When I was in college, I worked as an intern at Microsoft twice. The first internship was in the data science department in San Francisco, and the second internship was in the position of product manager in Seattle. In this program, I will focus on sharing my first data science internship. In case you are not familiar with data science, in short, it is a combination of computer science and statistical mathematics.

skill

So before I share how to get this job, I believe that getting a perfect job is actually just a formula. First of all, you need to master the skills.

Data structure and algorithm

In order to get this job in data science, the first thing I did was to take some programming courses. Including basic programming, data structures and algorithms. Using the algorithms I learned in these courses, I finally got my first technical internship. I was an intern in a small software development company in Beijing. After the internship, I began to study some interesting math problems.

Autonomous learning and practical projects

After that, I also spent several months studying statistical mathematics, because this is my major. Then, I began to collect the machine learning courses of California Institute of Technology online through myself. For the resources of these courses, you can visit the Virtual Private School, which corresponds to our courses and get links to the tutorials.

Then, using the knowledge I learned from these courses, I began to practice some machine learning projects on a website called Kaggle. Kaggle is a website founded by 20 10 in Melbourne, which mainly provides a platform for developers and data scientists to hold machine learning competitions, host databases, write and share codes. This platform has attracted the attention of many data scientists, and the resources of these users are the main factor that attracts me.

comprehensive ability

Therefore, after making these preparations, when I applied for the position of Microsoft Data Science, I believe that what stood out was my major in statistical mathematics, my programming experience and the comprehensive ability of machine learning projects. This comprehensive accumulation of knowledge may not be found in the resume of any other job seeker.

Interview question

There are two main types of questions in interviews for data science positions in San Francisco. One kind of problem is to solve mathematical problems. Some mathematical problems mainly focus on probability, while others focus on combinatorics. I'm actually well prepared for this kind of question. After all, it's my major. The other is related to data analysis. For this kind of problem, it will be helpful to practice some machine learning related projects. These necessary skills are not because I want to find a job in data science, but mainly because I really enjoy the process of practicing machine learning projects. I know that these projects are helpful to find a job at some time and to some extent. I also know that basic math skills are worth learning because they are universally applicable.

The ability to link information

So, let's go back to the formula mentioned earlier. To get a satisfactory job, as I said just now, besides having skills, you also need the ability to link information. Before applying for this position, I also tried to take part in some data science activities during my college years. So I told my statistics professor this idea, and then one day, she told me that there was a lecture, and a lecturer from Microsoft explained how data is used in science and statistics. So I attended this lecture, and then I asked the lecturer if Microsoft had any interns in data science, and he said yes, so I sent him my detailed resume. That's how I got the interview. Just like mastering skills, I want to participate in activities related to data science not only because I want to put it on my resume. But because I want to keep in touch with this information in order to get a job opportunity. That's what makes sense to me.

abstract

To sum up, first of all, I think the combination of formal education and practical experience and personal projects is the core competitiveness. In my personal experience, I taught myself statistics courses, had an internship experience in a project, and then had my own projects related to mathematics and machine learning, which contributed to my first internship at Microsoft. Then, my second experience is: I think you should enjoy the process of building your own skills and connecting information. If you are interested, you will naturally master these skills more easily. Well, that's all the geek programmers cut wood in this issue. We shared the knowledge of learning objectives and work planning. I hope you can find a good internship or job. Finally, if you want to hear more free dry goods audio programs, like and subscribe to our programs. See you next time!