Data is everywhere. The amount of data generated today across different mediums is manifold compared to what it was a decade ago. The shifting technological trends in parallel have ensured that more and more companies are subscribing to the idea of taking smarter decisions that are data driven.
To this end, they look to hire who we call now ‘ the data science professionals’. They serve various roles but ultimately work toward a common goal for the organization. These roles vary in expertise and are many, like, data scientist, data engineer, data analyst, data architect, and more…
Anybody, really. There is a lot of hype surrounding the field and it is obviously due to the heavy demand and the high salary of data scientists. This has led to a lot of people considering a career as a data science professional. But not all are cut out for it.
There are skills of the data scientists that one learns and there are some that’s just you. One such skill is the aptitude with data. You can learn to work with the data, but to handle such huge amounts of data does require special affinity towards working with gobs of data that don't make sense.
If you believe you can work with it, then rest everything can be learnt and honed to perfection. Once you have learnt the skills required, then`you can get a job as data science professionals depending on your core skills. Here’s how to become a data scientist.
You can take classes in data science online courses or from MOOCS, just browse and collect information from the internet on data science. But a better and more streamlined approach will be to take a data science course offline in your city or online and then you need to supplement this knowledge with books. Information in the books are in order and you can go in order and with minimal distractions. All of these are not possible using the internet.
The Internet, without doubt, is the largest source of data available, some of which is information. But there is no proper sourcing and citations and many times, we get unauthenticated information floating around, which if you end up learning is not good for you. Other than this, use the data science roadmap to figure out what skills you need to learn.
Books are written by field experts after painstaking research and there is always an information trail to backup the claims made by a book. The authors stake their reputation on their work. Also, the books have the info you seek and not much more, so there are less chances of diversion from the topic you are studying.
Additionally, you will be spending maximum time glued to your computer or device. Books will let you have a change in pace and scene. Books are the best source of structured information even in the age of the internet. So it is advisable that every data scientist has his own bookshelf of resources. Learn More: Differences between Data science and Artificial Intelligence.
In the upcoming section, we’ll see some of the most important literature that every beginner and intermediate data science should have read. These books can be used for self-paced learning of data science.
1.Think Stats: probability and statistics for programmers by Allen B Downey A standard book for learning data science, it’s a part of every data scientists’ collection. It explains concepts and is a great choice for those who are used to coding in Python as it gives working examples in python.
2. Introduction to statistical learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani It is a good option for covering basics of statistics and also for machine learning. This is like an all time classic book for statistics. It is good for beginners and intermediates. This book also has concepts explained and tried out in the popular data science language R.
3. Introduction to probability by J. Laurie Snell and Charles Miller Grinstead It is the classical resource for probability for every graduate student. This is a great resource for a novice who is just getting started in probability- with this book. It’s that simple and great.
4. Artificial Intelligence in practice by Bernard Marr It is a great resource for those looking to learn and grow their expertise in using data science for business intelligence. You can get better ideas and improve your data analytics strategies. It will serve you well if you are looking for practical use of AI in enhancing data science.
5. Deep learning with python by Franchois Chollet This is a brilliant work that is apt for learning concepts of machine learning and deep learning side-by-side with practical coding. It is taught using Keras library. So it is the best for learning ML and DL theory with coding.
6. Foundations of statistical natural language processing by Christopher Manning and Hinrich Schütze. It is an excellent resource to add to your library. It is good for beginners and intermediates. It is a comprehensive guide on everything natural Language Processing. It will give you a very good mathematics and linguistics foundation.
7. Programming computer vision with python by Jan Erik Solem This is a very hands-on book. You learn to use python for your projects. It covers stereo imaging, 3D reconstruction, AR and other heavy-duty topics. It is a good choice for all enthusiasts and it is helpful to have coding knowledge of python. 8. The Master Algorithm by Pedro Domingos The master algorithm is a good choice of book to learn about the impact and use of AI across various fields. Remember that this is not a technical book on learning AI. It is about the main question- Will we succeed in creating one algorithm that will solve all our purpose?
9. Fluent Python: clear, concise, and effective programming by Luciano Romalho It is a typical programming language book. It teaches how to use and code in python. It gives examples and also touches on a few libraries that will help you code better.
10. Mastering python for data science by Samir Madhavan As the name suggests, this book is for learning programing with Python from a data science angle. This is in contrast with other programming language resources, where you learn the language to code and then you figure out more and more applications for it. Here, you learn it to use for data science, exclusively.
11. R for everyone Jared P. Lander As the name indicates, this book is on learning the programming language R from scratch. It is for new coders and it makes a good reference guide for those well versed in R. It is really helpful for people who are from the non-statistical background.
12. R for data science by Garrett Grolemund and Hadley Wickham R for data science is just what it says it is. It is a book written exclusively for the use of R in data science. Here is why you should choose R for your data science project. This book gets you the nitty-gritty of coding in R for data science operations.
13. Python for Data Analysis: Data Wrangling With Pandas, NumPy and IPython” by Wes McKinney This is NOT for novices. Once you wet your feet in coding with python, and you start loving the work you do with it, that’s the time you crack open this book. The versatile python language is loved by data scientists. In this book, you go in-depth in coding and using various tools for data analysis with python.
14. Storytelling With Data: A Data Visualization Guide for Business Professionals by Cole Nussbaumer Knaflic This book is a must have if you are looking to whip up intriguing data visualization results to present. It is for data visualization enthusiasts. It explains how to build an interesting narrative to explain to your audience.
15. Doing Data Science: Straight Talk from the Frontline” by Cathy O’Neil and Rachel Schutt It’s a superb book for those looking for an introduction to the field of data science. It covers the topics broadly and also clears up many of the hypes surrounding the data science field. It is made up in an easy to learn format and is a good book to read for a up and coming data scientist.
16. Data Mining and Analysis: Fundamental Concepts and Algorithms by Mohammed J. Zaki & Wagner Meria Jr., It is a book for data mining and data analysis. It concentrated on the basics.It also touches on cutting edge data mining topics and gives detailed explanations on how to use pattern mining and exploratory analysis.
17. Modeling With Data by Ben Klemens This is a pretty straightforward book and the name itself suggests why you should have one in your library. It has statistical methods that work with data science and gives you extremely detailed description on how to implement the data-driven models.
19. Python for Informatics: Exploring Information by Dr. Charles R Severance, This book introduces students to learning programming and computation. The book concentrates on the programing aspects with a focus on exploring data. It also teaches us to use python as the language to explore.
20. Hadoop, the Definitive Guide: Storage and Analysis at an Internet Level by Tom White It is one of the best resources for Hadoop and on processing language. The readers can learn the basics of hadoop, its wide usages and how to build scalable systems from data.
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