This first one will generate a new user. Exit from postgreSQL and go back to bash! Then you have to log out from the superuser as well and go back to your normal user! Now you can login with your normal user to your SQL database with this command: You are back to SQL again!
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The 2nd and the 3rd fill some values in it. This is a desktop application for postgreSQL, that you can use to access your SQL database from your personal computer without connecting to your remote server in terminal and write queries much easier. As a first step — make your remote server ready to connect by typing these 5 lines of code copy-paste it one by one:. What you are doing here is to login to the root user and make some modification in the config files of postgreSQL.
Then download pgadmin4 from here: Select your OS, then download, install and run it! If you right click on the name of the server on my screenshot: Or connect the same way, when you want to get back. Also if you left click on one of your databases on my screenshot: Notice that on my screenshot you can see the very same result, that we got in the Terminal SQL! R is the easiest tool to set up! First use apt-get again to install R: Now you have R. The syntax is a bit different, than it was on Python and much more different than it was in Bash.
You can exit from R: We have an application for R as well to make your data coding easier. You can login with your username eg. We will start from the very basics, I promise! If you want to be notified first about new content on Data36 like articles, videos, handbooks, etc. You have everything from the technical side to start coding in Python! Now this tutorial will start off with the base concepts, that you must learn before we go into how to use Python for Data Science. The five Python base concepts will be:. The first one is here:.
In Python we like to assign values to variables. Because it makes our code better, flexible, reusable and understandable. When we refer to something, that refers to something, that refers to something… well, understanding that, needs some brain capacity. We will type this into a Jupyter notebook cell:. But this all-in-one solution was easier and more elegant. Just like in SQL , in Python we have different data types. In Python 3 a string is a sequence of Unicode characters eg. Another numeric data type is float , in our example: Booleans can be only True or False.
There are many more data types, but as a start, knowing these four will good enough and the rest will come along the way. You have just learned about variables. Well, first of all a bunch of basic arithmetical operations! We can use some variables with comparison operators.
The results will always be Boolean values! And eventually we can use logical operators on our variables! This is easy and maybe less exciting, but again: What will be the returned data type and the exact result of this operation? First try to find it out without typing it into Python — then check if you have guessed right! False or True and True , which is True.
But this time try to figure out the result of this slightly modified expression: There is a trick here!
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To give a proper answer you have to know one more rule! The evaluation order of the logical operators is: As we have discussed, the first logical operator evaluated is the not. After firing all the not s, this is what we have: True or True and False. The second step is to evaluate the and operator. True or True and False , which leads to True or Fals e.
Done with episode 1! Did you realize, that you have just started to code in Python 3?
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Regardless of what questions you are interested in learning about, you can see that with only a little bit of Python, data analysis is simple and straightforward. With Python, you can ingest and transform data in less than 10 minutes and start exploring your questions immediately. There are many different online courses for an introduction to Python.
I recommend taking a look at a few and determining which fits your needs. Whatever your path to Python data scientist is, remember to stay curious! Through your curiosity and willingness to learn, you will have a long and successful endeavor as a data analyst. I remember learning in school that the difference between freedom of speech and freedom of the press is that not everyone has a printing press. The internet provided us with the kind of a printing press, and it has given us the power to perform the sort of analysis that until recently was exclusively available to governments and wealthy corporations.
During the past year, we have increasingly heard that data science is the best profession of the 21st century and the one that will be in greatest demand. The upshot is that you should spend time in the coming months learning whatever you can about data science. No matter what work you do, you will be able to use the tools of data science to analyze past performance and make predictions based on it.
Nowadays, although there certainly are places that have a full-time database staff, the assumption is that every developer has at least a passing familiarity with relationship or even NoSQL databases and how to work with them. The world of data science—the tools, the algorithms, the applications—are moving at an overwhelming speed. Just be sure to increase your understanding of the theory, algorithms, techniques and software that data scientists use.
Where should you start? Every little bit helps! So, where do you start?
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You probably should be able to download and read also Elements of Statistical Learning by Hastie, Tibshirani and Friedman. Both books are published by Springer, and both would be available in PDF form, as free downloads. His writing is clear, and he tries to be very practical about what he shows you. Note that you definitely should not wait until you have read through books, watched lectures and taken courses to start playing with machine learning. That would be akin to saying you should try to learn a language only after you have mastered its grammar. Statistical learning refers to a set of tools for modeling and understanding complex datasets.
It is a recently developed area in statistics and blends with parallel developments in computer science and, in particular, machine learning. The field encompasses many methods such as the lasso and sparse regression, classification and regression trees, and boosting and support vector machines. People with statistical learning skills are in high demand. ESL has become a popular text not only in statistics but also in related fields.
But ESL is intended for individuals with advanced training in the mathematical sciences. An Introduction to Statistical Learning ISL arose from the perceived need for a broader and less technical treatment of these topics. In this new book, we cover many of the same topics as ESL, but we concentrate more on the applications of the methods and less on the mathematical details.
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We have created labs illustrating how to implement each of the statistical learning methods using the popular statistical software package R. These labs provide the reader with valuable hands-on experience.
In order to motivate our study of statistical learning, we begin with a simple example. Suppose that we are statistical consultants hired by a client to provide advice on how to improve sales of a particular product. The Advertising data set consists of the sales of that product in different markets, along with advertising budgets for the product in each of those markets for three different media: TV, radio, and newspaper. The data are displayed in Figure 2. It is not possible for our client to directly increase sales of the product.
On the other hand, they can control the advertising expenditure in each of the three media. Therefore, if we determine that there is an association between advertising and sales, then we can instruct our client to adjust advertising budgets, thereby indirectly increasing sales. In other words, our goal is to develop an accurate model that can be used to predict sales on the basis of the three media budgets.
More generally, suppose that we observe a quantitative response Y and p different predictors, X1,X2,. In this formula- error term tion, f represents the systematic information that X provides about Y. The plot suggests that one might be able to predict income using years of education. However, the function f that connects the input variable to the output variable is in general unknown. In this situation one must estimate f based on the observed points. Since Income is a simulated data set, f is known and is shown by the blue curve in the right-hand panel of Figure 2.
The vertical lines represent the error terms.