[0] Getting Started with Internet Data Analysis The requirements of different companies in different industries will be very different, for example, banks will require SAS/SQL to do data analysis and modeling, while the Internet industry data analysis can be done as long as you know SQL. For example, a small company may require R/Python or something, but a medium-sized company like Facebook may only need to know SQL. At first glance a little strange, but actually not surprising, because the larger companies infrastructure (infra) to do a good job, many things such as A/B test such things are automated, do not need to write special code. The next content are more than medium-sized Internet companies as an example, to talk about how to prepare for the three dimensions of work required: technology, data analysis methods, industry knowledge.

【1】Technology Technology SQL is the most basic, but also the most important. Several sites where you can learn, basically there will not be much difference http://www.w3schools.com/sql/default.asp https://community.modeanalytics.com/sql/tutorial/introduction-to-sql/ https://sqlzoo.net/ Chinese version of w3c: https://www.w3cschool.cn/sql/

Some websites where you can practice.

http://www.programmerinterview.com/index.php/database-sql/advanced-sql-interview-questions-continued-part-2/ The focus needs attention: where / group by / order by / left join / right join / inner join / null / not null / having / distinct / like / union / avg / sum / min / max The rankover is already quite advanced. Of course, in addition to SQL, Excel is also to be a little bit, for example, to do a graph, calculate the total, average and so on, a little bit more complex pivot table (pivot) will be enough. If SQL is faster and you have plenty of time, then practice Tableau, the main purpose is to see what kind of charts are available and feel what kind of scenarios they are applicable to. It's not very important to know how to do the diagrams, and it's good to search for them when you really want to use them. Tableau is very expensive, so the next trial version can be, and then the trial period to learn some of the most basic can be. Speaking of data visualization, mention this blog again, the author wrote "Storytelling with Data":

http://www.storytellingwithdata.com/

【2】 Data Analysis Methods Often asked to learn which books are recommended for data analysis, usually the answer is that I have not read any data analysis books, most of the time is to search for the integration of a variety of online resources, and then carefully thought about it, there are still some of them. Case in point. the classic management consulting book, which version does not matter, the impression of about half to read about. It seems that because of laziness, so did not finish reading, but also because the set is similar, look at half can almost be. Introduction to Probability Models by Sheldon M. Ross. should be out to the 11th edition, but the content should not be very different, look at the first chapter can be, need to figure out the conditional probability, the concept is still a little important.

Storytelling with Data, the Chinese version of Storytelling with Data, the author is also the author of storytellingwithdata.com mentioned earlier. Then find a book on the basics of statistics (any textbook is almost the same, if you can't read the long entry on the wiki under statistics a few more times), don't get too tangled in the theory, proof, always remember that you have to be able to explain these concepts to people who don't know statistics, explain things that are not clear you figure out, the effect is also to greatly reduce.

Figure out several common distributions, hypothesis testing, false positives, false negatives, difference estimation, significant differences, p-value, mean, median, p1/p25/p50/p75/p99, correlation, causality, survivorship bias, law of large numbers, 80/20, etc. Thinking, Fast and Slow. When the popular science book to look at it, if you can not read then read "Milk and Coke Economics".

[3] Industry Knowledge Unfortunately, this part of the book is really not particularly relevant to read, basically rely on the search, summary, thinking, and then search, summary, thinking. If you are usually relatively concerned about the Internet, technology industry, this part will get started quickly, to understand some basic concepts, try some products, basically 20 days to reach a level of entry.

3.1】Try the relevant products All Internet companies emphasize that employees should use their own products, also known as dogfood, which is the most effective way to improve product thinking, no one. For example, Airbnb will provide a certain amount of money each quarter for employees to go on vacation when they can use to live on Airbnb listings, Uber will provide employees with credit to play Uber, Facebook will provide employees with credit to advertise on Facebook, usually a few hundred dollars per quarter. In this way, employees may find some bugs, or provide some product-related feedback, etc. Obviously, even if you are not an employee of the above company, you can still use their products and think about what they are doing and what can be improved. Take google map for example, there is a feature that shows the street view of the destination when you reach it. Then there can be a series of questions. Why do you want to show the street view? To facilitate the user to identify the destination. What else can be displayed besides the street view? Maybe there is no parking lot at the destination itself, so it might be a good feature to automatically give users some options if they need to park. Suppose we want to do such a feature, under what circumstances should it be displayed? For example, obviously it should only be displayed when the user is driving, so how can you tell if the user is driving and not walking? For example, if there is a parking lot at a destination, then there is no need to display it, so how can we tell? If we want to display the parking lot, what information should be displayed? For example, the distance of the parking lot? Price? Opening hours? Assuming that some new features have been made, how can we verify whether the results are good, A/B test, how to choose metrics, and which users to use? Similar questions can be asked all the time, more similar thinking training, the accumulation of industry-related knowledge, training product thinking is very helpful.

And this accumulation does not mean that we must sit at the table and start thinking slowly, after forming a habit, the process of using the product will naturally think of these.

For example, the above example is in a navigation after found google map automatically displayed the destination of the street view and thought of. Data analysis work in addition to the need for technical polish, how to train the analytical process of thinking, that is, analytical/critical thinking is also a very important part. A very effective way is to encounter a problem, ask yourself at least 5 why, and keep going deeper and deeper, peel the silk cocoon, the problem is naturally more and more clear.

3.2] Make good use of search engines When the information explosion, how to search for information, aggregation, refining the useful information becomes particularly important. Specific about how to use google some tips, this article will not be detailed. In addition, the wall of students, try to climb the wall or use Bing it. In addition, Zhihu and quora have gathered a large number of Internet-related practitioners, many questions and answers are also related to this, as to whether you can find the information you want, the search skills are very important.

3.3】Exchange with industry seniors This industry seniors can be already working in the industry, brothers and sisters (sometimes brothers and sisters), but also in the industry and have nothing to do with the people. Linkedin is a good place to get to know such people and get them to help you. Personally, I would love to help my fellow countrymen who are somewhat prepared. Note that being somewhat prepared and having spent some time on your own to accumulate thoughts makes the questions more focused and effective. If you do not directly know the people working in this field, it is likely that you can also indirectly know through your friends, in addition to some social networking sites, such as WeChat group, Zhihu, etc., you can also know a lot of industry cattle. Speaking of which, I have to mention a big V on Zhihu, who is doing data analysis in Ant Financial Services. One time someone asked him, as more than 100,000 powder Zhihu big V, the best way to cash is what? He answered to the effect that through such resources to know more industry cattle, and constantly enhance their own, which is the best way to "cash in. Another good way is through various offline gatherings, such as meetups, which are more common in the US, especially in the Bay Area, where there are events for various industries from time to time. At these events, you can usually talk to people in the industry about what they do, ask questions, and generally they will also promote their recruitment plans, so you can kill two birds with one stone. [3.4] Networking resources No matter where you are in the world, as long as there is a network, it also means that you can access to the world's countless open courses, industry leaders share. But too much information means that it becomes more difficult to streamline your selection. Here I share some of the resources I've summarized over the past few years, as well as a brief rationale for my recommendations.

[3.5] Books Zero to One by Peter Thiel -- From 0 to 1 The Hard Thing about Hard Things by Ben Horowitz These two books are about entrepreneurship and both have Chinese versions. If you haven't read them and want to work in the Internet industry, you should definitely read them. Although it is about entrepreneurship, it allows one to take a step back and understand how to make a good product from a higher perspective.

[0] Getting Started with Internet Data Analysis The requirements of different companies in different industries will be very different, for example, banks will require SAS/SQL to do data analysis and modeling, while the Internet industry data analysis can be done as long as you know SQL. For example, a small company may require R/Python or something, but a medium-sized company like Facebook may only need to know SQL. At first glance a little strange, but actually not surprising, because the larger companies infrastructure (infra) to do a good job, many things such as A/B test such things are automated, do not need to write special code. The next content are more than medium-sized Internet companies as an example, to talk about how to prepare for the three dimensions of work required: technology, data analysis methods, industry knowledge.

【1】Technology Technology SQL is the most basic, but also the most important. Several sites where you can learn, basically there will not be much difference http://www.w3schools.com/sql/default.asp https://community.modeanalytics.com/sql/tutorial/introduction-to-sql/ https://sqlzoo.net/ Chinese version of w3c: https://www.w3cschool.cn/sql/

Some websites where you can practice.

http://www.programmerinterview.com/index.php/database-sql/advanced-sql-interview-questions-continued-part-2/ The focus needs attention: where / group by / order by / left join / right join / inner join / null / not null / having / distinct / like / union / avg / sum / min / max The rankover is already quite advanced. Of course, in addition to SQL, Excel is also to be a little bit, for example, to do a graph, calculate the total, average and so on, a little bit more complex pivot table (pivot) will be enough. If SQL is faster and you have plenty of time, then practice Tableau, the main purpose is to see what kind of charts are available and feel what kind of scenarios they are applicable to. It's not very important to know how to do the diagrams, and it's good to search for them when you really want to use them. Tableau is very expensive, so the next trial version can be, and then the trial period to learn some of the most basic can be. Speaking of data visualization, mention this blog again, the author wrote "Storytelling with Data":

http://www.storytellingwithdata.com/

【2】 Data Analysis Methods Often asked to learn which books are recommended for data analysis, usually the answer is that I have not read any data analysis books, most of the time is to search for the integration of a variety of online resources, and then carefully thought about it, there are still some of them. Case in point. the classic management consulting book, which version does not matter, the impression of about half to read about. It seems that because of laziness, so did not finish reading, but also because the set is similar, look at half can almost be. Introduction to Probability Models by Sheldon M. Ross. should be out to the 11th edition, but the content should not be very different, look at the first chapter can be, need to figure out the conditional probability, the concept is still a little important.

Storytelling with Data, the Chinese version of Storytelling with Data, the author is also the author of storytellingwithdata.com mentioned earlier. Then find a book on the basics of statistics (any textbook is almost the same, if you can't read the long entry on the wiki under statistics a few more times), don't get too tangled in the theory, proof, always remember that you have to be able to explain these concepts to people who don't know statistics, explain things that are not clear you figure out, the effect is also to greatly reduce.

Figure out several common distributions, hypothesis testing, false positives, false negatives, difference estimation, significant differences, p-value, mean, median, p1/p25/p50/p75/p99, correlation, causality, survivorship bias, law of large numbers, 80/20, etc. Thinking, Fast and Slow. When the popular science book to look at it, if you can not read then read "Milk and Coke Economics".

[3] Industry Knowledge Unfortunately, this part of the book is really not particularly relevant to read, basically rely on the search, summary, thinking, and then search, summary, thinking. If you are usually relatively concerned about the Internet, technology industry, this part will get started quickly, to understand some basic concepts, try some products, basically 20 days to reach a level of entry.

3.1】Try the relevant products All Internet companies emphasize that employees should use their own products, also known as dogfood, which is the most effective way to improve product thinking, no one. For example, Airbnb will provide a certain amount of money each quarter for employees to go on vacation when they can use to live on Airbnb listings, Uber will provide employees with credit to play Uber, Facebook will provide employees with credit to advertise on Facebook, usually a few hundred dollars per quarter. In this way, employees may find some bugs, or provide some product-related feedback, etc. Obviously, even if you are not an employee of the above company, you can still use their products and think about what they are doing and what can be improved. Take google map for example, there is a feature that shows the street view of the destination when you reach it. Then there can be a series of questions. Why do you want to show the street view? To facilitate the user to identify the destination. What else can be displayed besides the street view? Maybe there is no parking lot at the destination itself, so it might be a good feature to automatically give users some options if they need to park. Suppose we want to do such a feature, under what circumstances should it be displayed? For example, obviously it should only be displayed when the user is driving, so how can you tell if the user is driving and not walking? For example, if there is a parking lot at a destination, then there is no need to display it, so how can we tell? If we want to display the parking lot, what information should be displayed? For example, the distance of the parking lot? Price? Opening hours? Assuming that some new features have been made, how can we verify whether the results are good, A/B test, how to choose metrics, and which users to use? Similar questions can be asked all the time, more similar thinking training, the accumulation of industry-related knowledge, training product thinking is very helpful.

And this accumulation does not mean that we must sit at the table and start thinking slowly, after forming a habit, the process of using the product will naturally think of these.

For example, the above example is in a navigation after found google map automatically displayed the destination of the street view and thought of. Data analysis work in addition to the need for technical polish, how to train the analytical process of thinking, that is, analytical/critical thinking is also a very important part. A very effective way is to encounter a problem, ask yourself at least 5 why, and keep going deeper and deeper, peel the silk cocoon, the problem is naturally more and more clear.

3.2] Make good use of search engines When the information explosion, how to search for information, aggregation, refining the useful information becomes particularly important. Specific about how to use google some tips, this article will not be detailed. In addition, the wall of students, try to climb the wall or use Bing it. In addition, Zhihu and quora have gathered a large number of Internet-related practitioners, many questions and answers are also related to this, as to whether you can find the information you want, the search skills are very important.

3.3】Exchange with industry seniors This industry seniors can be already working in the industry, brothers and sisters (sometimes brothers and sisters), but also in the industry and have nothing to do with the people. Linkedin is a good place to get to know such people and get them to help you. Personally, I would love to help my fellow countrymen who are somewhat prepared. Note that being somewhat prepared and having spent some time on your own to accumulate thoughts makes the questions more focused and effective. If you do not directly know the people working in this field, it is likely that you can also indirectly know through your friends, in addition to some social networking sites, such as WeChat group, Zhihu, etc., you can also know a lot of industry cattle. Speaking of which, I have to mention a big V on Zhihu, who is doing data analysis in Ant Financial Services. One time someone asked him, as more than 100,000 powder Zhihu big V, the best way to cash is what? He answered to the effect that through such resources to know more industry cattle, and constantly enhance their own, which is the best way to "cash in. Another good way is through various offline gatherings, such as meetups, which are more common in the US, especially in the Bay Area, where there are events for various industries from time to time. At these events, you can usually talk to people in the industry about what they do, ask questions, and generally they will also promote their recruitment plans, so you can kill two birds with one stone. [3.4] Networking resources No matter where you are in the world, as long as there is a network, it also means that you can access to the world's countless open courses, industry leaders share. But too much information means that it becomes more difficult to streamline your selection. Here I share some of the resources I've summarized over the past few years, as well as a brief rationale for my recommendations.

[3.5] Books Zero to One by Peter Thiel -- From 0 to 1 The Hard Thing about Hard Things by Ben Horowitz These two books are about entrepreneurship and both have Chinese versions. If you haven't read them and want to work in the Internet industry, you should definitely read them. Although it is about entrepreneurship, it allows one to take a step back and understand how to make a good product from a higher perspective.