Hot answers tagged

69

So you can integrate with the rest of the code base. It seems your company uses a mix of Java and python. What are you going to do if a little corner of the site needs machine learning; pass the data around with a database, or a cache, drop to R, and so on? Why not just do it all in the same language? It's faster, cleaner, and easier to maintain. Know any ...


46

Drew Conway published the Data Science Venn Diagram, with which I heartily agree: On the one hand, you should really read his post. On the other hand, I can offer my own experience: my subject matter expertise (which I like better as a term than "Substantive Expertise", because you should really also have "Substantive Expertise" in math/...


33

Most non-technical people often use Excel as a database replacement. I think that's wrong but tolerable. However, someone who is supposedly experienced in data analysis simply can not use Excel as his main tool (excluding the obvious task of looking at the data for the first time). That's because Excel was never intended for that kind of analysis and as a ...


30

1) I think that there's no need to question whether your background is adequate for a career in data science. CS degree IMHO is more than enough for data scientist from software engineering point of view. Having said that, theoretical knowledge is not very helpful without matching practical experience, so I would definitely try to enrich my experience ...


26

There may be a lot of reasons like: Workforce flexibility: One Java / Python programmers can be moved to other tasks or projects easily. Candidates availability: there are plenty of Java / Python programmers. You do not want to introduce a new programming language to later find out that there are no qualified workers or they are just too expensive. ...


18

Focus less on gaining skills and more on gaining experience. Try to actually solve some problems and post your work on github. You'll learn more in the process and be able to demonstrate knowledge and experience to employers, which is much more valuable than having a supposedly deep understanding of a topic or theory. Data Science is a pretty loaded field ...


17

Do experienced data scientists use Excel? I've seen some experienced data scientists, who use Excel - either due to their preference, or due to their workplace's business and IT environment specifics (for example, many financial institutions use Excel as their major tool, at least, for modeling). However, I think that most experienced data scientists ...


14

I think most people are answering without having a good knowledge of excel. Excel (since 2010) has an in memory columnar [multi table] database , called power pivot (which allows input from csv/databases etc), allowing it to store millions of rows (it doesn't have to be loaded on a spreadsheet). It also has an ETL tool called power query allowing you to read ...


14

It is in general true that for purely data science and statistics exercises R offers the best and fastest (especially if using the data.table package) tools and methods, that otherwise would be heavier to implement in Python (I assume by Python we all mean Pandas, though). Most data scientists do in fact use R to perform their models and calculations, or ...


14

So you're still on the Basics and William's answer is pretty good, I will list here a bit of stuff to learn, and where to. 1 - You need the basics, that is already much more than you expected it to be: Linear Algebra: knowing the best way of inverting a matrix might be useful for a computer scientist, but you're not aiming for that. You need to understand ...


9

From the job ads I have seen, the answer depends: There are jobs which are more technical in nature (designing big data projects, doing some analysis) or the exact opposite (doing analysis, storage etc. is someone elses job). So I would say that SOME software design skills are extremely useful , but you don't need the abillity to build a huge program in C# /...


9

Well it depends on what kind of "Data Science" you wish to get in to. For basic analytics and reporting statistics will certainly help, but for Machine Learning and Artificial Intelligence then you'll want a few more skills Probability theory - you must have a solid background in pure probability so that you can decompose any problem, whether seen before ...


9

Business intelligence is perfect for you; you already have the business background. If you want to become a bona fide data scientist brush up on your computer science, linear algebra, and statistics. I consider these the bare essentials. I don't know about Scandinavia, but in the U.S., data science covers a broad spectrum of tasks ranging from full-time ...


8

In his book Data Smart, John Foreman solves common data science problems (clustering, naive bayes, ensemble methods,...) using Excel. Indeed it's always good to have some knowledge of Python or R but I guess Excel can still get most of the job done !


7

I'm surprised how many people are attached to the coolness of the profession rather than the actual job to be done. Excel is excellent tool, with free Powerpivot, Powerquery, it can do so much. (these are not available on OS X). And if you know VBA, you can do some nice stuff. And then if you add on the top of that knowledge of python you can combine the ...


7

I do like Berkeley course on Data Science, will give a good foundation and taste for Data Science, After moved to udacity and coursera and many more resources. So if you have Programming skills than will need math and stat and a lot of visualization. Also will be great to get used to IPython because is essential to see every step(visualize)how it perform ...


7

I've seen quite a few companies using the title Data Scientist for "Data Engineer" type roles. Particularly in the big data space. If the company is using Hadoop or a distributed framework like Spark to do it's analytics in then Java or Python (or probably Scala) would be the languages that would make the most sense .


6

I see at least five ways to approach this problem of finding a data scientist position/work specifically at non-profit, non-governmental or similar organizations, as I describe below. I hope that this is helpful. First, and the most obvious, way is to search major job portals, such as indeed.com, dice.com, monster.com, CareerBuilder, Glassdoor and others, ...


6

Absolutely. Keep your software skills sharp. You can do this in an academic program if you simply implement by yourself all the algorithms you learn about. Good selection of courses, btw. Consider getting an internship too.


5

First of all I should say you question probably is an off-topic and will be closed soon. Discussed at this SE site Anyway I can target you to similar questions discussed at this SE site already: Statistics + Computer Science = Data Science? Starting my career as Data Scientist, is Software Engineering experience required? Cross Validated SE A set of ...


5

Disagree with David, a true data scientist is an applied statistician who codes and knows how to use machine learning algorithms for the right reasons. Statistics is the base of all data science. It is the "cake" per se. Everything else is just icing. The question is what kind of data scientist do you want to be? Do you want to be a master of the subject (...


4

Let me first clarify that I am starting my journey into data science from a programmer and database developer standpoint. I am not a 10-year data science expert nor a statistical god. However, I do work data scientist and large datasets for a company that works with rather large clients worldwide. From my experience, data scientist use whatever tools they ...


4

Excel allows only very small data and doesn't have anything that is sufficiently useful and flexible for machine learning or even just plotting. All I would do in Excel, is stare at a subset of the data for a first glance over the values to make sure I don't miss anything visible by eye. So, if his favourite tool is Excel, this might suggest he rarely deals ...


4

Due to high demand, it is possible to start a career in data science without a formal degree. My experience is that having a degree is often a 'requirement' in job descriptions, but if the employer is desperate enough, then that won't matter. In general, it's harder to get into large corporations with formalized job application processes than smaller ...


4

Why not do an MSc in ooh... Data Science? I wrote a quick review of UK Data Science Masters' offerings recently. That should help you get an idea what is offered. Mostly they are mashups of stats and computing, but there are specialisms (health, finance for example) that might interest you. Note that list was compiled for courses that have already started, ...


4

Data science is a multi discipline subject. In addition to math you need some programming skills and ideally - understanding of the domain where you are going to solve problems. You can start from online-courses and some introduction literature. Look at the answers at current site: Data Science Sertifications Books about Data Science Data Science vs Data ...


4

Both MCMC methods and network analysis play an important role in data science. I think you should go for the project you like more. However, in my experience, community detection in networks is a niche (applied math/graph theory) of network analysis, while MCMC methods involve lots of statistical and computational concepts. Personally, being a statistician, ...


4

If you want to be a practical man with true knowledge, start with math(calculus, probability + stat, lelinear algebra). On every step try to implement everything with programing, python is nice for this. When u get good ground, play with real data and solve for problems Courses. Linear algebra - edx Laff or coding the matrix Stat - edx stat 2x Barkley ...


4

Machine Learning is a really big field. Depending on what exactly you want to do, there might be huge differences. Having said that, the following skills are helpful: Programming, especially Python / C++ Frameworks like TensorFlow, sklearn, Torch, ... Algorithms like neural networks (especially gradient descent), SVM, decision trees, clustering algorithms, ...


Only top voted, non community-wiki answers of a minimum length are eligible