The trajectory of the 4th year university students who are aiming for data scientist in the future started studying in October 2019 I would like to post to qiita with the theme of "a story of a person who started aiming for data scientist from a beginner". I write about what I learned in the first two months, including self-introduction and motivations.
――From next year, I will get a job as an engineer of a major sler with no experience --MARCH level university 4th grade ――At university, majoring in the fields of statistics and management information --Full-scale study of Python from October 2019 ――Until then, I used R lightly and made a simple website with HTML \ CSS to some extent.
――Because I wanted to do advanced analysis in my seminar graduation thesis ――Because I wanted to acquire skills as an engineer, not completely inexperienced ――Because I really wanted to study something for the rest of the time as a college student ――I'm very interested in the mathematical approach ――Because I was originally interested in machine learning in the hot field of today ――Uncertain that the industry called sler will disappear as it is ――Honestly, I have a longing for the web industry
This is where I learn! I think the biggest aspect of investing in the future is: v: It's been less than two months since I started learning, but I've gradually come to understand it, so it's become a lot of fun.
Well, the main subject is from here! !! Please note that it is very hard to see because it is a bulleted list: woman_tone1:
→ First, I was interested in something like programming, so I bought it.
→ You can roughly understand the description method. Feeling that the object-oriented meaning peculiar to programming is not well understood
I think it's about 5 hours in total in 2 days!
This is a continuation of the above course. I think it's about 5 hours in total in 2 days as well! It was really good to be able to learn the theory such as supervised learning and unsupervised learning and regression analysis at a level that can be used in practice through the two courses! Highly recommended for beginners!
I wanted to study after understanding the above four things, so I read it to see how to study for the rest of the time to become a professional. I finished reading it in about 3 hours.
Speaking of programming school, web-based is the main, but I wanted to know a specific study method, so I went to experience with Aidemy and SAMURAI engineer who are focusing on AI. I went to codecamp, but I don't remember much because I didn't do AI in earnest. The SAMURAI engineer was particularly glad to have a free trial. I thought about studying at school, but it was financially difficult, and even if I learned it now, the code was too low, so I didn't have to ask the instructor and I mainly studied by myself. I thought it was bad. I will consider the school after strengthening a little more in the future.
This was mostly the main thing in November. There are various courses, but I went through the Python grammar (study time is about 82 hours) course from inexperienced. I started to play basic chords, but it is difficult to draw a line as to how much I should remember. After this is over, I am doing a machine learning course (the standard study time is 42 hours or more). Currently in progress.
I bought and read the book that Aidemy had when I had a free trial. It took me about two weeks because I was reading slowly and solving the problems listed.
I started reading around November 20th. I think it is the most famous book related to artificial intelligence. It will be readable in about 5 days. It was very interesting.
It was mentioned in a book for those who want to become machine learning. Knowledge of linear algebra is indispensable for understanding machine learning, so I have been learning since my second year of university. I think it will take about 2 weeks.
I think it is essential for improving study productivity. I use a site called e-typinng. I've been doing it for 30 minutes every day since November 20, 2019.
Take the TOEIC (L / R) test on November 24, 2019. Average 1.5 hours during October. Before the exam in November, I spent about 3 hours studying. I got 700 points a year ago, so I was studying with the goal of 800 points. When I became a member of society, I was studying because the specifications were in English and I was thinking of studying at overseas sites such as Coursera in the future.
Originally I was using R, but since I am studying python, I analyze it with python and write my thesis. I am doing data analysis for the J League of soccer. It is a valuable output place.
① Participation in TEAM AI study session ② Challenge Kaggule as a trial → This is also scheduled to participate in the study session ③ Try exercises with pyq mainly for machine learning ④ Linear algebra campus seminar (1 lap by December 10) ⑤ Check the statistical test level 2 and start studying
E Qualification: To certify the skills of engineers who implement deep learning G test: Tests whether you have the knowledge to utilize deep learning in your business The qualifications from here are interesting so I will study
I will continue to do my best through trial and error! !! !!
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