What did I get in the five months of training on the program Data Analyst Nanodegree from Udacity
Hello! Saw on Habré the articles devoted to educational programs of Udacity . I finished one of these programs and would like to share my experience.
I have been involved in distance learning, or rather, for the last six years I have been accompanying a corporate educational portal and developing modules for it in a fairly large company. He himself periodically studied at different courses from Coursera, edX, Udacity.
About a year ago, Udacity launched a new kind of software - Nanodegree. I want to share my learning experience on one of them. At that time, the choice was between the Front End and Data Analyst. I chose the latter. The topic is new, interesting and quite complex. In addition, recently, many things related to data processing applied at work. Well, after such a long period of working with the same product there is a desire to develop and try yourself in a new role.
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The program contains 5 modules (now 7). The essence of all training is to learn the basic tools needed to start a career in Data Scientist. To obtain a certificate, you need to create 5 projects that require knowledge of statistics, data preparation and processing (Data Wrangling), Exploratory Data Analysis, machine learning and data visualization. If you have enough knowledge, you can immediately start projects, if not, for each of them you need to complete a course.
I signed up for the newsletter and as soon as the registration for the program opened in November 2014, I signed up for the first cohort of students. Since this was the first group, we had a little training for us — we changed the tools and interfaces along the way, and by the end of the training, we began to add new modules (but this is for new groups).
10 things to remember and like the learning process
That was difficult or did not like
What is the result?
A set of skills that is not yet very much in demand in Ukraine. I basically understood it, I studied for laying the foundation for the future. Mashable calls Data Scientist the hottest profession of 2015 and assures that in the next 10 years without the knowledge of data analysis it will be impossible to qualify for the position of top and middle level manager.
What is really cool is post-educational support. For example, Udacity helps for free with creating the right resume, LinkedIn profile, in preparing for an interview, to negotiating salary, attracts employers from the Valley. If you're lucky, they give you the opportunity to work with them as an intern for two months. The whole division of career support is working on this. Unfortunately, it really works for graduates from the United States, with a visa does not help.
Additionally created Alumni Club. It seems that you are constantly in touch, although more than six months have passed since the end of the course. You remain part of the community, completely Udacians. We, together with seven other graduates, formed a team to participate in the Kaggle competition.
I have no regrets about studying. Training in the program and cooperation with other graduates allowed me to master:
I think a good set to make a shift in my future career and understand where I want to grow further. However it may be, the program is not a magic pill and not a panacea. In Google, no one calls after graduation, although a couple of graduates still got there. It lays a good and proper foundation, then everything depends only 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.
I have been involved in distance learning, or rather, for the last six years I have been accompanying a corporate educational portal and developing modules for it in a fairly large company. He himself periodically studied at different courses from Coursera, edX, Udacity.
About a year ago, Udacity launched a new kind of software - Nanodegree. I want to share my learning experience on one of them. At that time, the choice was between the Front End and Data Analyst. I chose the latter. The topic is new, interesting and quite complex. In addition, recently, many things related to data processing applied at work. Well, after such a long period of working 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 needed to start a career in Data Scientist. To obtain a certificate, you need to create 5 projects that require knowledge of statistics, data preparation and processing (Data Wrangling), Exploratory Data Analysis, machine learning and data visualization. If you have enough knowledge, you can immediately start projects, if not, for each of them you need to complete a course.
I signed up for the newsletter and as soon as the registration for the program opened in November 2014, I signed up for the first cohort of students. Since this was the first group, we had a little training for us — we changed the tools and interfaces along the way, and by the end of the training, we began to add new modules (but this is for new groups).
10 things to remember and like the learning process
- Course Interactivity You are constantly involved. I tried to watch a course on the subway on my way to work, but this is a useless undertaking. Such a number of interactivity and tasks in the course of the course make it impossible to just watch and listen. Here, pieces of explanation of the theory on average last from 30 seconds to 2-3 minutes. Then you need to do something: write code, answer for a survey, perform an 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 to three hours to work through the practical material on my own.
- The material is selected based on the current needs of the business. It gives exactly those skills that are now in demand in the industry. The course developers took the time to interview the leading companies in the Valley about what they need right now.
- Practiced skill, not theory. For example, in one of the courses you are not taught how to program in R, but it gives a ready-made tool Exploratory Data Analysis, which is implemented using R. Therefore, you learn the language immediately in the context of its actual use.
- A huge number of useful links. My favorites in the browser after the course stores more than a hundred. I appeal to them periodically.
- Practice teachers from Facebook, Twitter, MongoDB, etc. For example, a machine learning course is given by Sebastian Troon, CEO of Udacity, Professor Stanford, former Google VP and inventor of the Google self-drive car. And he starts his course just behind the wheel (or rather sitting sideways to the steering wheel) of a riding car.
- Courses are diluted with interviews with interesting people from top tech companies that tell how they practice in practice, what is taught in the course.
- Quality feedback, verification of completed projects with detailed comments. Opportunity to ask a question through the forum, online during the weekly Office hours, or arrange a one-on-one meeting with the teacher.
- Continuous development of the course. For example, by the end of my training, another course and A / B testing project was included in the curriculum. And today there are already 7 projects.
- Good emotional involvement, innovative approach, good video quality
- Involving students who become reviewers. This is a plus for a student. I myself have a little doubtful attitude to the fact that my code will be checked by someone who studied two months before me. We were in the first group, so we checked the guys from Udacity.
That was difficult or did not like
- California teachers accent. In fact, 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 more difficult, it was writing in English reports on the project. But this complexity allowed me to develop listening skills and in five months my IELTS Listening grew from 6.0 to 7.0
- Since this is the first group of training that was recruited, feedback tools changed periodically. Sometimes there were technical problems.
- Schedule Office hours and webinars often fell at 3 in the morning in our opinion. Although it is not a big problem - everything can be seen in the record.
- Not quite an academic teaching style, a bit unusual. The material is not always structured from the point of view of theory. When you get used to this style, it becomes even a plus. You learn only applied things.
- With all 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 requirement at the entrance to start training on the program and work is not very difficult to find.
What is the result?
A set of skills that is not yet very much in demand in Ukraine. I basically understood it, I studied for laying the foundation for the future. Mashable calls Data Scientist the hottest profession of 2015 and assures that in the next 10 years without the knowledge of data analysis it will be impossible to qualify for the position of top and middle level manager.
What is really cool is post-educational support. For example, Udacity helps for free with creating the right resume, LinkedIn profile, in preparing for an interview, to negotiating salary, attracts employers from the Valley. If you're lucky, they give you the opportunity to work with them as an intern for two months. The whole division of career support is working on this. Unfortunately, it really works for graduates from the United States, with a visa does not help.
Additionally created Alumni Club. It seems that you are constantly in touch, although more than six months have passed since the end of the course. You remain part of the community, completely Udacians. We, together with seven other graduates, formed a team to participate in the Kaggle competition.
I have no regrets about studying. Training in the program and cooperation 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 in Ubuntu
I think a good set to make a shift in my future career and understand where I want to grow further. However it may be, the program is not a magic pill and not a panacea. In Google, no one calls after graduation, although a couple of graduates still got there. It lays a good and proper foundation, then everything depends only 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.
Source: https://habr.com/ru/post/298656/
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