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, markéting 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.

Science vs Scientist:

Data science is a vast field. In addition to the Python language, data science makes use of many other languages such as R, SQL, Julia, Scala, etc. The course is only about data science with Python. It’s not about training a data scientist. There are many other technical and analytical skills in this profession. On the other hand, a course in data science with Python fits perfectly into a broader training plan to become a data scientist. Profill: data scientist: The data scientist is a specialist Digital economy, at the intersection of computer science and statistical analysis. He has a scientific expertise of university level and know-how in a field of activity To know more, we propose this definition. The Python language: Scientic and numerical Python is only one of the possibilities of the Python language. Data science with Python can be a springboard to other language specialties as it can be a complement for a developer Python. Continue in Python: With a minimal base in Python, a scientific and digital Python pathway leads naturally to object-oriented programming, other areas of specialization in Python and the development of Web sites, of Software, GUIs and applications. Developers who master the other specializations of the Python language have an advantage, because they already know the Python syntax on which the scientique and numerical Python is based. In other words, it is easier to learn a Latin language if we already speak a Latin language than if our main language is asian. With a good background, motivation linked to his expectations and work, everything is learned. Python: a generic language We present some language specialties Python (Figures 6). See Appendix. - Network administration and communications: Twisted, PySerial), pybluez; - Systems Administration: Ansible, Salt, OpenStack ;- Bioinformatics: Biopython ; - Development of multi-touch applications: kivy; - GUI development: tkInter, PyGObject, PyQt, PySide, Kivy, wxPython; - Development of 2D video games:Pygame; - Software development: Buildbot, Trac, Roundup; - Web development: Django, Pyramid, Bottle, Tornado, Flask, web2py; - Database management: MySQLdb; - Scientic and digital python: SciPy, Numpy, Pandas, IPython, matplotlib; - Image processing, artifcial vision parcaméra: PIL, SimpleCV; - Sound processing, voice synthesis: eSpeak; - And much more.

Machine Learning Definition: What is Machine Learning?

If Machine Learning is not new, its precise definition remains unclear for many people. In practical terms, it is a modern science to discover patterns and make predictions from data based on statistics, data mining, pattern recognition and predictive analysis. e Machine Learning is very effective in situations where insights need to be discovered from large and diverse and changing data sets, i.e.: Big Data. For the analysis of such data, it is much more efficient than traditional methods in terms of accuracy and speed. Thus, this method is much more effective than traditional methods for analyzing transactional data, data from social networks or CRM platforms. Machine learning can be defined as a branch of artificial intelligence encompassing many methods for automatically creating models from data. These methods are actually algorithms. A traditional computer program performs a task according to precise instructions, and thus systematically in the same way. On the contrary, a machine learning system does not follow instructions, but learns from experience. As a result, its performance improves over time as the algorithm is exposed to more data.

Data Analytics: R or Python, what language to learn for data analysis?

In the field of data analytics, or Data Analytics, the two most used programming languages are R and Python. Discover which of these two languages is better to learn to embark on this vocation. Data Analytics: R and Pyhon allow to exceed the limits of programs like SAS and Excel: Most data analysts use spreadsheet programs like Microsoft Excel or Google Sheets. Others use proprietary statistical software such as SAS, Stata, or SPSS. However, these various tools also have limitations. Excel cannot support datasets beyond a certain limit, and cannot replicate analyses on new datasets. The main weakness of programs like SAS is that they have been developed for very specific use, and do not benefit from a large community of contributors capable of adding new tools. Data Analytics: what is the best language to learn between R and Python? These two languages are ideal for working on large data sets or creating complex data visualizations, but what is the best of these programming languages to learn for data analysis? Concretely, Python is more suitable for data manipulation and repetitive tasks, but R is better for analyzing and exploring datasets. Indeed, unlike Python, R does not allow you to create a website and automate processes. On the other hand, R is more suitable for large statistical projects and ad hoc data set explorations. In terms of learning ease, the learning curve of R is steeper, and most beginners will quickly feel helpless. Python is often considered easier to learn. Another advantage of Python is that it is a more general language, which can also be used for website creation or other computer programs. In fact, for someone who wants to become a programmer, Python is better suited. Anyway, in the field of data analysis, the differences between R and Python are getting thinner and thinner. Most of the tasks that were previously associated with either of these languages can now be performed with both languages. In fact, if your colleagues are proficient in one of these two languages, it may be wise to choose the same one. In conclusion, if you only want to do data analysis, any of these two languages will do.

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