“Data Science A-Z from Zero to Kaggle Kernels Master”

“Data Science A-Z from Zero to Kaggle Kernels Master”A brief story of my last year learning Data ScienceLeonardo FerreiraBlockedUnblockFollowFollowingAug 9I’m from Brazil and many people of all world get in touch with me to ask for tips to learn or get a vacant job in the area of data science, so I decided to write this text to have something a bit more “structured” and contribute in the better way with people who are in the beginning of this journey.In this initial article, I will make a kind of contextualization for the next texts that I intend to write, where I will go deeper in some of the topics that I will address in this text.I will tell my story in the data area so far, and in the end, I will leave some tips to help those who are starting and also want to enter the area but don’t know very well what to do, where to start or where to go.My first contact with data science was just over a year and a half ago, on May 25, 2017..I started by testing some courses, and since I had found the platform different from the ones I had already tested, I decided to try the paid version and started the “Data Scientist Track with Python”, where if I could not develop the code or understand the problem I could click on “HINT” and a hint or instruction on the task would appear, and if I still did not know how to do it, just hit the HINT again and the system presented the complete code..I was doing the course and completing the theory with the book Data Science of Zero, more specific books, texts, blogs, papers, groups, forums, videos on Youtube and several other references to understand better each concept and applications … But when it started the part of Machine Learning, I began to feel quite insecure because it had the impression that I “was not learning”, which can be normal for who is beginning in any area and has not yet put the learning in practice.Dominated by frustration, and unsure of what to do, I decided to try the “Data Scientist Track with R” because I had read that R was a statistical language, and because I was frustrated with Python, I got deep into the R, and maybe for now being somewhat familiar with Python, I had a great facility with the R language, where I finished in a short time, and I also reinforced most of the concepts and reapplied to other datasets … After finishing this Track, I decided to do another R course , focused on the financial area, the “QuantitativeAnalyst Track with R”, where I learned a lot of interesting and new things..Until in December 2017, I got a internship opportunity, and as I had to use use Python at work, I went back to the Python course in the Datacamp.Since I had been working with Python for some time, I was quite lost when I got back, and it was when I had to put my knowledge into practice in the first “real dataset”, with the task of predicting fraud for a company, and this time, there was no one to tell me what to do, how to analyze, what to use and what metrics were important … There was not much right and wrong.In the first moment I was completely lost, without knowing where to start, because until then, I had only seen theory, especially with a dataset that was not “popular” (as the iris, mushrooms, taxi ny, breast cancer etc), where the variables that should be investigated were somewhat obvious and very limited.My total exp and completed Courses on Datacamp on August 2018As the days passed, things were making more sense and the project was delivered on time, and in the end we had a good result in the predictions of the frauds, but even if everything went well, it was quite tense for me, because I still got some difficulties doing anything that involved code, and as I had heard about Kaggle, which is a platform for predictive modeling and analytical competitions in which statisticians and data miners compete to produce the best models to predict and to describe the sets of data sent by companies and users that was recently acquired by google, I went after a classic dataset they was recommended to me, which was the German Credit Risk where the objective was to develop an analysis and predict if the credit could or not being granted to a particular client based on their histories, and since I am an accountant, I had a certain facility to explore and present the data in a which ultimately earned me many votes on the data platform and motivated me to continue producing more kernels until I was comfortable enough with code and different methods of analysis across different data types and different industries, trying to focus data in a more abstract form, but always considering the different nuances of each industry.I quickly realized that there was a niche for analysis with a more financial / economic side, that for some reason, few people focused on this type of analysis, but that there was a good acceptance.My first interaction with Kaggle was on January 8, 2018 and in a month and a half, I reached the level of Kaggle Expert Kernels..At the same time that the competition rolled in April 2018, I changed my company and went to work in a company that visualizes really big data, with javascript (and I didn’t know javascript yet) in a platform of its own, where I already entered as a data scientist, and a month and a bit later, May 20, I was the 21st person in the world and the first Brazilian to win the Kaggle Kernel Master status.After all analysis and works I have done in Kaggle, and the projects that I have participated in the last few months for big companies, plus the long hours of studies, I already have accumulated a good baggage to be able to have a good understanding and frequent insights of data, to products, analyzes, and often only by curiosity or knowledge itself..This is a very interesting and rich area for anyone who likes to learn, is not afraid of challenges and especially to peoples that like to solve problems.From what I have seen so far, there is no single profile or consensus for a data scientist, and this is very liberating because it opens space for people from the most varied spectrum of society, and although many try to put innumerable constraints and your area or academic status as a prerequisite, the important thing is that you can think, understand, explain and mainly apply the knowledge in any type of dataset.I wrote too much here, I told my story just to contextualize the tips I give to friends and people who call me on LinkedIn or Kaggle, and show that it is possible to learn in a short time and with quality, as long as there is dedication and focus.I have currently had good conversations about data with people all over the world, and mostly worked very closely with the award-winning and great master Anderson L..So I intend to go soon.Finally, here are some tips to people that are in this way and want to become a data scientist:1 — IT TAKES TIMEJust as you cannot lose weight or become muscular in a few weeks, or months, do not expect data science to be any different.. More details

Leave a Reply