What I got during five months of training on the program Data Analyst Nanodegree from Udacity

Hello! I saw articles on Udacity curriculum on Habré . I have completed one of these programs and would like to share my experience.

I have been engaged in distance learning, or rather, for the last six years I have been accompanying a corporate training portal and developing modules for it in a fairly large company. Sam periodically studied at different courses from Coursera, edX, Udacity.

About a year ago, Udacity launched a new kind of program - Nanodegree. I want to share my learning experience on one of them. At that time, the choice was between Front End and Data Analyst. I chose the second. The topic is new, interesting and rather complicated. In addition, recently I used many things related to data processing at my workplace. Well, after such a long period of work with the same product, there is a desire to develop and try yourself in a new role.

The program contains 5 modules (now 7). The essence of all training is to learn the basic tools necessary to start a Data Scientist career. To obtain a certificate, it is necessary to create 5 projects that require knowledge of statistics, data preparation and processing (Data Wrangling), Exploratory Data Analysis, machine learning and data visualization. If there is enough knowledge, you can immediately start projects, if not, you need to take a course for each of them.

I signed up for the newsletter and as soon as in November 2014 the registration for the program opened - I signed up for the first cohort of students. Since this was the first group, they trained a bit for us - we changed tools and interfaces along the way, and by the end of the training we started adding new modules (but this is for new groups).

10 things to remember and like the learning process
  1. Course interactivity. You are constantly involved. I tried to watch the course in the subway on the way to work, but this is a futile undertaking. Such a quantity of interactivity and tasks during the course do not give the opportunity to just watch and listen. Here, chunks of explanation of the theory last on average from 30 seconds to 2-3 minutes. Then you definitely need to do something: write code, answer for the survey, complete the exercise, etc. Moreover, sometimes between two two-minute sections of the training video, in order to answer the small question correctly, it took me two or three hours to independently study the practical material.
  2. The material is selected based on the current needs of the business. The skills that are in demand in the industry are given. The developers of the course took the time to interview the leading companies of the Valley about what they needed right now.
  3. A skill is developed, not a theory. For example, in one of the courses you are not taught how to program in R, but you are given a ready-made tool called Exploratory Data Analysis, which is implemented using R. Therefore, you learn the language immediately in the context of its real application.
  4. A huge number of useful links. I have Favorites in the browser after the course stores more than a hundred. I periodically turn to them.
  5. Practice teachers from Facebook, Twitter, MongoDB, etc. For example, a machine learning course is taught by Sebastian Trun, CEO Udacity, Professor Stanford, a former VP of Google, and Google’s inventor of self-drive car. And he begins his course just at the wheel (or rather, sitting sideways to the steering wheel) of a traveling car.
  6. The courses are diluted with interviews with interesting people from top tech companies that tell how they put into practice what is taught in the course.
  7. High-quality feedback, verification of completed projects with detailed comments. The opportunity to ask a question through the forum, online during the weekly Office hours, or make an appointment with the teacher one-on-one.
  8. Continuous course development. For example, by the end of my training, another course and a project on A / B testing was included in the training program. And today there are already 7 projects.
  9. Good emotional involvement, innovative approach, good video quality
  10. Involving students who become reviewers. This is a plus for the student. I myself am a little doubtful that my code will be checked by someone who studied two months before me. We were in the first group, so it was the guys from Udacity who checked it.

That it was difficult or did not like

  1. California faculty accent. In reality, with my intermediate it was sometimes difficult to make out what exactly they were saying. And in the course, sometimes every word played a decisive role. Even harder, it was to write project reports in English. But this complexity allowed me to develop listening skills and in five months my IELTS Listening grew from 6.0 to 7.0
  2. Since this is the first training group that was recruited, feedback tools were periodically changed. Sometimes technical problems arose.
  3. The schedule of Office hours and webinars often fell at 3 a.m. in our opinion. Although, this is not a big problem - everything can be seen in the recording.
  4. Not quite an academic teaching style, a little unusual. The material is not always structured in terms of theory. When you get used to this style, it becomes even a plus. You learn only applied things.
  5. Despite the popularity of Data science, this course does not seem to be the most basic. Most graduates are Front End Nanodegree. It is understandable, the least of all the entry requirements for starting training on the program and finding work is not very difficult.

What is the result?
A set of skills that is not yet in great demand in Ukraine. In principle, I understood this, I studied to lay the foundation for the future. Mashable calls Data Scientist the hottest profession of 2015 and assures that in the next 10 years without knowledge of data analysis it will be impossible to apply for the position of senior and middle manager.

What is really cool is post-educational support. For example, Udacity helps with the creation of the right resume, LinkedIn profile for free, in preparation for the interview, for salary negotiations, attracts employers from the Valley. If you are lucky, then they give you the opportunity to work with them intern for two months. A whole division of career support is working on this. Unfortunately, this works really for graduates from the USA, they do not help with a visa.

Additionally created by Alumni Club. It seems that I’m constantly in touch with you, although it has already been more than six months after graduation. You remain part of the community, completely Udacians. Together with seven other graduates, we created a team to compete in the Kaggle.

I’m not sorry for a minute that I studied. Program training and collaboration with other graduates allowed me to master:
  • Python (and many different libraries)
  • R, R Studio
  • Git github
  • Mongodb
  • D3.js, Tableau
  • Statistics and Machine Learning Tools
  • Started working on Ubuntu

I think a good set to make a shift in my future career and understand where I want to grow further. Be that as it may, the program is not a magic pill or a panacea. Nobody calls on Google after the release, although a couple of graduates got there. She lays a good and correct foundation, then everything depends on you.

PS while I was going to write this post, Udacity started another Nanodegree program - Machine Learning. A more in-depth study of this topic started in Data Analyst. I’m not ready for it yet, I need at least somewhere to apply my skills in a real project.

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