the techniques I used, although most of the times I used a "leap of faith" approach to many techniques and topics I would have liked to know more in detail (and alas I did not, mostly because of time and deadlines, but I can't say I have not been lazy sometimes).

Specifically, I found that I love math and statistics more than I thought, although, as I mentioned, I mostly understand it after I use some method rather than studying it from scratches.

Not that I finished my duties as a Bioinformatician (a job I'm still doing as a postdoc), I'm thinking to switch career and get more into Data Science. I'm not looking for jobs in the field (still), as I know I still have a lot to learn and to experience and at the moment my profile is not very "suitable" for Data-science related jobs outside my field, but I'd really like to and this times I'm committed to change path.

​

At the moment I've applied to the [Data Science Specialization](https://www.coursera.org/specializations/data-science-]python) on Coursera and I'm struggling to follow it. I also bought some books mostly related to data wrangling to sharpen my knowledge of pandas and some of the R libraries I "underused" in these years.

​

I am still wondering what I'm missing and how long it will really take to start applying for DS jobs, and what I can possibly expect. Do I need to apply for Internships first? Or can I apply to "Junior" positions? Is my knowledge already enough for covering some areas of expertise or I've just started? I'm very confused (and a little bit overwhelmed) by the amount of information and the huge diversity I found online, so I hope that you can help me clarify some of my doubts and concerns.

​

If you need more information I'll be glade to give them to you, although for the moment I'd like to stay anonymous (I'm a little bit shy, I admit.)

​

Thanks for your time!

r/OMSCS • post
0 points • laflameburrito
Need Advice: Associate's vs Post-Bacc

Hey Everyone,

I am really interested in the OMSCS program, specifically the Interactive Intelligence specialization. I know my current background would not be enough to get me into the program and my issue is trying to decide between pursuing an Associate's at a local CC or to do an online Post-Bacc in CS at Oregon State. I would really prefer to save money and go the Associate's route, but I don't know if this is enough to give me a good shot at admission. Here's an overview of my background:

​

Education:

  • Bachelor's in Business Information Systems | GPA: 3.0 | State University - Graduated in 2017
  • Relevant Coursework from Bachelor's: Introduction to Programming (C#), Database Management Systems (SQL), C++ for Business Applications, Systems Analysis & Design (Structured & OO Analysis & Design)
  • Applied Data Science Certificate | MOOC - Coursera/University of Michigan | Received in January 2019

Experience:

  • 2 Years as a Data Analyst at software company | Python, SQL & VBA work | Current Full-Time Position
  • 6 Months Data Analyst work for continuing education company | Python, SQL & Excel work | Contract Part-time position

​

I know I need to take some accredited CS courses to not only increase my chances of admission, but to also increase my chances of succeeding in the program if admitted. What do you guys think? Will getting an Associate's degree (and getting high grades in the classes) be enough to show I have the fundamentals of CS down and increase by chances from 0% to at least 80%? Or do you think getting a CS post-bacc is necessary in my case?

​

** I considered starting off with taking CS classes at my local community college and then applying to the post-bacc if I didn't get into the OMSCS after a couple of applications. But if I went this route, the classes from community college wouldn't transfer to the post-bacc and would be a waste of time/money. **

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Appreciate the help in advance!

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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.

​

IBM: https://www.coursera.org/specializations/ibm-data-science-professional-certificate?action=enroll

​

UM: https://www.coursera.org/specializations/data-science-python

r/Python • comment
5 points • Old_Kat

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

Coursera

EDX

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 python.org 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
https://www.coursera.org/specializations/machine-learning

course 3 in
https://www.coursera.org/specializations/data-science-python

course 8 in
https://www.coursera.org/specializations/jhu-data-science

all of
https://www.coursera.org/specializations/mathematics-machine-learning

various courses by Andrew Ng (Co-founder, Coursera; Adjunct Professor, Stanford University)
https://www.coursera.org/instructor/andrewng

any of
https://www.edx.org/course?search_query=machine+learning

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 https://www.coursera.org/specializations/data-science-python . 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 https://www.edx.org/python-for-data-science 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:

https://www.coursera.org/specializations/data-science-python

​

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

https://80000hours.org/

​

Good luck!

r/belgium • comment
1 points • bananensoep

De cursus waar ik het meest aan gehad heb, is deze: https://www.coursera.org/specializations/data-science-python

Een andere bekende in data science is deze: https://www.coursera.org/specializations/jhu-data-science 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: https://www.coursera.org/learn/machine-learning

r/datascience • post
1 points • velos
IBM Data Science Professional Certificate on Coursera, anyone taking / have taken?

Hi Guys,

Just wondering what your take is for this specialization from IBM on Coursera
https://www.coursera.org/specializations/ibm-data-science-professional-certificate

If we complete it, we'll get (in addition to signed certificate from Coursera), a digital Badge from IBM.
Does it mean anything that it carries the IBM name?

​

How does it compare to, say Applied Data Science with Python Specialization

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 :

https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/

https://www.coursera.org/specializations/data-science-python

​

I do not advertise them but these have helped me

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.

https://www.coursera.org/specializations/python-3-programming

https://www.coursera.org/specializations/python

https://www.coursera.org/specializations/data-science-python

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/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/learnmachinelearning • post
1 points • KlutzyCoach
ML courses Toronto/Online

Yes, I know python, pandas.. I want something that can make my resume more presentable apart from projecHello, 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: https://www.coursera.org/specializations/data-science-python

​

  1. IBM course: https://www.coursera.org/professional-certificates/ibm-data-science

​

​

As coursera needs investment I want to invest in a good course as I am limited on my budget too. Thanks!!ts I have been working on. I learnt that most of these courses are outdated? Is it true???

r/learnmachinelearning • post
1 points • iMakeBaadChoices
Which of these are the best for me?

Hey everyone! So long story short I have a decent stats background of the more theoretical side, and almost 0 experience/knowledge in the applied side. I've also of course taken courses like linear algebra, calculus, multivariable calculus, intro to probability/statistics, intro to regression. I would like to get a job as a Data Scientist once I graduate and maybe head into the ML field after if possible. I'm also doing CS on the side so I know Python.

I looked around for a few courses to prepare me for getting a job as a data scientist since I assume I'd be doing more of prepping data and creating models and making inferences rather than proving that the random variable X is an unbiased estimator (like damn I spent an entire month doing that in class lol).

I looked around Kaggle for some fun projects but I barely know what to do with the data. I managed to use the titanic data set and clean it up to the best of my knowledge (ie, replace null with mean values, drop a few columns, etc) and slap on XGBoost and tweak the parameters to get a score of 0.7~ but I felt like I didn't really know what I was doing.

Anyway, here's the courses I've found that seem good. I hope you guys can help me decide which to take!

John Hopkins Data Science Specialization (seems great but I'm a little sad it's in R and not Python)

IBM's set of Data Science courses

Applied Data Science with Python Specialization, this course looks pretty good and has good reviews but I found this highly upvoted thread talking about how bad and how much of a mess it is so I don't really know any more...

If you guys have any other recommendations, I'm all ears! And if the course has some math/stats to it, that's totally okay!

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 quantecon.org 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!