Aspiring Data Scientists! Learn the basics with these 7 books! http://ift.tt/2fz4Cd7 STEM aimlnn Data Science Aspiring Data Scientists! Learn the basics with these 7 books! Tomi MesterNov 14, 2016 Data analyst & researcher. “Data helps you to understand your customers.” Focusing on #datadriven #startup #ecommerce #bigdata #analytics http://www.data36.com In the last few years I spent a significant time with reading books about Data Science. I found these 7 books the best. These together are a very valuable source of learning the basics. It drives you through everything, you need to know. Though they are very enjoyable, none of these is light reading. So if you decided to go with them, allocate some time and energy. It is worth it! If you combine this knowledge with the free practical data courses, that I wrote about earlier, it’s already a good-enough level for an entry level Data Scientist position. (In my opinion, at least.) Note: you can see I listed four O’Reilly books here. If it looks suspicious: I’m not affiliated with them in any way. ;-) I just find their books really useful. I suggest this specific order: 1. Lean Analytics — by Croll & Yoskovitz The first book to read is about the basic business mindset about how to use data. It says it’s for startups, but I feel like it’s much more than that. You will learn, why is it so important to select the One Metric That Matters as well as the 6 basic online business types — and the data strategy behind those. 2. Business value in the ocean of data — by Fajszi, Cser & Fehér If Lean Analytics is about business + data for startups, this book is business + data for big companies. It sounds less fancy, than the first one, but there is always a chance to pick up some useful knowledge from the big guys. Eg. how insurance companies use predictive analytics or what data issues are banks facing. 3. Naked Statistics — Charles Wheelan I constantly promote this book on my channels. It’s not just for Data Scientists. It’s the very basis of statistical thinking, which I think every human being should be familiar with. This book comes with many stories and you will learn how not to be scammed by headlines like “How we pushed 1300% on our conversion rate by changing only one word” and other BSs. 4. Doing Data Science — Schutt and O’Neil The last book before going really tech-focused. This one takes the things that you learned so far in the first 3 books to the next level. It goes deeper into topics like regression models, spam filtering, recommendation engines and even big data. 5. Data Science at the Command Line — Janssens The other thing I constantly promote is to learn (at least) basic coding. With that you can be much more flexible on getting, clearing, transforming and analyzing your data. It just extends your opportunities in Data Science. And when you start, I suggest to start with the Command Line. This is the only book, I’ve seen about Data Science + Command Line, but one is enough as it pretty much covers everything. 6. Python for Data Analysis — McKinney The second data language to learn is Python. It’s not too difficult and it’s very widely used. You can do almost everything in Python, when it comes to analysis, predicting and even machine learning. This is a heavy book (literally: it’s more than 400 pages), but covers everything with Python. 7. I heart logs — Jay Kreps The last book on the list is only 60 pages and very technical. It gives you a good view to the technical background of data collecting and processing. Most probably as an analyst or data scientist you won’t use this kind of knowledge directly, but at least you will be aware, what the data infrastructure specialists of the company do. And that’s it! As I mentioned before, if you go through on all of these — combined with the free practical data courses — you will have a solid knowledge about Data Science! Learn more about how to Create a Good Research plan — and don’t miss my new data coding tutorial series: SQL for Data Analysis!. Thanks for reading! Enjoyed the article? Please just let me know by clicking the 💚 below. It also helps other people see the story! Tomi Mestermy blog: data36.com my Twitter: @data36_com Hacker Noon is how hackers start their afternoons. We’re a part of the @AMI family. We are now accepting submissions and happy to discuss advertising & sponsorship opportunities. If you enjoyed this story, we recommend reading our latest tech stories and trending tech stories. Until next time, don’t take the realities of the world for granted! Show your support Clapping shows how much you appreciated Tomi Mester’s story.
Aspiring Data Scientists! Learn the basics with these 7 books!
Label: AI-NN-ML
Date: September 02, 2017 at 06:00PM
Aspiring Data Scientists! Learn the basics with these 7 books! http://ift.tt/2fz4Cd7 STEM aimlnn Data Science Aspiring Data Scientists! Learn the basics with these 7 books! Tomi MesterNov 14, 2016 Data analyst & researcher. “Data helps you to understand your customers.” Focusing on #datadriven #startup #ecommerce #bigdata #analytics http://www.data36.com In the last few years I spent a significant time with reading books about Data Science. I found these 7 books the best. These together are a very valuable source of learning the basics. It drives you through everything, you need to know. Though they are very enjoyable, none of these is light reading. So if you decided to go with them, allocate some time and energy. It is worth it! If you combine this knowledge with the free practical data courses, that I wrote about earlier, it’s already a good-enough level for an entry level Data Scientist position. (In my opinion, at least.) Note: you can see I listed four O’Reilly books here. If it looks suspicious: I’m not affiliated with them in any way. ;-) I just find their books really useful. I suggest this specific order: 1. Lean Analytics — by Croll & Yoskovitz The first book to read is about the basic business mindset about how to use data. It says it’s for startups, but I feel like it’s much more than that. You will learn, why is it so important to select the One Metric That Matters as well as the 6 basic online business types — and the data strategy behind those. 2. Business value in the ocean of data — by Fajszi, Cser & Fehér If Lean Analytics is about business + data for startups, this book is business + data for big companies. It sounds less fancy, than the first one, but there is always a chance to pick up some useful knowledge from the big guys. Eg. how insurance companies use predictive analytics or what data issues are banks facing. 3. Naked Statistics — Charles Wheelan I constantly promote this book on my channels. It’s not just for Data Scientists. It’s the very basis of statistical thinking, which I think every human being should be familiar with. This book comes with many stories and you will learn how not to be scammed by headlines like “How we pushed 1300% on our conversion rate by changing only one word” and other BSs. 4. Doing Data Science — Schutt and O’Neil The last book before going really tech-focused. This one takes the things that you learned so far in the first 3 books to the next level. It goes deeper into topics like regression models, spam filtering, recommendation engines and even big data. 5. Data Science at the Command Line — Janssens The other thing I constantly promote is to learn (at least) basic coding. With that you can be much more flexible on getting, clearing, transforming and analyzing your data. It just extends your opportunities in Data Science. And when you start, I suggest to start with the Command Line. This is the only book, I’ve seen about Data Science + Command Line, but one is enough as it pretty much covers everything. 6. Python for Data Analysis — McKinney The second data language to learn is Python. It’s not too difficult and it’s very widely used. You can do almost everything in Python, when it comes to analysis, predicting and even machine learning. This is a heavy book (literally: it’s more than 400 pages), but covers everything with Python. 7. I heart logs — Jay Kreps The last book on the list is only 60 pages and very technical. It gives you a good view to the technical background of data collecting and processing. Most probably as an analyst or data scientist you won’t use this kind of knowledge directly, but at least you will be aware, what the data infrastructure specialists of the company do. And that’s it! As I mentioned before, if you go through on all of these — combined with the free practical data courses — you will have a solid knowledge about Data Science! Learn more about how to Create a Good Research plan — and don’t miss my new data coding tutorial series: SQL for Data Analysis!. Thanks for reading! Enjoyed the article? Please just let me know by clicking the 💚 below. It also helps other people see the story! Tomi Mestermy blog: data36.com my Twitter: @data36_com Hacker Noon is how hackers start their afternoons. We’re a part of the @AMI family. We are now accepting submissions and happy to discuss advertising & sponsorship opportunities. If you enjoyed this story, we recommend reading our latest tech stories and trending tech stories. Until next time, don’t take the realities of the world for granted! Show your support Clapping shows how much you appreciated Tomi Mester’s story.
Aspiring Data Scientists! Learn the basics with these 7 books!
Label: AI-NN-ML
Date: September 02, 2017 at 06:00PM