Statistics with R
Below are the top discussions from Reddit that mention this online Coursera specialization from Duke University.
In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural ph...
Bayesian Statistics Linear Regression Statistical Inference R Programming Statistics Rstudio Exploratory Data Analysis Statistical Hypothesis Testing Regression Analysis Bayesian Linear Regression Bayesian Inference Model Selection
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Taught by
Mine ÇetinkayaRundel
Associate Professor of the Practice
and 18 more instructors
Offered by
Duke University
This specialization includes these 4 courses.
This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule.
Mine ÇetinkayaRundel
10 mentions
This course covers commonly used statistical inference methods for numerical and categorical data.
Mine ÇetinkayaRundel
7 mentions
This course introduces simple and multiple linear regression models.
Mine ÇetinkayaRundel
4 mentions
This course describes Bayesian statistics, in which one's inferences about parameters or hypotheses are updated as evidence accumulates.
Mine ÇetinkayaRundel
6 mentions
Reddit Posts and Comments
3 posts • 142 mentions • top 42 shown below
37 points • Velluvial
Online Stats Course
Hi r/statistics,
With the plethora of options for statistics courses online, which would you guys recommend for someone who hasn't taken a stats course in four years? I've been doing the Khan Academy Statistics and Probability, but I wasn't too sure if this would be the best resource.
Looking at past threads, I've seen people mention Duke's Statistics with R Specialization, These RBloggers videos, and a few others.
I want to learn statistics to potentially switch careers into data science. So, I'm leaning towards the Duke course, but I don't know if that covers the basics or not and a refresher is more than needed for me.
In the end, I know doing any of them would be better than doing nothing, but I just want to hear other people's opinions. Thanks in advance!
45 points • seismatica
Intermediate statistics course (with lecture videos and free textbook) from the Technical University of Denmark (DTU)
I've been struggling to find an online mathematical statistics course with video lectures, since the majority of online statistics courses don't use much math (such as the Duke's Statistics with R course or Bekeley's Statistics 21. The only true mathematical statistics course with video lectures that could find was CMU's 36705, but the video lectures' quality is quite poor.
However, today I accidentally covered a statistics course that partially met my criteria. It's the Introduction to Statistics course from the Technical University of Denmark. What I like about this course is:
1) The textbook is free!
2) Video lectures are available (under the 'Podcast' tab of the course website)
3) Homework and solutions are available, as well as exams going back a few years (again, with solutions!)
4) Most formulas have mathematical derivations (though it might not be quite as rigorous as an standard mathematical statistics course e.g. calculus was not present much, if at all)
5) Combine both probability and statistics so someone who wants to refresh both topics or learn them both for e.g. machine learning could accomplish quickly in one course.
I have taken the Duke's statistics courses on Coursera, but will use this course to strengthen my probability and stats knowledge before I embark on a real Machine Learning course (I'm looking at CMU's 10701 by Tom Mitchell). Hope you guys find this course useful as I do!
8 points • amoun1365
Is there any good statistics specialization (MOOC, certification) with Python?
I heard great stuff about Duke's R Statistics specialization. Is there any similar program with Python?
Here's Duke's R Statistics Specialization https://www.coursera.org/specializations/statistics
7 points • secretnsaaccount
I really like the Stats with R specialization on Coursera. It doesn’t assume much and you pick up some R in the process.
https://www.coursera.org/specializations/statistics
6 points • greatdarkbeyond
How's my plan to self teach myself to become a Data Scientist?
So I studied something completely irrelevant in college (History) and landed a pretty good job a few years out of college in a major company as a BI Analyst. Even though it's a big company, the department I work in runs more like a start up, so I get to wear all sorts of different hats and manage everything in analytics.
I've been here a year and my plan is to use this job as my playground to learn and experiment. When I first started, everyone was just using Excel and doing everything really manually, like joining data sets using vlookup, remove duplicates, and then sumifs (uh yeah, you can imagine how long this took).
Thanks to coursera and online tutorials (classes are expensed by the company which makes this even sweeter), I self taught myself SQL and began implementing and using RDMBS. I first used Access and then am slowly learning and using SQL Server 2014 (free version). It's definitely saved me a lot of time on reporting and my boss has praised me for being innovative and proactive.
I'm currently taking a class on data visualization and communication with Tableau on coursera. Once I'm finished with that, I'm planning on taking courses on stats and R, specifically this: https://www.coursera.org/specializations/statistics
Do you think after I take all these classes, coupled with practical work application on my job, I can essentially become a self taught data scientist in a matter of 23 years of total experience? It kinda of amazes me that I can literally dive into this profession without any formal training  I mean I'm just taking online classes and applying what I learn to my job.
I'm also learning some of the data back end stuff, in part because I have to. This entails like setting up SQL Server, developing an ETL process, etc. Do you think this is something I should focus on? I should note that the only reason I feel I need to learn this is because my current job doesn't have any engineering resources on my team to implement it for me. I figured I would just do it myself, but I know if I were to move on to another company with dedicated support, there usually would be staff that does all this for me.
6 points • syntheticcfm
Moving from Software Engineer to Data Scientist
Hello Everyone,
I've been thinking about switching from my current career of doing mostly web development to becoming more of a date scientist.
I don't know exactly how I'd like to do this but I imagine it would involved some sort of online course (https://www.coursera.org/specializations/statistics  this is a paid course but I'm okay with a free one if we can find a good free one) and a good buddy to partner up with to help move things along.
I'd like to the person I team up with to already be a software developer of some sort so there isn't that gap to bridge. I've been working for roughly 3ish years in industry, so anyone with similar or more experience works for me.
I'd expect us to do this course fairly independently, check in on each other, talk about concepts on google hangout. Those types of things.
If you are interested please let me know below and give me a little bit of your background if you can :)
(my background Grew up in Connecticut, went to a small liberal arts school for English but ended up getting a BA in CS instead. Then did a web bootcamp in san francisco, got a job there, and have been working and living in SF ever since.)
5 points • Chrono803
I learned a ton from taking the Coursera's statistics courses from Duke University. It's a whole specialization, but you can take individual classes. The textbook they used is free for the ebook and is very well written.
18 points • my_password_is______
just started two days ago
https://www.edx.org/course/theanalyticsedge
What you'll learn
An applied understanding of many different analytics methods, including linear regression, logistic regression, CART, clustering, and data visualization
How to implement all of these methods in R
* An applied understanding of mathematical optimization and how to solve optimization models in spreadsheet software
there's also
https://www.coursera.org/specializations/statistics
https://www.edx.org/professionalcertificate/harvardxdatascience
https://trevorhastie.github.io/ISLR/
3 points • AcceptableCause
I want to learn the relevant statistics for epidemiology and PH. Resources?
I'm studying medicine and am very interested in PH and epidemiology. And statistics are obviously a very important tool and a fundamental understanding seems very important to me. I want to understand why certain models and tests are used for certain studies, how to interpret them etc.
However, our stat course at uni is almost nonexistant. It's too condensed. I sort of understand now when to use ttests and some other tests, and what the basics are (confidence intervals, etc), but somehow the foundation is missing. Ultimately i'd like to be theoretically able to do the statistical analysis myself for a study.
Obviously this is a very ambitious goal, because studying medicine already leaves me with little free time, but I'm willing to invest time. Could you recommend any introductory lectures, books, exercises, and whatnot so I can I can build my knowledge from the ground up, at a slow pace?
I've found this on coursera: Statistics with R Specialization
Is it a good start?
Tl;dr: Suggestions for resources on how to develop a basic (and later deeper) understanding of statistics especially for the epidemiology and medicine?
2 points • ndha1995
Coursera's Statistics with R Specialization  learn statistics and get your hands dirty with R on the way. You can audit each course for free, but if you want access to graded assignments you'll have to pay up. If you have a CS degree you should already know basic Calculus and Linear Algebra right? At least when I attended college, my school's CS degree required 4 semesters of Calculus and 1 semester of Linear Algebra.
2 points • notsoslimshaddy91
Working professional here, should I pursue certification: Statistics with R specialization ?
I am a BI developer with 3 years of working experience. I have worked on ETL and Reporting. I think the next logical step for advancing in my career is Data Science. I have a fairly good understanding of business. My goal is to work in technobusiness role in near future. I do not have any technical certifications and I think getting certified in Statistics will be beneficial no matter what course my career takes. I have an intermediate understanding of R and can work my through problems. Currently, planning to take the Statistics with R Specialization Statistics with R Specialization . Just want to know your views on the course syllabus and I am open to suggestions/ alternatives to these certification. Also, you can recommend any other technical certification that will be a plus for my goal.
4 points • ARCU5
I was actually deciding between this one and the Duke Statistics with R course https://www.coursera.org/specializations/statistics?
3 points • Scalar_Mikeman
Basic Statistics Class or Bayesian Statistics Class
I'm finishing Analytics Edge on EdX in the next week or two and I am looking to focus on learning statistics. I failed statistics twice in college before passing. I know the bare basics like mean, median mode and standard deviation. I don't really understand how to interpret P values, Z scores and I see things I'm unfamiliar with floating around like T tests, Kurtosis, Bayesian Inference etc. My question is, for my next course should I look for a straight Introduction to Statistics Course and then a Bayesian Statistics Course later or go straight into a Bayesian Course? Any recommendations for MOOCs. I was looking at this set of courses from Coursera https://www.coursera.org/specializations/statistics . Has anyone taken it? Any reviews/thoughts?
1 points • callmecuriousperson
The best stats basics course IMO is the Statistics with R Specialization.
The instructor, Mine ÇetinkayaRundel explains concepts in a very simple and easy language, that you won't forget the concepts at all. If you're not keen on using R, you can skip some of the videos/assignments for now, although learning R would be very helpful in the long run.
1 points • Lastteabender
Koi online Statistics course suggest karega?
Duke ka mila hai ek: https://www.coursera.org/specializations/statistics
Kisi ne try kia hai kya?
1 points • naitzyrk
A pleasure! I found myself in the same situation before knowing R.
I didn’t have any background in statistics. I’ve worked some times with SPSS but nothing relevant, I’m pretty much still learning by myself.
Regardless, having some background in statistics sure is helpful, but nothing you can’t learn by yourself by investing some time into it.
There are also courses on the internet, such as in coursera, that teach statistics with R. I haven’t taken it but I’m looking forward to it.
1 points • teksag09
R programming to be more precise. Check out these courses by the same instructor : https://www.coursera.org/specializations/statistics
1 points • geebr
https://www.coursera.org/specializations/statistics
A more advanced one is Mathematical Biostatistics Bootcamp 1 & 2. Both very good, but requires a bit more mathematical literacy. Would recommend not skimping on the maths.
1 points • holken11
I have done the Statistics with R specialization on Coursera. As a multicourse specialization, it may be more extensive than what you're looking for, but it does exactly what you asked for, and it is very good.
2 points • tjscollins
Although I took Linear Algebra and multivariate calc many years ago, I plan on doing this before applying to OMSCS: https://www.coursera.org/specializations/mathematicsmachinelearning and https://www.coursera.org/specializations/statistics
2 points • iMakeBaadChoices
[Q][E] Would really love some advice on some online courses I can take to supplement my learning
Hello! I'm a returning statistics major who hasn't done any sort of stats in a very long time. I swapped back and forth in my major and landed back into statistics so here I am. I remember taking a probability course and statistics course which was really math and proof heavy. I then took a regression course. This was all about two years ago and I barely remember anything to be honest. I'm starting my upper year courses now and I'm quite lost to say the least.
As for the upper year courses, I'm taking a course called "Categorical Data Analysis" which seems to be my hardest upcoming course. We're doing a bit of proofs but we're mostly working with different distributions like poisson and such. The description for the course says we cover this.
> Statistical models for categorical data. Contingency tables, generalized linear models, logistic regression, multinomial responses, logit models for nominal responses, loglinear models for twoway tables, threeway tables and higher dimensions, models for matched pairs, repeated categorical response data, correlated and clustered responses. Statistical analyses using SAS or R.
I then have another course called multivariate analysis. This seems okay so far since we've mostly been doing linear algebra review but now we're getting into discussing the multivariate normal distribution and I'm already feeling like I'll be left behind. The description for this course is as follows
> Linear algebra for statistics. Multivariate distributions, the multivariate normal and some associated distribution theory. Multivariate regression analysis. Canonical correlation analysis. Principal components analysis. Factor analysis. Cluster and discriminant analysis. Multidimensional scaling. Instruction in the use of SAS.
I'm also taking another applied data collection course which seems like it shouldn't be too hard.
I really do enjoy the field of statistics so I guess I'm just trying to relearn it well because I feel like I didn't learn it properly the first time (I still wonder how I passed), but at the same time, I wish to hopefully cover things I'll be going over in class since I'd like to do well in those courses even if I will be going back and relearning everything!
So, do you guys have any recommendations of any online courses I can take to help me? I prefer video lectures to just straight books as I feel like I absorb information better if that makes sense. Having someone explain it to me just feels better I guess.
I've looked around and I have come up with two options. First being Statistics 110 (tho I heard this doesn't go over statistical inference, which even though I don't remember what it is, sounds like something I've done in a few of my classes) and the second being the Duke statistics with R specialization.
Any recommendations will be welcomed, thanks so much!
2 points • ABatIsFineToo
Coursera statistics with a focus on R
https://www.coursera.org/specializations/statistics?action=enroll
Or, if you're pretty comfortable with stats already, I would use Khan academy or a similar resource to learn python and then you can Google the code for R suite and tweak it to your own liking, because 8/10 times someone has already done it
2 points • brazzaguy
I recommend the Coursera Specialization, Statistics with R by Duke University. The Specialization is based on the book OpenIntro Statistics. Both the specializations and the book are free.
If you want the certificate for the course but can't pay, you can ask for the financial aid.
7 points • KJ6BWB
So you want to write a story but don't really have any idea of the main part of what you'd be writing about, and you only have the bare bones of a plot?
> Probability deals with predicting the likelihood of future events, while statistics involves the analysis of the frequency of past events. Probability is primarily a theoretical branch of mathematics, which studies the consequences of mathematical definitions while statistics is primarily an applied branch of mathematics, which tries to make sense of observations in the real world.
I recommend taking a few months and working through:
 https://online.stanford.edu/course/probabilityandstatisticsselfpaced
 https://www.classcentral.com/mooc/2309/udacityintrotodescriptivestatistics
 https://www.classcentral.com/mooc/3048/edxsoc120xiheartstatslearningtolovestatistics
 https://www.classcentral.com/mooc/2244/edxut701xfoundationsofdataanalysis and the part 2
 https://www.coursera.org/learn/basicstatistics?siteID=SAyYsTvLiGQPK0cKnVLZVCAlLaxRqNOkg
 https://www.coursera.org/specializations/statistics?siteID=SAyYsTvLiGQEcjFmBMJm4FDuljkbzcc_g
 https://www.classcentral.com/mooc/1496/edx6041xintroductiontoprobabilitythescienceofuncertainty
You're going to need to understand statistics/probability to figure out the plot, because you're going to want the protagonist to discover/notice things that tie back in with the math.
Now you can handwave most of this, but you're still going to need at least a bare plot: the characters (protagonists, antagonists, and NPC's), the climax points, setting, etc.
1 points • rafaelocremix
Johns Hopkins university has excellent courses on r and data analysis on Coursera. It is one of the oldest running courses on the platform and they explain both the application and the math behind it. They also have public health specializations.
https://www.coursera.org/jhu
For a deep dive in statistics I suggest dukes statistic inference specialization.
https://www.coursera.org/specializations/statistics#howItWorks
1 points • lhink
Buscando cursos de estadística para data analysis y machine learning, vi que por acá recomendaban mucho éste: https://www.coursera.org/specializations/statistics. Yo lo empecé y, al menos el primer módulo, se me hizo muy ameno. Si lo tomás en modo "auditar" es gratuito, aunque no se pueden enviar los test para ser revisados
1 points • danjd90
Difficult to say how you'd handle the time commitment without knowing more about your educational background, your motivation for taking the course, or what you do professionally. From the course description:
> In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make databased decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique databased claims and evaluated databased decisions, and wrangle and visualize data with R packages for data analysis.
> You will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions.
https://www.coursera.org/specializations/statistics
They advertise the course as a beginner level course requiring no prior experience. In my view this course would be approachable to anyone with a moderatetostrong background in math (read: has taken linear algebra or higher level courses in the last 35 years), preferably with an intro to mathematical statistics somewhere in their prior training. Familiarity with frequentist or bayesian inference would be a bonus. An intro level exposure to any programming language is strongly encouraged.
If you have at least one of the things mentioned before, I think you'll be fine. The posted requirement is 3 hours per week. You can probably do it in 25 hours depending on your comfort level with portions of the material and your familiarity with the R programming language.
If you can afford the enrollment fee and you're interested in the certificate for building a portfolio of data analyses, it's going to be a good course. Duke is home to a top 20 department in statistics, and I'm sure they will give exceptional instruction in the course.
1 points • ewig94
Hey, thanks! For DS and bioinformatics I think you will find this one interesting:
Statistics with R (Coursera Specialization from Duke University)  Dr. Rundel teaches statistics decently.
and then maybe this one:
Mastering Software Development in R (Coursera Specialization from Johns Hopkins University)
Python and R are the two dominant languages in DS, so be prepared to learn both eventually!
2 points • Sarcuss
You can choose between the Statistics with R Specialization at Coursera, Statistics in Medicine at Lagunita Stanford or Foundations of Data Analysis 1 and Foundations of Data Analysis 2 at Edx
4 points • Wild_daluka
Confused on learning route
I know this question has been posted numerous times, but please bear with me.
There are way to many options out there that I don't know which route to take. I am currently majoring in Computer Science, and want to go into Data Analysis. I have taken one statistics course at my college, but don't have the options to continue on with that in school.
Right now, I am signed up for Python for Data Science and Machine Learning bootcamp, which I definitely want to finish. But it seems like it teaches you more about the tools of data science and less about the thought process and other techniques.
There's like an infinite amount of resources out there, and I'm confused about which one to pursue. For example:
 Dataquest.io
 Foundations of Data Analysis edX Part 1 Part 2
 The Analysts Edge edX
 Statistics with R Specialization Coursera
 Machine Learning by Andrew Ng Coursera
And so many more.
I graduate in May of 2018, so I'm trying to be marketable by December to give me a chance to find an actual full time job by next summer. I'm excited about Data Science and Machine Learning, so motivation is not an issue.
Does anybody have any suggestions on which of these I should pursue, or other options? I like the dataquest.io route because it's hands on, but it's also pricey. I also figure I should learn something with R since I have experience with Python under my belt already.
My thoughts are to take a statistics course to complement the bootcamp I'm taking.
What do you guys think?
1 points • MirrorLake
I've seen that there are a few ongoing R Langauge MOOCs going on at EdX and Coursera recently. I can't vouch for any of them, but it might help for you to audit one of them for free.
Edx:
https://www.edx.org/course/statisticsandr
Coursera:
https://www.coursera.org/learn/rprogramming?specialization=datasciencefoundationsr
And another one on Coursera:
https://www.coursera.org/specializations/statistics
3 points • Master_Broshi
Need guidance balancing and focusing my self study curriculum
I've been lurking around this sub for a couple months now. At this point I consider myself just past the stage of ignorance; I know what I don't know and I've started plotting out the resources I will need to know it. I've read just about every reddit guide and website curriculum (like elitedatascience etc) but now I am looking to solidify a plan that best suits my needs.
What I need help with is:
(1) prioritizing the order of my studies and
(2) maximizing the efficiency of the time I spend studying.
I am not looking for a "6 months to data science track" for obvious reasons, but I also can't take on 10 superthorough6monthlong MOOC's that overlap in what they teach or I'll never hit my goals. So basically, what should I focus on first, what should I save for later, and what subjects are good to learn in tandem?
Some background about myself:
 BS in Finance and Information Systems (double major)
 MBA w/ concentration Finance
 7 years experience as a Businesss Analyst/Product Developer/Project Manager, primarily in FinTech
 Currently working for a small startup with a team of data scientists. Job responsibilies include finding/cleaning/storing their data, running and QAing their models, and working with development teams to automate these tasks.
 I have expressed intrest in learning more about data science and they have encouraged me down this path.
My skillset and self ratings:

Translating business requirements (5/5)

SQL (4/5)

Python (2/5)

Statistics (2/5)(Would say 3, but I realize I'm talking to real statisticians here)

Data gathering/cleaning/elementary data analysis (2/5)(would say 4/5 by manually using SQL and Excel, but I am trying to take this to the next level; ie automation, prediction, and reusable tools)
Steps I've taken (a bit scattered):

Learning Python (on and off the past year, much more focused the past several months)

Resources : Automate the Boring Stuff, Absolute Beginner's Guideto Python, checkIO for practice, some on the job practice, and pet projects (NFL stats or whatever RPG I'm playing)

Focusing not only on getting things done, but on how to get things done in a pythonic fashion.


Data Science from Scratch as a syllabus.
 Using this to get from 0 to 1. Great handbook for basics.

Started the first few chapters (or lessons) of:

Think Stats  not sure I like the format and all the extra work you have to do to even use the book

Linear Algebra videos/lessons  I've got the absolute basics down, but I'm sure I need more

Introduction to Statistical Learning  Realized I needed some more math refreshers first. Also self conscious about jumping into R without a firm enough grasp on python

Machine Learning class by Andrew Ng  Got through week 3 but feel like I need to refresh my maths first or I'll be getting too ahead of myself.

Next Steps I'm mulling over:

Full computer science intro?
 I'm getting a good handle on Python but am not sure if I should complete something like CS50 or if that is overkill.

Take a full Statistics course or a refresher?

I know down the road I will need more and more advanced stats, for now I need to focus on what I'll need to get started.

I know regression, multiple regression, ANOVA tables, and some more from quantitative finance classes. But could stand to refresh these topics.

I don't know Bayes and am not sure when the best time to learn this will be.

The Duke class is supposed to be great, but I am not sure if I need a 6 month long Statistics program.


Full Linear Algebra course or stick with the basics until I need more?

Have heard both points argued, not sure which I should pursue.

Course options are Khan Academy or Harvard


Single and Multivariate Calculus

Need a refresher for single (haven't looked at it in 10 years) I hear Khan Academy is good, I also just dug up my old textbook.

Never learned multivariate
 How important is this? Should I take a full class or focus on the basics for now? Again, I've heard Khan Academy is good.


Machine Learning

Introduction to statistical Learning or Andrew Ng's Machine Learning class or both?
 Both are highly touted and from what I've seen in both's introductors classes they are very well tought.


Harvard's CS109 Data Science Class
 Are classes like this too general or would this be a good course to take?
Down the Road for later (I know I'm not there yet):

More and more stats

Essentials of Statistical Learning (I can only hear it refered to as the bible so many times)

Neural Networks

AI

Big Data (Hadoop/Spark)

Natural Language Processing
Conclusion:
I have done a lot of research on what I will need to know and have started or tried out a bunch of material. I've weened out plenty sources of I need help focusing my effort
edit: formatting
1 points • ludji97
First of all, thanks for the fantastic write up. Secondly, you are correct, I want to firmly grasp the subject, as I've heard from a lot of people in the corporate sector that econometrics are a must. I'll addres your points as you've laid them out.
(1) Excellent, I've been looking at this course, but got scared it was too advanced for me. I'll definetely dig into it if it's my level.
(2) Another excellent recommendation, I'll go through it as they state it's pretty basic. If anyone is reading this later, here's the link to their edx course: https://www.edx.org/course/probabilitythescienceofuncertaintyanddata0
Since we're on this subject,what do you think of this course: https://www.coursera.org/specializations/statistics
Since it teaches R as well, I'm inclined to check it out. It seems even lighter than the MIT one, so maybe I should go through it first or is that a bad idea?
(3) I had it on my first year, same as stat, but yeah, I need a refresher, so I will look at more MIT courses!
Once again, thanks for answering, it means a ton to me!
1 points • EduGuy33
Have you seen this:
 first 2 courses in this program (Python): https://www.edx.org/micromasters/datascience
 this program (statistics and R): https://www.coursera.org/specializations/statistics
I can't comment on how good they are, and this will also depend on your requirements and prior knowledge. But it could be interesting for you to have a look!
1 points • QuandlFgt
I took the John Hopkins Data Science specialization a while ago when it first came out; it was in R (think it still is), but I thought it was pretty good. I honestly prefer R to Python personally, but it's much less popular in industry now. The course focuses a lot more on tooling then theory, but it's good to get you started
I took the Deep Learning specialization (deeplearning.ai) last year, it's good for learning the fundamentals of neural networks, it's in Python.
https://www.coursera.org/learn/machinelearning? is generally people's go to for an intro to machine learning; it's in Octave/Matlab which is unfortunate, but if you don't mind jumping around languages it teaches the fundamentals quite well
https://www.coursera.org/specializations/statistics is pretty good, I only took the first course but it's really good if you have a weak statistics background or just want to see if you have any gaps to fill in
There's some courses from U. of Ill. that are a part of there DS masters on their; I took the cloud computing one, it's basically distributed systems, honestly a lot more useful for a software engineer then for a data scientist but IMO it was a great class
I'm sure there's more, but those are the ones that I've tried or finished. It's just an awesome platform
1 points • neckturtles
I'm currently going through MIT OCW math courses and a statistics course on coursera and I'm finding them really well done
1 points • ACrispWinterDay
Your going to have a ton of theoretical math involved in a higher quality BS in statistics. In general it will include Real Analysis, Linear Algebra, Measure Theory, and Probability/Statistics for math majors. In these classes you will do about 80100% of your course work doing mathematical proofs, where you logically prove the foundations of calculus, probability, statistics, as well as what mathematical systems are and why they work.
If you really enjoy this kind of deep theoretical thought, then look up a curriculum for a BS in stats and just look for the courses or books online. If you want to just learn the basics of applied stats (pretty much what you would use in industry), just take some applied stats courses. https://www.coursera.org/specializations/statistics is a pretty good course, can take it for free.
Also, these books are great:
Intro to statistical learning: https://faculty.marshall.usc.edu/garethjames/ISL/
Elements of Statistical Learning: https://web.stanford.edu/\~hastie/ElemStatLearn/
The intro is much more accessible, still really deep. Elements is more along the lines of what someone with a classic training in mathematics/statistics would learm
1 points • PhilDick3
I took an earlier version of this MOOC, which I can highly recommend. It's put on by Duke University, starts with statistics basics and builds towards statistical learning methods using R. It was an excellent way to learn statistics/probability fundamentals in a practical way for me. Looks like the first two courses in this specialization are the content from the course I took? (scroll down to see all courses).
https://www.coursera.org/specializations/statistics
Here are some free ebooks that I have from the course, both have the instructor as an author, I'm not sure which or both are directly used in the course(s).
1 points • sparedOstrich
On the contrary to what people on willbell thread are saying, I have a BTech in Electronics and Communication engineering. I have learnt some coding languages like C, C++, python, along with coding languages in ECE like assembly, Arduino. I have used them to develop codecs and for audio signal processing. I think learning languages gives you an understanding of how you can use a machine to do what you desire.
So you can start with a MOOC like Chuck Severance's Programming for everybody. You can even audit it. Or you can use Youtube series like Bucky Robert's Python Programming Tutorials (OLD version, Python 2) or Python 3.4 Programming Tutorials. You can use this to learn python, in general, to do whatever you want. And then " you learn one statistic concept and then see how you can program the concept in r or python ".
Or if you want to learn statisticsintensive way, you can find MOOCs like
a. https://www.coursera.org/learn/pythondataanalysis
b. https://www.coursera.org/specializations/statistics
c. https://www.edx.org/course/statisticsandr
d. https://www.edx.org/course/probabilityandstatisticsindatascienceusingpython0
Now I am doing BSc in Statistics, Maths and Computer Science. (programming then statistics) order has benefitted me. I could easily learn other languages like Maxima or Scilab. I also have the option of implementing the code in a language I already know.
I personally feel "it's easier to teach a software developer statistics than it is to teach a statistician to program"  google or Amazon, as I see some of the old stats professors and research scholars at my University are devoid of programming skills and find it hard sometimes.
1 points • sarptas
I think you're looking for guides/tutorials/lessons on R. It's because RStudio, most basically, is an alternative GUI for R.
There exist numerous resources on R on the web.
QuickR is a good start point to learn R. https://www.statmethods.net/
A free course on R @Datacamp: https://www.datacamp.com/courses/freeintroductiontor
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Some courses @Coursera
R Programming course: https://www.coursera.org/learn/rprogramming
Statistics for R: https://www.coursera.org/specializations/statistics
Other R courses: https://www.coursera.org/courses?query=R
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And as you know, tons of video lectures @Youtube: https://www.youtube.com/results?search_query=r+programming
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Here is R documentation including many manuals:https://www.rproject.org/otherdocs.html
20 Free Online Books to Learn R and Data Science: https://cmdlinetips.com/2018/01/freeonlineresourcesbookstolearnranddatascience/
Learn R : 12 Free Books and Online Resources: http://ucanalytics.com/blogs/learnr12booksandonlineresources/
R related twitter accounts: https://twitter.com/sarptas/lists/r