Back to the basics

Photo Alban Gonzalez @ Flickr

As I have written before (There are no shortcuts) sometimes is good to return to the basics and take it as reflection and improvement of your knowledge. All the programming languages and tools have many options how it could be extended and used together with libraries, packages, and plugins. But do you know how it works inside, what's under the hood? How to setup properly the parameters to get the best results from such tools?

What I want to do today is continue with this idea, but not on the philosophical level as last time, but with concrete examples. I give you some hints which I have tried by myself.

Machine learning

What could you do with machine learning? Whenever you install Python or R language you get such libraries like NLTK or Scikit-learn for Python or e1071. But do you see under the hood? Do you know how it's implemented inside or it's just a black-box for you? Do you want to know? So, what you can do:

• Start with the implementation of the simplest machine learning methods by yourself. For instance, k-Nearest Neighborhoods is the really good example and you can do it in half an hour from scratch or Decision Tree method or even Random Forest method. 
• Take a look how it's implemented in existing code, for example, you can take a look into NLTK Python code and learn how they invent particular process

Natural Language Processing (NLP)

The machine learning methods used for NLP are the similar one which you know already from the previous study of basics (kNN, Decision Tree, Random Forest) or they are quite extensive to implement them from scratch. The same it is with n-grams and textual document models, so what to do then:

• You will not most likely implement word2vec, but what you can implement simply is bag-of-words or with little bit more effort is possible to implement tf-idf.
• This code is also accessible in NLTK library where you can take a look and for instance, find that tf-idf is implemented with smoothing.
• With n-grams when you implement it (see my post Computing text conditional entropy with uni- and bi-grams in R and Python) you can play further and calculate pointwise mutual information and find the collocations and associations between words with that.

I will return to this topic whenever I have more experience to share, but if you will search you will definitely find similar sources about other topics of your interests such as Big Data, Visualization, Statistics, etc.

There are no shortcuts

During last 3 months I have realized it is really difficult to progress with something when you don't have enough knowledge and you are trying to go ahead without proper basics and good background, because you don't have time to focus on them, rather jump directly to wild water and swim. What a mistake.

I was dealing with many things simultaneously. I needed to start with second publication for my PhD study, but I worked on second experiment which needed to be included into this publication. And of course I studied two difficult and time consuming courses in the same time on Coursera and elsewhere. All of this together with big workload in work and push from my company to learn German. To much to process, to much work to do.

Problem is, nobody can control dreams you have.
There is a reason why Magical Realism has been born in Columbia.
It's a country wheres dreams and reality are conflated.
Where in the head, people fly high as Icarus.
But even Magical Realism has it's limits
and when you get to close to the sun...
your dreams may melt away.

- Narcos

I haven't fly so high as Icarus yet. I haven't fall down and let my dreams melt away yet. But I have been close. My PhD was running away from me. I haven't finish courses.

So, there are no shortcuts. Basic need was to organize and finish all of those tasks one by one with proper priority and timing. This helped me out of the bad mantra I am too busy for basics, but I cannot manage the complex things because of lack of basics.

And I returned to basics (learn particular technique or setup, install and explore new technology) at least for once a week instead of work on my PhD or watching lot a videos from MOOC courses I am focusing on small parts with hands on experience.

Every Data Scientist must only pay taxes and die, rest is just optional...

Picture from cacm.acm.org

I am just wondering. There are many articles about what every Data Scientist MUST know and do to be real Data Scientist. There are many articles about MUST not do as Data Scientist. What real Data Scientist MUST read and so on and so on. And honestly I don't care. Why is that so?

Research

Let's take the last one must: "What Data Scientist must read" list. I just briefly took several results from Google, here is the list of 10 of them:

• https://www.quora.com/What-is-a-list-of-books-that-every-data-scientist-should-read
• http://bigdata-madesimple.com/20-must-read-books-for-budding-data-scientists
• http://www.analyticsvidhya.com/blog/2015/06/infographic-read-books-analytics-data-science
• http://www.datasciencecentral.com/profiles/blogs/15-books-every-data-scientist-should-read
• http://www.mastersindatascience.org/blog/7-must-read-books-for-the-budding-data-scientist
• http://www.forbes.com/sites/gilpress/2015/03/31/3-recent-books-for-budding-data-scientists-and-business-executives-involved-with-big-data-analytics
• https://www.umbel.com/blog/big-data/must-read-big-data-books
• http://analyticstraining.com/2014/five-must-reads-for-data-scientists
• https://www.quora.com/What-are-the-must-reads-for-data-science-and-machine-learning
• http://www.analyticsvidhya.com/blog/2015/06/infographic-read-books-analytics-data-science

And what is this quick research good for? From this circa 80 books and list of several articles you get list of subjective chosen resources which lead practically to nowhere. Just several books repeating like famous Nate Silvers Signal and Noise and of course some R or other Cookbooks. So, what is conclusion?

Conclusion

This lists of books which someone else read leads me always to Vincent Granville's article Fake data science. And what you need to take from it? Pick any book you need for your field of expertise. And what should be your field of expertise? Choose some project, doesn't matter if your personal one, school or for instance from Kaggle.com. And follow up approaches which you need to for goal achievement, then pick book, course to support your path towards this goal. And by real work, life experience you will sooner or later become Data Scientist.

Big Data and Data Science study - presentation

During our Friday 26th of September company meeting I talked about topic in title with presentation. I think this presentation could be useful not only for audience which was there but for anyone.

Here is description what is content of presentation about:
Big Data and Data Science study with subtitle "study materials and online courses" is little bit more over 40 slides presentation about 10 domains of Data Science covered by online free and paid MOOC courses, study materials and free books.

Based on my almost year of study, investigate and collect of materials, tutorials, courses, books, links, etc. I have prepared distillation of the best in this short presentation.

Of course list is not full, because there is always something new, undiscovered and better than before. But it contains the most important information for those who want to start or don't know where exactly follow up when they already begun.

Follow up to the Speaker Deck site and you can download presentation as PDF which is quite useful when you consider functional links over all presentation.

Data Science and PhD study

When I was applying for the PhD study I was thinking about it as opportunity to have chance to use all techniques, programming languages, methods, processes and technologies in practical way and delivery something reasonable which supports my professional growth and development. And it could stands as proof that I am really keen to learn and improve in this field and I have passion to do Data Science.

I was thinking about it again when I have read this article The Modern Data Nerd Isn’t as Nerdy as You Think on Wired about Data Nerds who are currently in charge of Data Science departments or in similar role and apply their experiences and knowledge and usually they do not have PhD. More over they do not have master degree, just bachelor degree. Or in case they have any kind of degree it is not from field close to Data Science.

Interviews to Data Scientist role

After almost year of self study of Data Science courses via Coursera, DSE program and another MOOC pages I had chance to go to Data Scientist role interviews. So far two.

First one was a couple weeks ago I was in interview to the Researcher / Data Scientist position. Mainly about prediction models, math, statistics and data mining/machine learning. Mostly in R connected with Hadoop and some other mathematical tools. This was the first for such position.

Second one was a couple days ago and it was interview to the purely Data Scientist position. Including all data science work from many different sources with project driven or data driven approach, it depends. This was most recent experience and I hope not a last one.

My study list for Summer time 2014

I was thinking what to study during Summer time. I need to improve my insight into Big Data, especially Hadoop and its fundamentals like HDFS, HBase and MapReduce. At least from non-Java developer point of view. But I would like to start with Java development (or another programming language) perspective too, some intro would be good.

I have also in my study plans two courses about Machine Learning and Data Science on Coursera which I already subscribed to. When I am currently taking a break from courses in Data Science Specialization.

And last but not least I need to finish mini project in R for DSE 501 and follow up with next one DSE 502.

If anyone wants to join me, for Coursera courses for example. Sign in, links to the courses or another staff are below and let me know in comments. We can make a study group.

Course: DSE 501 - Machine Learning with R

After I went successfully through DSE 400, which is in detail described here. I followed up with DSE 501 Machine Learning with R. I was really eager to learn Machine Learning since beginning of my Data Science study, so here was an opportunity and I took it.

First at all I need to say, I really enjoyed it, even though I haven't finished it yet. Last thing which is still on my table even though I am working on it really hard is final project, the 6th assignment. You are choosing it by your self and define proposal by your self, so it's up to you, how difficult it would be. But, I am going backwards. Let's start from beginning.

Course: DSE 400 - Fast Track to Data Science

After I described the whole DSE Program it self here. I would like to continue with specific parts which I came through. Currently I study 2nd course DSE 501 - Machine Learning with R which I will describe in some next blog posts, but let's start first with initial course which was DSE 400 - Fast Track to Data Science.

As I wrote already in my previous blog post Data Science Enablement Program is really different than other known courses which you know from Coursera or other MOOC websites. Instead looking at video courses and doing quizzes and course project your need to be personally involved and discuss weekly topics and also do (at least) weekly assignments which corresponds to current week topic. Each week you got a lot of study materials which could help you to achieve assignment and get reasonable knowledge about the topic and also lot of following practice suggestions with reasonable questions or tasks which move you forward through the topic.

Gartner's Magic Quadrants as a source for what technologies to study

As a Data Scientists you need to learn a lot. Even if you know a lot you need to learn more. New things and don't stop learning, because you wanna be top. And people on top are at least know about trends if not following them and create them (that's the best choice :-)).

So, the idea is simple, follow up a Gartner's Magic Quadrants which shows all necessary players from specific field and look what is necessary to know, what should be fine and what is optional or even could be omitted. Also we should not forget to see who is novice player and who has been rejected.

Update 4/22/2014: Included pictures of all Gartner's Magic Quadrants mentioned in this post.

Course: Data Scientist Enablement Program (DSE)

Data Scientist Enablement Program (DSE) is something which I have found by accident. At the beginning of year I was looking for new courses or some complex study with topics about Data Science and Big Data and have been really keen to learn something new. Then I found form which gave me access to SONO to DSE.

This program and its all 4 modules (so far I have done 1st one, so I am just assuming based on the current experience) are not classic course with video tutorial, quizzes and labs with question form and score. It is almost like in school, you can participate or you can not, you can read and watch materials which you got, but nobody checks if you have done it or not. Only one thing you need to do, is submission of assignment. And even this action is not time limited, so there is no penalty for late submissions, only recommendation to do it ASAP.

Moreover, you will get certificate corresponding to your maturity level in this study and based on your composite score which is computed based on your social engagement, activities, projects, collaboration, etc in all 4 modules.

Study: Apply to Berkeley School of Information MIDS Program

It was little bit easygoing decision. When I saw offer to apply to Berkeley School of Information Master of Information and Data Science (MIDS) Program I tried it. It is completely online study. And here is my experience with how it went. In front of this post I need to say I haven't finish it and I won't do it. The main reason is: for me it is impossible to finance it. Before you start, please have a look over financial part too. You can find tuition-fee described in detail in separate article. I haven't done that, I expect it is expensive, but not so much, of course it is Berkeley :-), so I have been surprised in the middle of application.

Reading: Free online resources January 2014

This is just a simple list of sources which I have found on different places and are available free and online:

• An Introduction to Data Science by Jeffrey Stanton
• Introduction to Probability and Statistics Using R by G. Jay Kerns
• R for Machine Learning by Allison Chang
• Think Stats: Probability and Statistics for Programmers by Allen B. Downey
• Data Analysis with Open Source Tools by Philipp K. Janert

I will post new list, when I have something new, if possible next month.

What, where, why and how to study Data Science

I just jumped into the water. Just like that. I haven't so much thought about that. I knew some basics about Big Data, but about Data Science not. It changed when I passed through one commercial enterprise Big Data solution introduction. It also contains Data Science role description and its contribution to development process.

Next step was reading many web discussion about it. And then finally read really good article Fake Data Science from Vincent Granville published in February 2013. It formed my opinion and it turned my way little bit different way than I was at first time. Simply said from more technical point of view to more business point of view. And it make sense if you would start to look on Data Science from right angle. What is result or what result do you expect (what's are business needs) and what path do you need to use to accomplish it?