Self Learning of Data Science for Free: Skill Development for Data Science Jobs
The amount of data currently generated by the various activities of the society has never been so big, and is being generated in an ever increasing speed. This trend is being seen by industries as a way of obtaining advantage over their competitors if one business is able to make sense of the information contained in the data reasonably quicker, it will be able to get more costumers, increase the revenue per customer, optimise its operation, and reduce its costs. Big Data analytics is still a challenging and time demanding task that requires expensive software, large computational infrastructure, and effort. Data is the new basis of competitive advantage. Enterprises that use data and sophisticated analytics turn insight into innovation, creating efficient new business processes, informing strategic decision making and outpacing their peers on a variety of fronts. Successful data scientists come from a number of different disciplines: biostatistics, econometrics, engineering, computer science, physics, applied mathematics, statistics, machine learning, and other interrelated disciplines. Experience of applying the scientific method to many disciplines and areas of research will prove fruitful in the field of data science. This book is a very basic introduction to data science. It is designed particularly for the beginners having the aptitude to learn and pursue careers in the emerging Data Science. The main emphasis of this book to help students think about the world in data science terms and learn taking advantage of free online web resources. While some elementary data science skills will be appraised, the emphasis is on skill development through self learning. Because skills are a must for data science. Data science, as practiced today, arises out of the "big data/cloud computing" world and complexity science. This means data science is an advanced discipline, requiring proficiency in parallel processing, map-reduce, computing, petabyte-sized noSQL databases, machine learning, advanced statistics and complexity science. I believe that data science is as much about mindset as it is about the skillful use of tools. Thus I want the students early in their careers to start thinking holistically about data science and related tools and techniques. There are many concepts and skills that a practical data scientist needs to know besides the fundamental principles of data science. These skills and concepts will be discussed in order to take advantage of free online data Science tutotials, courses, bootcamps, videos, blogposts, podcasts etc. This book 'Self Learning of Data Science for Free' is perfect for aspiring or current data scientists to learn from the best. It s a reference book packed full of strategies, suggestions and recipes to launch and grow your own data science career.
1123458506
Self Learning of Data Science for Free: Skill Development for Data Science Jobs
The amount of data currently generated by the various activities of the society has never been so big, and is being generated in an ever increasing speed. This trend is being seen by industries as a way of obtaining advantage over their competitors if one business is able to make sense of the information contained in the data reasonably quicker, it will be able to get more costumers, increase the revenue per customer, optimise its operation, and reduce its costs. Big Data analytics is still a challenging and time demanding task that requires expensive software, large computational infrastructure, and effort. Data is the new basis of competitive advantage. Enterprises that use data and sophisticated analytics turn insight into innovation, creating efficient new business processes, informing strategic decision making and outpacing their peers on a variety of fronts. Successful data scientists come from a number of different disciplines: biostatistics, econometrics, engineering, computer science, physics, applied mathematics, statistics, machine learning, and other interrelated disciplines. Experience of applying the scientific method to many disciplines and areas of research will prove fruitful in the field of data science. This book is a very basic introduction to data science. It is designed particularly for the beginners having the aptitude to learn and pursue careers in the emerging Data Science. The main emphasis of this book to help students think about the world in data science terms and learn taking advantage of free online web resources. While some elementary data science skills will be appraised, the emphasis is on skill development through self learning. Because skills are a must for data science. Data science, as practiced today, arises out of the "big data/cloud computing" world and complexity science. This means data science is an advanced discipline, requiring proficiency in parallel processing, map-reduce, computing, petabyte-sized noSQL databases, machine learning, advanced statistics and complexity science. I believe that data science is as much about mindset as it is about the skillful use of tools. Thus I want the students early in their careers to start thinking holistically about data science and related tools and techniques. There are many concepts and skills that a practical data scientist needs to know besides the fundamental principles of data science. These skills and concepts will be discussed in order to take advantage of free online data Science tutotials, courses, bootcamps, videos, blogposts, podcasts etc. This book 'Self Learning of Data Science for Free' is perfect for aspiring or current data scientists to learn from the best. It s a reference book packed full of strategies, suggestions and recipes to launch and grow your own data science career.
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Self Learning of Data Science for Free: Skill Development for Data Science Jobs
104
Self Learning of Data Science for Free: Skill Development for Data Science Jobs
104Paperback
$10.00
10.0
In Stock
Product Details
| ISBN-13: | 9781530150717 |
|---|---|
| Publisher: | CreateSpace Publishing |
| Publication date: | 02/20/2016 |
| Series: | Data Science , #1 |
| Pages: | 104 |
| Product dimensions: | 5.98(w) x 9.02(h) x 0.22(d) |
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