12 Estadísticas que exhiben el Rol del Análisis de Datos en la Transformación Digital

Ultimamente mucho se me pregunta sobre el Rol que cumple #AnalisisDeDatos en la #TransformacionDigital. Atento a las cifras. La fuente de los datos la menciono en la misma presentación.

12 Estadísticas que exhiben el Rol del Análisis de Datos en la Transformación Digital

La guerra detrás de la transformación empresarial

Cuando uno va a comenzar una transformación empresarial, sea digital, ágil, o lo que sea, uno se entusiasma, siente que va a ser parte de algo grande, que tendrá un beneficio para todos. Puede que así sea (el tiempo lo dirá), lo que si es cierto es que habrán quienes reciban el cambio con los brazos abiertos y lo apoyen, pero déjame decirte, serán pocos. Identifícalos, aprécialos, cuídalos, apóyate en ellos, reconócelos.


La mayoría sera indiferente, harán lo q les pidan o aparentaran hacerlo al menos. “Otra moda más que acabara en nada” será el rumor en los pasillos antes de que siquiera comiences.


Habrán unos cuantos fuertes detractores, algunos lo dirán frontalmente, otros no. Es que esta transformación despertara una guerra política interna, en la que (desees o no) estarás inmerso. Los “no tan amigos” del sponsor del cambio estarán sutil pero irrevocablemente en contra.


Otros detractores naturales serán los adictos al protagonismo, porque estas personas se sentirán amenazadas.


Por otro lado, habrá quienes quieran “aprovechar la ola” y conseguir beneficios personales involucrándose en esta iniciativa. No hay nada de malo en querer algo para uno, lo malo está en que sea el único motor para apoyar algo, mas allá de un autentico deseo de ayudar.


Ah, me olvidaba, y hay los cuida-imagen, por llamarlos de alguna manera, personas que no apoyarán la transformación no porque no crean en los objetivos, sino simplemente por cuidar su imagen. La transformación podría terminar mal, y eso podría “salpicar su reputación”. Esto pasa mas que nada a personas que prefieren mantenerse al margen de lo que no se les ha sido asignado directamente. Lo importante para ellos es que no se los vaya a relacionar con posibles fracasos.


Estás prevenido. La transformación empresarial es como una rosa, tiene sus recompensas, pero tiene sus espinas también. Habemos personas a quienes nos apasiona dedicarnos a esto, pero al principio toca aprender ciertos detalles a la mala, golpeándose, como en todo en la vida. Suerte en tu transformación y disfruta el viaje!

Nota: Este artículo fue inspirado en el siguiente párrafo del libro Driving Digital de Isaac Sacolick (ver texto resaltado).


La guerra detrás de la transformación empresarial

Survival Guide for Student+Worker with children

For people who don’t know me so well, I’m a full-time worker, currently a graduate student at a Top 10 US Computer Science program from Georgia Tech, a husband and a father of a 23 months old baby girl and more recently a father again of a 7 weeks old baby.

It’s been stressful, let me start with that. Just 2 years ago, I didn’t think I could start a new education challenge, I felt overwhelmed with my job and spending time with my wife and maybe fixing something around the house. I obviously didn’t know the meaning of the word “overwhelmed” and didn’t know what was coming.

I wanted to start grad school right away, but then me and my wife found out we were expecting our first child. We loved the news and were full of joy, something we had longed for a long time and I wanted to share the experience and the responsibilities with her. Then my first child was born, so I decided to wait 1 more semester to apply to grad school.

To make the long story short, this has been the single toughest most stressful time of my life and by far the happiest too. My day starts very early and it doesn’t finish when my baby girls fall sleep, because I go to study for a few hours after that.

Things I’ve found helpful:

1. Build a strong help network around you

Nothing will save you from what is about to come, but it can surely make the road a little less bumpy. Think about people closest to you and some other people services you can afford to pay for. Your parents, your in-laws, daycare or getting a nanny would surely make a difference. I’ve even heard about foundations and communities helping with children care.

Just as a side note, you can find me through my Linkedin profile and ask me any question related to OMSCS, OMSA or online education in general. Just send me an invitation and I’ll be glad to share my network with you.

2. Make a schedule with your spouse

Yeah, no surprise, but even if you don’t get to use it or don’t fully respect it, having a schedule will make you feel more empowered. More in charge, more organized. Doing a schedule on your own (without spouse) will only bring trouble to an already stressful situation. If possible get your schedule signed by you and your spouse, believe me this can be a relationship saver for some people. Put it in a visible place, like on the fridge. You can even use a digital calendar like Google Calendar to share your schedule with your significant other. Yes, it does support sharing calendars, and if you’re not ready for that kind of sharing, you can only mark the events you both need to be aware of and share those through an email invitation, you can make freely available tech work for you.

You may need to make changes to your schedule on the long run, but starting without one is not sane.

3. Make room for your studying

In my case, my program is very rigorous. You be the judge, it can take from 20 to 40 hours of studying per week depending on number of courses taken per semester. I’ve seen people trying to start with a minimum time availability in their hands and then struggle their way to only become frustrated by reality. Some courses are more difficult than others, and difficulty may also vary depending on your background.

I could have settled for a less rigorous local program, with a more flexible time frame, but it’s really up to everyone to decide what they like and what they want for themselves. In the end, people are known to do way better what they really like to do.

4. Embrace change

I don’t want to sound like an agilist right now (which I am, but that’s another story). But there will be many moments when you’ll see things are not going according to plan. It’s easy to feel frustrated when you start your day thinking you’ll do your piece of studying and then when you’re back in your bed at night remembering all things that got you so tired during the day, you remember none of them were the things you wanted to do, or the things you were supposed to. Don’t think about it too much, suck it up, and go studying even if it’s midnight. If you are too tired and your body doesn’t respond anymore, it’s no biggie. Grab some sleep. And wake up as early as you can, the next day will bring a new chance to finish what is left to do. You can do this.

5. Print your course syllabus and schedule and post them in a cork board

Every class has its singularities and its due dates. Don’t run into last minute panic. Print every course schedule and syllabus and post them in a visible place, I use a cork board I look at regularly.

A course schedule may vary in time, but usually they are very sutle changes.

6. Don’t hesitate to ask for assignment due date extension if you run into some proven emergency

I’ve had a couple of those, and instructors have been very understanding about the subject really. They just asked me to send them some document proof of what I’m saying. Usually that’s a document signed by a physician.

7. Celebrate every milestone

Celebrate every end of semester and every special/important achievement. It will enhance your position towards new challenges coming ahead.

Send “thank you notes”. I send a Whatsapp message to my “help network” every end of semester indicating the name of the courses I’ve finished and the grades I got thanks to them. I keep reminding them the importance of their effort in this goal they’re being part of.

8. Enjoy life

Don’t make yourself a stranger to the eyes of your close ones. Have special attention to your kids and spouse, your time is very important to them, even if they don’t mention it. My oldest daughter behavior with me changes a lot based on the quantity and most importantly the quality of time I spend with her.

Time with your loved ones is the most valueable asset, not money, not titles, not academic credentials, not job, not professional recognition.

When I look back through time I only see people faces. It’s people who I feel I should care about the most. In my latin culture family is most important thing, and now I have my own family I understand why and I appreciate everytime from a different perspective, which makes my recognition to other people’s work of love even greater.

9. Build habits in your kids

dad and kid brushing teeth.jpg.838x0_q67_crop-smart

A consistent early bed time will make room for your studying and sharing as a couple. A quite time every day is priceless if used consciously.

10. Tools to learn faster

Have a look at gadgets I’ve learned to be a real boost for online studying. Real-life-tested gadgets that may be handy for you.

Well, there you go, I hope I helped someone out there. Thank you for reading and have a great time with your new challenges to come!

Survival Guide for Student+Worker with children

What was it like to take Computing for Data Analysis (CSE 6040) course in Fall 2017?


Computing for Data Analysis is a data science introductory course from Georgia Tech at edX, part of the MicroMaster in Analytics worth 9 credits accountable for the Online Master of Science in Analytics from the same university.

This course teaches you Python from the very beginning, in fact it was not a difficult course, but don’t be fooled by that. It was work intensive indeed. Let me share with you a progress graph that shows the quantity of assignments I had to do to finish the course.


If you are an edX student like me, you can see this graph clicking in the “Progress” tab of the courses you’ve taken. As you can see, there were 15 notebooks this Fall 2017, 16 if you count the Midterm 1-R. There were 2 Midterms and 1 Final Exam.

This is how grade was divided:


As shown, there were so many notebooks, but they only accounted for 50% of the grade, 3 exams were 50% of the grade.

Just as a side note, you can find me through my Linkedin profile and ask me any question related to OMSCS, OMSA or online education in general. Just send me an invitation and I’ll be glad to share my network with you.

I liked this course, because it taught me things that I think are very basic every day usage concepts that I hadn’t had the chance to polish on. I’ll share with you the list of topics:

  1. Python Essentials (there were some things I didn’t know of, like functional programming)
  2. Pairwise association mining
  3. Math prerequisites review (a lot of linear algebra and matrices stuff)
  4. Representing numbers
  5. Preprocessing unstructured text (always so necessary)
  6. Mining the web (useful indeed)
  7. Tidying data
  8. Visualizing data and results
  9. Relational data
  10. Intro to Numpy/Scipy for numerical computation (I had already gone through this, but a little brushing up wasn’t bad)
  11. Ranking relational objects
  12. Linear Regression (a little Machine Learning to make things more interesting)
  13. Classification (more ML)
  14. Clustering via k-means (still more ML)
  15. Compression via PCA (ML, ML, ML)
  16. Eigenfaces (personally I hate this topic, but very recurrent in Analytics courses indeed)

You can take a look at my course certificate if you want. That’s how it would look yours if you take the course or any other edX course.


  1. I liked the course, it touched very foundational topics and my favorite language after php and javascript: python, what an important and fun language to learn.
  2. I liked how we used Jupyter notebooks, I hadn’t had the chance to work with them. Vocareum is a web jupyter notebook server I didn’t know, a real jewel.
  3. I liked the level of automation in this course, almost fully automated. We could instantly know what our grades were after every response we gave, all thanks to Vocareum.


  1. From my point of view, there was relatively short time to address midterm 1, which was in Vocareum, there was a lot of reading to do to understand what to do on each question. I have nothing against to coding as part of an exam, even though it was the first time for me in this way of doing things, but time was an issue, at least for me.
  2. Some topics like “Representing numbers” were a little boring to me.
  3. I took this course as a verified student, there were other 2 types of students: audit students and Georgia Tech students that took the course as part of the Master of Science in Analytics. GT and verified student forums were put in Piazza (a very handy forum software used by other famous courses) in 2 different groups, with majority of students coming from GT, so verified students were kind of isolated from the majority of the perspectives on the course topics, which made learning not as fun as other online courses I’ve taken.

In sum, this course took a lot of time from my schedule in the 15 weeks it lasted, from 5 to 10 hours per week maybe. Not as much as other courses, but you already saw the number of assignments that were allocated. If I had the chance to take it again, I would do it, it was fun and it strengthened basic foundations for me as a data analyst. I felt I had to tune my python knowledge a bit, and this course did just that. Hope you like it as much as I did if you are about to take it. Best of luck! Don’t forget to connect with me in LinkedIn, visit my public profile. I’ll be glad to share my network with you. See ya.

What was it like to take Computing for Data Analysis (CSE 6040) course in Fall 2017?

What was it like to take Data & Visual Analytics course (CSE 6242) in Fall 2017?


I wasn’t sure to take Data & Visual Analytics course this Fall, as there had been some negative comments in the OMSCS Google+ Group for Spring 2017, but everything went smoothly. You can take a peek to the DVA course website. There were 3 homeworks and 2 projects and just 1 final exam as you can see below. There were other 5 assignments called Activities that were worth 1% each, and they were completely optional as you can see if you sum everything up. Activities were not 10 times easier than homeworks (worth 10% each) let me tell you haha.


The exam was open notes/internet, it was still proctored using ProctorTrack as usual, but just because they really wanted to make sure that you didn’t get someone helping you out in person or through the internet (something like a forum or slack request for help) during the exam.

Letter grading rules were nothing special really, you got an A for 90 or above and a B for 80 or above.


I did 4 out of the 5 activities, that assured me 4 extra points, which I though may be useful as I remember someone telling me the final exam worth 30% could be a massacre if there was no curve applied, which is just what happened in previous semesters. I didn’t want to be so exposed to a B or C grade chance, as there was a different course teacher (Dr Joyner), and we didn’t know much about what to expect specially on final exam.

Just as a side note, you can find me through my Linkedin profile and ask me any question related to OMSCS, OMSA or online education in general. Just send me an invitation and I’ll be glad to share my network with you.

I spent hours on each “1% activity”, and I remember that after I finished each of them I got like “wow and this was only for a 1% of the grade”. But in the end I think it was worth it, I got an A as I wanted.

I think what made things a little easier is that I had already taken Machine Learning (CS 7641) and Machine Learning for Trading (CS 7646) courses before DVA, which helped me a lot, specially for the final part of the course where we saw Logistic and Linear Regression. Additionally, something I think helped me was that I was taking Computing for Data Analysis (CSE 6040), another Georgia Tech course at edX at the same time, which covered maybe a 30% of the material, but from a different perspective (using Python instead of R and from a computational angle).

Another observation would be that really all Data Analytics courses I’ve taken so far have touched Machine Learning concepts in some way, most of them include it by the end of the course material, and it really makes things more interesting and a little more difficult as well.

Something particularly stressful that happened to me while taking DVA was having 50% of the grade on the table by the time I took the final exam, but everything turned out just fine.

In the end, DVA wasn’t as tough as I expected, coming to a very unknown environment and with some hard comments on Spring 2017 course roll out. My expectations were low, but in the end I got a course that wasn’t as difficult or problematic as I thought and I had a lot of fun with data visualization and I got to practice R a little more, as I had already had an R course in Summer, which I recommend to you (GT’s ISYE 6501x Introduction to Analytics Modeling at edX).

PS: If I had the chance to take courses in a different order, I would’ve taken DVA and ML4T courses first, and ML afterwards, as ML was by far a tougher course. Just to make a comparison if you’ve already taken ML4T, I consider ML4T to be more work intensive than DVA, but not necessarily more difficult.

Well, guys this is all, have fun! Good luck picking your courses for next semester. Hopefully I’ll be taking 3 courses next Spring 2018: Graduate Algorithms, Big Data for Health Informatics and Human Computer Interaction. I’m a little frightened by the difficulty of GA and BD4H and taking them at the same time but I have no other option, wish me luck. See ya.

What was it like to take Data & Visual Analytics course (CSE 6242) in Fall 2017?

Google Colaboratory, a new tool to spread Machine Learning to the world

Google Colaboratory, a new tool, something like Github for Jupyter notebooks. It kind of looks like Vocareum.com to me. You can use TensorFlow natively, neat! It only supports Python 2.7 and Google Chrome desktop at the moment, but it sounds promising. The jupyter notebook files are saved in Google Drive. More information about it you can find at its FAQ: https://research.google.com/colaboratory/faq.html

Google Colaboratory, a new tool to spread Machine Learning to the world