Python for Beginners Comprehensive Guide to the Basics of Programming, Machine Learning, Data Science and Analysis with Python. BOOK PDF FREE FULL 2022.

 Python for Beginners Comprehensive Guide to the Basics of Programming, Machine Learning, Data Science and Analysis with Python. BOOK PDF FREE FULL 2022.

Python more than ever essential in Data Science:

On average, Data Scientists use three programming languages: 

Python, SQL and R. Python remains the first language in Data Science (87%), increasing over one year. 

79% of data professionals recommend it as their first language. The data scientist toolkit has several essential features, including mastering one or more programming languages. And according to the State of Data Science and Machine Learning study, these languages are most likely Python, SQL and R. The most widely used data scientist, however, remains Python. Of the 20,000 data professionals surveyed, 87% report its use. It thus largely supplants the other two main languages, SQL (44%) and R (31%). 

 Python, default language of the data scientist: 

 Among the Top 10 of the Data, we can also mention Java, C, C++, JavaScript, Bash, MATLAB, and TypeScript.

 But the unmissable is Python. 

Its adoption has even progressed by four points over a year. On the other hand, the R recorded a decrease of 5 points over the same period. 

However, its decline is earlier since in 2017, 46% of data scientists used this language in their data science tasks. 

 87% of data professionals regularly use Python This trend should continue. Indeed, only 9% of the data scientists surveyed recommend R as the main development language. 

On the contrary, 79% of them do it concerning Python. Data scientists nevertheless multiply skills. Thus, mastering one language does not exclude knowledge of others. Indeed, on average, a data scientist claims to use three languages, not just one. 

The study therefore highlights above all the place of default programming language acquired or being acquired by Python in the fields of data science and machine learning. 

Another finding: a blatant lack of diversity.

 Not in terms of skills this time. It opens the door to more sophisticated techniques such as neural networks and deep learning. The scientific and digital Python gives a programming base to progress in Web development, software, applications, Governments, etc. The target clientele: We define the target clientele by its background and expectations. 

Typical backgrounds You must have used or be familiar with some one of the concepts that will follow. In other words, there are career paths that justify and facilitate learning Python scientific and digital.

 - Technical (CEGEP) or university training in a natural environment Science: Pure Health Sciences, Applied Sciences and Engineering.

 - Technical (CEGEP) or university training in a quantitative sector: social and administrative sciences with a concentration in quantitative methods (economics, accounting sciences, mark√©ting quantitative, geography, etc.); 

- Know other programming languages and/or programming from certain software (logic, algorithms), use software based on calculations and analysis; 

- Being familiar with a terminal and its controls or a line interface commands (UNIX, Mac OS X terminal, Linux bash, console or PowerShell Windows);

 - Use mouse or point-and-click computing software (mathematical, statistical, optimization or operational research software); 

- Use quantitative methods such as mathematics, statistics, computer science or computational methods in non-digital or unstructured data contexts (text and language analysis natural, social media work, web, user experience, etc.); 

- Use unstructured databases or NoSQL, vector files and JSON.

Post a Comment

Previous Post Next Post