Data Science

What's a good pathway to developing data science skills?

My background is a B.S. in EE, and this fall I'll be re-starting a Masters in CogSci. I know basic Python, and I'm currently working through "Discovering Statistics with R" by Andy Field. I don't know SQL, and my LinAlg and Calc needs brushing up on.

Frankly, I'm lost in all the online resources. I've spent the better part of a day trying to compare courses like Udacity's Data Analyst Course and Coursera's Data Science Course keeping in mind other things people continue to mention like Peter Ng's Machine Learning Course and free resources like DataQuest. I can't figure out where my time would be best spent.

I realize for job searching the best thing to have is a portfolio of projects, but it'd be nice to have a structured approach for the moment. Any and all advice is appreciated. Thanks!

Here's a good course I saw online:

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

TL;DR Learn R

Job is willing to foot the bill for some data science training. Looking for suggestions.

Hey all,

As the title says, my boss just told me we are creating some data science positions and I've been interested in switching from engineering to data science since day one. They said that, in preparation for the move, they'd be willing to pay for something like a coursera course to get me up to speed.

I was wondering if any of you had any suggestions. I know that the JHU Coursera course is pretty good, but I've also heard it's a bit superficial. I saw that UW has a more technical-sounding course, but I wasn't sure it'd be useful in an industry setting.

Also, I should mention I do have an undergrad degree in math and spent some time in grad school doing theoretical physics, so I'm mathier than your average dev. Thanks!

Opinion on Coursera's Data Science specialisation

Hello everyone! I graduated last June with a Bsc in Psychology and have now a mandatory gap year to fill before my Master. During this year I would like to learn a bit more about Data Science or analysis in order to gather more knowledge on this matter. I've stumbled upon the Data Science program by Coursera and wanted to know if it was worth it or not as I've taken some courses by them and the quality was... let's say fluctuant; and also if it was relevant as I want to pursue my career in academics.

Thanks in advance for help and/or opinions!

Data Science Specialization at coursera

Could currently or recently enrolled students in [Johns Hopkins' Data Science Specialization at coursera] (https://www.coursera.org/specializations/jhu-data-science) comment on whether the courses are handicapped for non-certificate enrollees?

I tried them out in December 2015 and suddenly in January 2016, mid-course 4/9, I could no longer see how I did on quizes, hand in assignments for peer-review, or review other peopls work. I stopped shortly there after - without those features these are just a bunch of lectures. I wanted to know if that was a glitch, or if the courses are meant to be like that for non-cerficate students from know on?

Data science is a word that covers a lot of stuff. A common form of data science is the analysis of website traffic and various stuff like A/B testing and analytics of browsing, and more recently natural language processing, but there are more sophisticated forms as well in science or financial forecasting, not to mention various automation with deep learning. The former things I'd rate as technically the easiest and with some programming skills and basic statistics you can learn the subject matter either by self-study or some online learning (on that note I've heard good things about this Coursera program ). One thing you shouldn't underestimate is the value of a recognizable degree in this area. So self-study is great, but it limits your ability to get through the superficial review that human resource departments often do. Finally, the more advanced types of data science and machine learning are tougher and you can pick up basics on your own I'm sure, but without at least an undergraduate degree in a quantitative science or engineering this will be tough to master and be professionally competitive in.

I want to become a Data Analyst, help me find my way!

Hello Guys,

So, first I want to give you some starting info:

• 27 yrs
• Mechanical Engineer
• Likes programming, but has no knowledge/experience of it*
• Strong Analytical skills

*1 class of python in college

I recently moved to London and want to pivot my career to Data Analysis. But as someone with basically no prior experience and knowledge of the area, I fully understand there is going to be a lot of studying for the next couple of months. Although I do not let this get me unmotivated, I want to maximize my study time by asking you guys this question:

What courses / programming languages / certificates should I go for in order to land a job as a Data Analyst in London?

After doing some research I have come to this:

1. Data Science Specialization on Coursera

Why? Because it is cheap, self-paced, online and gives me a foundation.

1. An SQL Course + Certification

Why? Because a lot of jobs require that you know SQL.

What you guys think about this? Would you suggest something different?

Would love to hear from reddit! Thank you!

¿Qué tan factible sería tomar alguno de estos cursos de Big Data y dar un giro laboral?

Estoy pensando tomar alguno de estos cursos de Big Data mas por curiosidad que por otra cosa, pero sería chingon terminar trabajando en algo así. No se que tan suficientes sean estos cursos para empezar.

[Data Science - Coursera.](https://www.coursera.org/specializations/jhu-data-science#about) (\~40 sem)

[Diplomado Técnico en Big Data - Aprende.](https://aprende.org/pages.php?r=.cfcs_oficial_infographic&diplomadoID=dtbd) (yo le calculo 8 sem)

Actualmente soy ingeniero en una rama no relacionada, y ando ganando poco menos de $7,00 a la quincena. He escuchado mucho del Big Data y no sé que tan conveniente sería prepararme para trabajar en otro giro laboral (que ya me causa curiosidad). No pretendo terminar como 250k MasterRace, pero si mejorar mis prospectos a futuro.

¿Gente que se dedica a esto como ven el panorama? ¿Podría empezar a hacer una carrera laboral con esto?

John Hopkins had a cycle of 9 R courses for data science on Coursera. They are increasing in complexity and focus so you can choose the complexity level that suites you - here https://www.coursera.org/specializations/jhu-data-science

I really think the main issue with learning through datacamp is that you do not build a library of code that you can refer back to, so you will find yourself often going back to the same videos, where as if you had the code saved off somewhere you could just go back and look.
In some cases, where they use default data sets in R like the Iris or Cars data set, you can get around this by copying your finished exercises and saving them into an R script. As you can imagine, this can get tedious. Additionally, I have run into some courses that are using proprietary data, which is not easily found online. Once you have built a decent foundation, I highly recommend the data science specialization from Johns Hopkins on coursera. You can audit the class for free, or if you want the certificate you can pay, but that course really opened my eyes as to all the possibilities in R.

If you're interested in projects, I suggest browsing Kaggle. They have datasets that you can play with as well as competitions which you can take part in.

The Johns* Hopkins University Data Science specialisation on Coursera is also a great source of learning and using statistics for R. It's fairly self-paced but does have weekly deadlines to help keep you on track.

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!

I had taken a few Coursera courses given by Johns Hopkins, which taught R. I don’t think it matters for Datanauts, though, as Datanauts has had several “water cooler chats” which are an intro to R.

Here’s the link to the Coursera series: https://www.coursera.org/specializations/jhu-data-science

Coursera charges $49 a month if you want a course certificate when you’re done. You may be able to audit the classes though.

People at all skills levels are welcome to apply to Datanauts so don’t feel you have to complete these to apply!

Again the same question :) best for what? What are you trying to do?

My answer may not fit you (have a PhD in CS, haven't taken MOOCs in a few years), but the ones I liked the best for data science where the Johns Hopkins courses (like https://www.coursera.org/specializations/jhu-data-science)

O teu caso é anormal (no bom sentido). Penso que queiras trabalhar só no alto nível da coisa (extrair dados previamente tratados com filtros, fazer previsões). Se for esse o caso, aprender python ou outra linguagem de raíz é contraproducente. O que desejas são conceitos de como e quando utilizares as bibliotecas de alto nível.

Dá uma vista de olhos nisto: https://www.coursera.org/specializations/jhu-data-science

Penso que depois será mais fácil orientares-te.

Infelizmente o nosso sistema de ensino superior não está preparado para casos como o teu (acontece também com malta das finanças e economia).

Guidance on how to set up the Development Environment for data science course.

Hello Redditors,

I am looking to do this course https://www.coursera.org/specializations/jhu-data-science.

I am thinking to setup an EC2 Windows instance with the all the specified tools.

Will I be charged for installing the tools such as RStudio, R, Git and other tools required to complete the course?

How can I minimize the cost of the operation considering am a broke college going student?

Are there any other strategies which you suggest I can use?

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.

New Job. Long Story. I need to learn some data science skills and fast.

Background: I have a bachelors in Math and minor in Physics

Situation: I have a job as a software engineer and "data scientist"

Issue: I want to be a real data scientist (what does that even mean?). I'm working on learning spark right now. I have to learn some machine learning, natural language processing, recap on some basic stats concepts and probably a lot more that I don't know about. My background is strong and my company is willing to expense me for an online course / set of courses.

What's the best place for someone like me to start? I'm looking at the data science toolbox (coursera) but I'm sure there are better programs. I would like to learn to use spark in the process because we are currently transitioning to it. Any advice would be of great help.

Thank you!

I highly recommend the JHU Data Science specialization on Coursera (disclaimer, I'm a PhD student at JHU). The instructors are some of the most respected statisticians/data scientists in the country, and there are 9 courses, so you can keep going until like you feel you've had enough

You can check wiki first. If you're unsure where to start, Coursera has data science specialization that's good to get something going.

Is taking the Data Science Specialization courses on Coursera an efficient way to learn data science or is there a better way? How do employers typically view this type of learning experience?

MS in Health Education and Promotion Want to crossover to research analytics, where to start?

I'm completing a masters in health education and promotion and will have CHES after completion. I'd like to work as a research analyst for the state or county and am curious if a certificate program like This would be better or more useful than This or if I should just consider a whole second masters or an MPH instead... Help Edit* formatting

my nephew (who just got his masters in data science) and i have been taking coursera courses in ml and ai (there are many in ml, ai and data science) for a few years now. Ng's courses are very good and they are hard. there are newer/easier courses you can take.

$50/month lets you take all of the courses in a specialization at the same time, so you can take this series: https://www.coursera.org/specializations/jhu-data-science

I believe this is one, I can get a more exhaustive list on Monday.

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

To me, it means that they are reimbursable by the company and it may help in future promotions.

Quick question for recruiters

I was wondering if learning in this John Hopkins University, Yelp, Swiftkey Coursera specialization would be enough to get a job or internship in the field?

I'm planning on learning more after, just need a job/internship to pay off more education. Thanks in advance!

https://www.coursera.org/specializations/jhu-data-science?utm_source=gg&utm_medium=sem&device=c&keyword=coursera%20data%20science&matchtype=e&device=c&network=g&devicemodel=&adpostion=1t1&hide_mobile_promo&gclid=Cj0KEQiA_MK0BRDQsf_bsZS-_OIBEiQADPf--pGcBqDTb9zfRwQywu6wd2hHjSbTWVXwVEyB0YBiw0UaAgC38P8HAQ

Taking a MOOC course with no experience (R). Which language should I pick up prior to starting the course?

Hi friends,

I put in a request for my employer for me to take a MOOC course: https://www.coursera.org/specializations/jhu-data-science

It's really basic stuff but they suggest having experience with a programming language. I've only worked with xhtml/css, never any programming. So in prep (~1 month), should I be putting my attention on C, C#, java, etc?

Which language would give me the best starting point before being introduced to R?

I looked at the FAQ and it suggests Julia, Python, R, or Matlab. I think though that I might be more efficient in starting something else that's more generalized, like C or C#. Or is Python more or less universal, giving me enough exposure to later pick up C/C#/R with a decent foundation?

Heya, this group of MOOCs is what got me all the knowledge I needed to get started with R and eventually land an analyst job. It builds from the very basics so you shouldn't have trouble following. And if you do get stuck with something, you can always come here or go to /r/rstats to get some help.

I'm midway through the Johns Hopkins Specialization through Coursera. If you don't care about the certificate, the videos are all on youtube and the books are all available for free on leanpub. It relies heavily on Swirl, an R package that has interactive tutorials (like datacamp or codecademy) but they run in R or RStudio.

Thanks for your feedback! I’m currently in the middle of the data science specialization in coursera https://www.coursera.org/specializations/jhu-data-science which is based in R.

Would you say this curriculum is adequate or should I supplement it with datacamp?

[Advice] Recent graduate with no relevant work experience looking for a certificate that would stand out on my resume.

I graduated in december with a BA in economics, I worked my way through college so i didn't take an internship. I started grad school going for a MA in Applied Economics in january, but I dropped out (I tried to do it online, at a prestigious university, and still work part time) and I've been having trouble finding a job.

Almost every relevant job posting I've looked at wants 1-2 or 2-3 years of relevant work experience, and the few entry level positions that I've found have no doubt been flooded with applicants. So I'm looking for something to help me stand out from the crowd, probably some sort of certificate in R or some other analysis tool. I've found This Coursera one from Johns Hopkins and i was wondering what you guys (espically if you're doing some hiring) would think if you got a resume with that on it. Would that help me stand out if I completed it and put it on my resume. Is there anything you would recommend i get instead of/ in addition to that certificate? And most importantly, would I be better off just trying for a masters again at my local state university? I'll probably end up going back for it eventually, should i just go for it now since I'm having no luck with the job search?

Thanks for any advice!

ok yeah, a lot of companies are caught up in legacy systems and have a big dependency on SAS. But it costs a little fortune annually so most companies are trying to move away from using it I think. At least in my experience.

Its probably worth checking out, and SAS offers a free students version from their website for download so that you can familiarise yourself with it. but I'm not aware of any good tutorials on working with SAS and its tough to get help on it from StackOverflow or anything like that. Its unfortunately one of the issues of proprietary software - finding help online is really tough and so learning it by yourself can be really difficult.

If you're looking to get into analytics, have a look at this course and see if its something you'd be interested in:

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

If you do the first 4 courses in that set (up to exploratory analysis), you should already be on a good start.

If you're looking to get into Business Intelligence (SQL, dashboarding, working with and maintaining data-pipelines), then the above link won't be super useful and you should probably spend more time learning SQL. I don't know any good resources for that offhand. My SQL is basically just knowing how joins work

Prior to picking up R, it's highly advised for you to learn Machine Learning (this is needed to understand how data analysis works) and Python, so you can learn the fundamentals of programming language.

Take a look at coursera's online course ( https://www.coursera.org/specializations/jhu-data-science ) so you can obtain the step by step guide, even if you don't want to enroll in it + you can specify what knowledge you need to search easily on google or yt. Since Data is the hot trend, it's easy to be overwhelmed and confused without guides.

Anything's possible if you have a strong will and want it imo, so ofc it's achievable. About the time you need before being able to list it as a skill tho, mm honestly it depends on you because it depends on how much hours you're willing to put in to learn and practice.

Take my advice with a grain of salt tho bcs my data-related classes in uni will only start after summer break. I learnt ML, AI, Python and C++ on my first year. This advice was based on my past research bcs I want to pursue the data analysis path as well.

Best of luck for both of us : )

From someone who's doing ML/Data Science heavy coursework in the M.Sci CS, I'd have to say that the premiere online Data Science course (https://www.coursera.org/specializations/jhu-data-science) is solid, but still leaves a lot of gaps in terms of both maths (LinAlg) & stats (Multivar & Bayesian Stats), and programming (use of Scikit/Tensorflow/Torch) required for the field.

The Data Science masters is still quite new and formative to have any established feedback. Looking at the handbook however, it does have a robust spread of theory and application.

There's 50 points of solid ML/Database/Distributed Systems programming, strong spread of stats subjects to get you up to speed, and some additional electives in programming/maths/crypography/geomatics/bioinformatics for whatever you fancy. If you have a CSP, and have a burning desire to be a data scientist, go for it.

If you are interested in self-learning Data Science, the online course linked above can give you the dot point synopsis of what to cover, and the remaining gaps in your knowledge can then be further supplemented in-depth.

Will the self-learning help you in pursuing your data science career? Note that DS is currently a buzz-word career in the midst of a boom, and you would have to compete against masters DS students, CompSci students, Statisticians and Actuaries looking to pivot into the field too. So yeah ¯\_(ツ)_/¯

Yeah, sure thing! Like i said it's a skill that is in very high demand. Do your research on this/other programs. Coursera also offers something similar.

I know these nano degrees can be effective for developers and digital marketing, but unsure how effective they are in data analytics to be honest (do some research!). Some degree of statistical knowledge is required and I haven't seen the syllabus enough to see if it's covered.

Glad it was useful for you!

STAT 382

I'm currently taking this course now and looking at internships that use R. My main question is how well this course prepares you for an internship using R and how well compared to say this Coursera course below?

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

I got a chance to take some of the Johns Hopkins Data Science classes on Coursera, and I was pretty impressed. The only thing I would say is you may need to spend some time improving your coding skills before hand. The cost is about $50 a month, but it's worth it if you're interested in exploring statistics and getting a basic understanding, as well as some foundational skills that you can use to build on top of later. I would recommend it because of the programming skills you gain alone, since statistics is no longer practiced without computers.

100% go for this: https://www.coursera.org/specializations/jhu-data-science

It opened worlds for me!

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

This one?

If you are truly bored, have begged people for things to do, and still have nothing, take online classes. I took this one, it was interesting and all the time spent in RStudio looked like I was actually working on something.

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

Every company I have worked for has had workload peaks and valleys. You will regret ever wishing you were busy, believe me.

[Advice needed] self-taught DS junior and too many ways to improve | paradox of choice

DISCLAIMER This would be rather long post, but I would really appreciate if you read it and offer any advice suggestions

Backstory

I'm currently unemployed data analyst/scientist which is lost in a flood of information in Internet and can't choose what to study next. I started as a technical worker in an IT company, but through the course of my work I started to develop tools for myself to give me better understanding of the feedback from our users and how our actions affect them. This was noticed by my boss and soon I started to work as data analyst. After some time I started to get tasks not only to analyze data, but also to try to apply ML algorithms and get "some results", if possible. I became quiet good at cleaning and processing data, learned myself a lot of python/pandas, so it was kind of a natural way to grow further in data science. I've been given tasks to read a paper and apply "this algo" on the data and measure the efficiency. While it was interesting, I was learning on-the-go and didn't have a chance to get a systematic knowledge in data science: one task done, here's another one. Other tasks were to try some different approaches about data: "let's not do regressions, let's do knowledge graphs", so I set the backend up and tested it with our data.

I ended up with the following skills(some are better than others):

• python, pandas

• data cleaning

• data visualization(matplotlib, d3.js)

• some algorithms(linear/logistic regressions, naive bayes, DBSCAN)

• some metrics(HVDM along with "standart" ones)

• running jobs on hadoop clusters

That being said, not everyone knows HVDM, but I do, but on the other hand I lack a lot of simple knowledge about number distributions and their properties etc etc. After I left my job I started applying to positions and going to interviews and realized how f*cked up I am: I do have some valueable knowledge and experience, but it's so chaotic...

I decided to improve. And here it comes...

Brain starts to f*ck me over

• "Hey, al, we need foundation first, so let's go over this set of articles on math to understand algorithms better"

• "You know, al, I've reconsidered, let's start with those awesome tutorials: we will implement algorithms in python and learn how they work"

• "Damn, maybe we should start this specialization on Coursera, looks good!"

• "Wait, I just realized smth, al! You don't know how to choose metrics! Let's find info on that!"

• "The interviewer mentioned bloom filters and some other sick data structures, learn this!"

• "Goddamit, al, you wanted to learn Clojure for so long, use those books"

• "NO! Let's start with some books, that overview the whole field of data science!"

• "Hm.... I read in some blog that they have tidyverse, maybe it's time to learn some R?"

• "F*ck math, let's practice: go to datacamp and dataquest, then head to Kaggle and own them all!"

• "ALARM! Everyone wants someone with experience with Spark, go learn Spark!"

Result? There is none.

In the end I became paralyzed by paradox of choice.

To sum it up

I still don't know which metrics are better to apply in which case. I don't know about which algorithms are preferrable given a task. Different distributions and their properties: is that even important? I'm not a jack of all trades, rather a king of none. And I'm lost. But I want to understand.

My questions

1. Where do I start? All those things I mentioned are important.

2. How to better combine theory and practice?

3. What path did you have? Will you recommend it?

4. What books/courses/whatever will you recommend?

5. What's the natural progression to grow as data scientist?

Thanks for reading this and offering your opinions!

You have plenty of time!

​

Talk to the professor(s) actually teaching intro to ML and intro to NLP and see if you can take some of those things as corequisites instead of prerequisites. You can also start right now doing some MOOCs. Coursera, for example, has some extremely accessible introductory courses that will greatly supplement the coursework you may or may not be able to do at your university.

​

Two years is an absolute ton of time to explore NLP and figure out if it's something you want to make a career out of. Don't worry about it being a time crunch.

I learned quite a bit from Coursera classes. Specifically the data science track from John's Hopkins. When I took it you could complete all of the material for free. I'm sure you can find a way to access the material for free since you're on a budget.

Start with this https://www.coursera.org/specializations/jhu-data-science. Retail need data scientists also. Don’t burn any bridges.

You could try with an online course such as Data Science on coursera. Its free, but you can pay for a certificate, if you want one.

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

You could bolster your education with this. I’m doing it now. Get professional certificates and everything you can put on your LinkedIn

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

Coursera.org Johns Hopkins data science would be a good place to start. You'll be comfortable with R by the end.

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

Coursera has a good series from Johns Hopkins. They'll try to get you to pay, but watch for the tiny-ass audit link at the bottom of the sign up. I believe that gets you access to all the lectures and project specs.

Acho que este delay é causado por diversas coisas:

• o pacote ggplot2 é naturalmente lento

• o servidor onde o app está rodando tem só 6GB de RAM

• o código não está paralelizado

E estes são os problemas só pra começar.

Eu sempre estive aberto para prestar consultorias para empresas, mas isto não é bem visto por algumas pessoas dentro da universidade pública, embora seja algo legal. De acordo com o regimento da minha universidade, eu poso prestar até 360 horas anuais de atividades remuneradas fora do meu emprego como professor de dedicação exclusiva. Mas como eu estava em estágio probatório até março, eu evitava entrar neste tipo de conflito por temer represálias.

BI é um nome bonito pra fazer gráficos e tabelas com dados das empresas. Um aluno meu defendeu uma monografia no semestre passado em que ele aplicou estatística para criar uma ferramenta de BI que era, basicamente, ler os dados de vendas e serviços de uma empresa, colocar isso num banco de dados e fazer aparecer gráficos de pizza e de barras via chamadas em SQL. Tinha pouca estatística envolvida, mas era um trabalho super bacana, com o qual ele já está divulgando aplicações da estatística nas empresas médias e granes daqui da região. Eu não diria que BI é uma especialização de estatística. É apenas uma aplicação dela no contexto empresarial.

Eu gosto dos cursos de Data Science da Coursera. Udemy e Data Science Academy não me agradam muito. Não acho eles ruins; apenas acho os da Coursera melhores, principalmente este da Johns Hopkins.

This is actually great! I started to learn about data science through this course on Coursera Data Science: Foundations using R

This is sponsored by my uni so I was like why not. It has been going great though I am not spending as much time as you but I feel I am learning more about this subject everyday :D

I'll definitely be checking out the sources you mentioned which I didn't know of. Thanks :)