Beginning Data Science With Python and Jupyter: Use Powerful Industry-Standard Tools Within Jupyter and the Python Book PDF FREE 2022.

  Beginning Data Science With Python and Jupyter: Use Powerful Industry-Standard Tools Within Jupyter and the Python Ecosystem to Unlock New, Actionable Insights From Your Data.

Why we love Python for data science:

Why we love Python for data science: Much of our data science focus is on Python for data science. 

 We love data science and Python so much, that we are developing a Python Center of Excellence in Barcelona, ​​to attract, train and place experienced talent in local and remote projects. 

Let 's see why we like Python for data science so much, especially when we compare it to other languages. 

Programming languages ​​for data science: First of all, let's see why programming languages ​​are necessary for data science.

 The first thing we need to know: data has always used some kind of programming language to function. For example, relational databases use forms of SQL (including T-SQL) to tell the database what to do with the zeroes and ones that make up the data in the database. 

 The second thing we need to know: in data science, extremely large amounts of data (Big Data) are manipulated using complex mathematical algorithms. 

 While SQL involves simple commands to merge rows of data, add or delete data, and create simple "views," advanced data science programming languages ​​manipulate data in ways that would be very expensive and downright impossible to do manually or in a spreadsheet. calculation.

 Some of the programming languages ​​available for data science are: R python matlab Java Hadoop Julia Scale Rubin Python is perfect. 

 And not just because, in survey after survey, Python is listed as the most popular and most searched language.

Python is used for:

desktop GUI scripting Web development game development machine learning data science Analysis of data Artificial intelligence Internet of Things (IoT) computer vision web-scraping Natural Language Processing Scientific and numerical computing Software application development network programming Python is easy to learn: This means that it is also one of the best for building larger teams of developers. Because of this ease of learning, Python is likely being used in all kinds of organizations. (Learning data science is a different matter, of course) Python is flexible It runs on almost all platforms including Windows and MacOS. As a programming language, it works well enough for a variety of uses, making it versatile and flexible. Python is efficient in code For what we can accomplish in R, we'll use much less code by writing it in Python. is many data science libraries and tools in language python: NumPy and Pandas Scikit Learn for machine learning pybrain tensor flow PyMySQL for MySQL databases Python Notebook for interactive programming Matplotlib for data visualization In addition to many others The final reason, of course, is that we are building a Python Center of Excellence in Barcelona, ​​dedicated to solving complex data science problems for local and global companies.

Who are Python’s data science users?

The Python Software Foundation (PSF) and JetBrains have just published a major survey on Python users in 2018. This survey based on over 20,000 responses attempts to draw a picture of the Python developers. You can find all the basic analysis here. The raw data is made available. In this article my ambition is to get a similar photograph but focusing on users on the side of data science. Python users in data science: The purpose of this article is to analyze a subcategory of Python users of the PSF survey, those who use Python to process data. Methodologically, we extracted the raw data from the survey and performed analyses on users whose main use of Python was data analysis or machine learning. This sub-category represents 28% of respondents, which allows us to have a sample of 4,585 observations. The first interesting information is that 84% of respondents use Python as their main language, which is exactly the same proportion as in the complete survey. The other languages: The languages used by Python users in data science in addition to Python are quite close to all users, we notice especially differences on languages such as R or Scala for which there are significantly more users. Conversely, users of the "web" language are rarer. The use of Python 3 is extremely developed among Python users in data science with 90% of Python 3 users. This seems logical given the recent appearance of Python in data science and the large number of new projects. How do we install Python in data science? If we go into more details, of the Python tools used, the PSF survey focuses on the tools used to install Python and not surprisingly, we see that Anaconda is much more present than other Python users.

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