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
Accessible for free. Completion certificates are offered.
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Taught by
Mine ÇetinkayaRundel
Associate Professor of the Practice
and 3 more instructors
Offered by
Duke University
This specialization includes these 5 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
5 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
21 mentions
The capstone project will be an analysis using R that answers a specific scientific/business question provided by the course team.
Merlise A Clyde
0 mentions
Reddit Posts and Comments
4 posts • 107 mentions • top 53 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 • 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.)
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.
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.
3 points • secretnsaaccount
https://www.coursera.org/specializations/statistics
3 points • secretnsaaccount
https://www.coursera.org/specializations/statistics
I think you can still follow along with the lectures and access the text for free.
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.
1 points • Geeiveturkey
I agree. Depends on the long term goal. FYI to OP. https://www.coursera.org/specializations/statisticswithpython
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 • teksag09
R programming to be more precise. Check out these courses by the same instructor : https://www.coursera.org/specializations/statistics
1 points • rhinocer
Every Data Science Masters math prerequisite is at least collegelevel course or equivalent knowledge in:
 Multivariate Calculus
 Linear algebra
 Probability/Statistics
But, UMSI MADS has no such prerequisite. I even wrote them an email to make sure, they responded nope, you don't have to know any of that, except what is in their Statistics with Python Coursera specialization.
I wonder how's that possible because from what I've read, these math skills are essential if you wanna be a good Data Scientist?
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 • secretnsaaccount
Statistics with R  Duke University https://www.coursera.org/specializations/statistics
1 points • etylback
Because if they publish the price, it would probably mean less clicks though the ad network they are using to monetize those posts (I mean that site, not Coursera). As with most specializations in Coursera, the price is U$D 49 per month, and there are 3 courses in this specialization.
Here's the link to the course:
https://www.coursera.org/specializations/statisticswithpython
The price is shown when you click the "Enroll" button.
1 points • notsoslimshaddy91
Career Question 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 (https://www.coursera.org/specializations/statistics) . 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.
1 points • notsoslimshaddy91
Career Question 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 (https://www.coursera.org/specializations/statistics) . 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.
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.
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?
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 • 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 • 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.
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!
5 points • seismatica
I've been struggling to find an online mathematical statistics course with video lectures to prepare myself for learning ML, and 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 I 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) It combines 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
6) R programming is used liberally in the book and the homework, which is great for those who want to learn the material through programming
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!
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.
0 points • MichiganOnline
Learn How to Conduct Statistical Analysis using Python  3 Online Courses from the University of Michigan
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!
1 points • Modmanflex
5 course Free R programming specialization from Duke University and Coursera
If you want a certificate for each class and the capstone there is a minimal cost. https://www.coursera.org/specializations/statistics
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 • Garuda1220
I am also interested in learning more about using Python for statistical analysis.
Has anyone out there completed the Coursera/UofMichigan Statistics w/ Python Specialization course?
I recently wrote a couple of short stats programs for a class that performed ANOVA and Linear Regression analyses using scipy.stats
, stats.f_oneway
, scipy.stats.linregress
.
I used as a reference just to look up how to use a couple of functions the following websites:
1 points • alberlovag
Critique my learning plan
Background
Ive just finished my bachelors in finance, work as a data analyst (basically an excel monkey). I dont have much coding knowledge, Ive basically done a python bootcamp 2 years ago, but I barely remember anything sincs I didnt use it since. I want to get into data science so Ive been looking around for free online courses and putting together a learning path for myself based on what Ive read. Im curious what you guys think.
Study Plan
So, from what Ive read, basically data science is a combination of programming, statistics and maths. However, Ive also read that most of the time its not worth getting stuck down on the theoretical/math part of it in the beginning, If I will need more maths later on, I will learn it then. All in all, Ive put together my study plan keeping the following criteriad in mind:

Free (or cheap)

Preferrably video/course format, but books are okay too

Focus on Python, since I have some experience with it

Focus should not be too much on maths/theory in the beginning.

Statistics with Python from the University of Michigan (3 months)  it would provide me a good basis in statistics and python

Andrew Ng's Coursera course (12 months)  for machine learning

Something on deep learning (havent decided yet)

Probability course from MIT

Kaggle competitions
What do you guys think?
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 • GabrielXCrescendo
Path towards a data science route from no CS experience?
Hi all, I am in a dilemma as to how to begin in computer science. I would love to hear your recommendations as to how I can best embark my learning journey.
Background
I graduated with a degree in Finance 5 years ago and have been working as an analyst in financerelated institutions (Banks, investment holding companies, etc.). My main responsibilities included analysis, report generation and analysis using Microsoft Excel. Recently, I moved to an operations role (as an analyst) in a logistics company. The job requirements are broadly similar, but my department is expanding towards data analytics. Coming from literally 0 programming knowledge, I researched online and Python seems to be one of the more popular programming languages used for data analytics.
Readings & Practicing
I started learning the basic of Pythons with Coursera's Programming for Everybody (Getting Started with Python). This gave me a fundamental understanding of Python and it's capabilities.
Upon completion, I started viewing Corey Schafer's videos on YouTube. I have also enrolled in edx's MIT's Introduction to Computer Science and Programming Using Python which started this week.
After speaking to my manager, he recommended me to first understand the fundamentals behind data science and understand the scientific approaches to learning from data and using it to tell the story.
To start off he recommended Coursera's course on Basic statistics. After which, he recommended me to combine what I've learnt and embark on Statistics with Python.
All of this is overwhelming as I find myself jumping back and forth between learning Python and Statistics. If I were to focus on Statistics, how indepth (Advanced Statistics?) do I have to go before embarking on Python?
Any comments is greatly appreciated! Thank you
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 • kevinlwei
Here are a couple places to start:
 This free stats textbook is an OK introduction and is also not very theoretically rigorous: https://www.openintro.org/stat/textbook.php?stat_book=os
 Intro to Statistical Learning w/ Applications in R (commonly known as "ISLR): also freely available online, will teach you the basics in very broad strokes + applications to machine learning/data science. Much of the theory is glossed over in favor of examples + applications: http://faculty.marshall.usc.edu/garethjames/ISL/
 UMich has a free stats course available on Coursera in Python. I haven't taken it, but it looks to be a decent intro from the contents (the other ones on Coursera are taught in R, which I imagine will be less useful in the OMSCS context). https://www.coursera.org/specializations/statisticswithpython
 For something more indepth, MIT has a series of 5 courses on edX: https://www.edx.org/micromasters/mitxstatisticsanddatascience
 If you really want to learn statistics at a graduate level, you can take a look at Casella & Berger's "Statistical Inference," which is the classic inference text (and iirc includes 34 chapters on probability theory). It's probably much more indepth than you would need for OMSCS, but if you intend to get this deep you should read this before you read ISLR/other things.
If you want to learn "probability" specifically, I'd start w/ the Casella/Berger. Probability theory is a subset of statistics but probably won't be the focus of any OMSCS courses (which would be much more regression/inference focused).
0 points • GabrielXCrescendo
Recommended path to start data science?
Hi all, I am in a dilemma as to how to begin in computer science. I would love to hear your recommendations as to how I can best embark my learning journey.
Background
I graduated with a degree in Finance 5 years ago and have been working as an analyst in financerelated institutions (Banks, investment holding companies, etc.). My main responsibilities included analysis, report generation and analysis using Microsoft Excel. Recently, I moved to an operations role (as an analyst) in a logistics company. The job requirements are broadly similar, but my department is expanding towards data analytics. Coming from literally 0 programming knowledge, I researched online and Python seems to be one of the more popular programming languages used for data analytics.
Readings & Practicing
I started learning the basic of Pythons with Coursera's Programming for Everybody (Getting Started with Python). This gave me a fundamental understanding of Python and it's capabilities.
Upon completion, I started viewing Corey Schafer's videos on YouTube. I have also enrolled in edx's MIT's Introduction to Computer Science and Programming Using Python which started this week.
After speaking to my manager, he recommended me to first understand the fundamentals behind data science and understand the scientific approaches to learning from data and using it to tell the story.
To start off he recommended Coursera's course on Basic statistics. After which, he recommended me to combine what I've learnt and embark on Statistics with Python.
All of this is overwhelming as I find myself jumping back and forth between learning Python and Statistics. If I were to focus on Statistics, how indepth (Advanced Statistics?) do I have to go before embarking on Python?
Any comments is greatly appreciated! Thank you