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Best Machine Learning Book for Beginners

You need a mixof different technologies for Data Science projects. Instead of learning many skills, just learn a few. The four main steps of any project are extracting the data, model development, artificial intelligence, and presentation. Attending interviews with many skills is not so easy. So keep the skills short.
A person with many skills can't perform all the work. You had better learn a few skills like Python, MATLAB, Tableau, and RDBMS. So that you can get a job quickly in the data-science project.
Out of Data Science skills, Machine learning is a new concept. Why because you can learn Python, like any other language. Tableau also the same. Here is the area that needs your 60% effort is Machine learning.  Machine Learning best book to start.

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5 Emerging Trends in Data Visualization

Nowadays, we deeply drowned in data of diverse kinds due to the increased computational power and accessibility.





Specifically, in addition to public data available on the Internet (e.g., census, demographics, environmental data), data pertaining personal daily activities are now more easily collected.




For example, through mobile devices that can log people's running distances and time or their manual record of nutrition consumption. Due to such expanded sources of data, there appear new applications that involve data collection, visualization, exploration, and distribution in daily contexts.


These applications do, not only display static information but also let users navigate the data in forms of interactive visualizations.


5 Emerging Trends in Data Visualization.



#1: This emerging trend has brought both opportunities and challenges to interaction designers to develop new approaches to designing data-based applications.




#2: Conveying information has been one of main functions of graphic and communication design since the analog printing era. The focus of information design is the communicative and aesthetic presentation of structured data as in an example of subway route map.




#3: In treating the increasing volumes of unprocessed data accessible either from public media or personal devices, the approaches of information design are now more diverse with the influence of other disciplines (Pousman & Stasko, 2007).




#4: Specifically, unlike information design in a traditional and confined manner, data visualization starts with the data that did not proceed through structuring and exist often in large volumes and complicated formats (Manovich, 2010).




#5: Thus, data visualization requires designers to obtain diverse knowledge and skill sets in addition to their visual aesthetic senses. The new requirements include human visual perception and cognition, statistics, and computational data mining.

How Visualization Applied in End user services 


Moreover, as data visualization has been more broadly applied to end-user services, interaction and experiential qualities need to be more critically considered.




Those qualities of data application do not only rely on the usability of data perception or task-based navigation but also build up on aesthetics that affords engaging and exploratory data navigation.




The latter have been remained as a less investigated area than the former due to the strong disciplinary tradition of data visualization in computer science and cognitive science.



Furthermore, visualizations are presented, used, and shared in diverse contexts from science labs to online journalism sites, to personal mobile devices.




This means that data visualization has become a truly interdisciplinary field of research and practice by weaving informatics, programming, graphic design, and even media art

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