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Top 10 Data Science Books For Beginners And Advanced Learners

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Data science is a domain of study that deals with vast volumes of noisy data. It combines programming skills, statistics and mathematics to make sense of this data. These clean, meaningful insights are then implemented with the help of algorithms and processes across a range of applications. Data science jobs can earn you a handsome salary and plenty of perks in India. Plus, you do not need high-end degrees or advanced knowledge to kickstart your data scientist career.

Start with enrolling in GeekLurn’s Data Science Architect Program. This 2-year course comes with 100% placement and money-back guarantee. It builds expertise in Testing, Hadoop Development, Statistical Computing, Real Analytics and Administration, making you eligible for the role of a data analyst, data architect, data engineer, senior data scientist, research analyst and analytics manager. Candidates can also be eligible for a research project scholarship of up to ₹2 lakhs and 1.5 years of research project experience certification. Students have the option to pay the tuition fees via EMIs after placement.

Simultaneously, consider reading the following data science books for beginners to better understand the core concepts of this discipline.

Head First Statistics: A Brain Friendly Guide

This is one of the best data science books to start with. It is ideal for high school and college students to learn the basics of statistics. It helps you acquire a firm grasp of chi-square analysis, probability distributions and histograms. It also contains descriptive statistics – mean, mode, median and standard deviation, with plenty of graphics and pictures. A visually rich format helps in understanding sampling, regression and correlation without confusion. 

2. Introduction to Probability 

Clear explanations and real-life examples help build a strong foundation in probability. It offers a comprehensive overview of the theories, including:

  • Sample Spaces
  • Combinatorial Analysis
  • Fluctuations in Coin Tossing and Random Walks 
  • Types of Distributors
  • Combinations of Events
  • Markov Chains
  • Stochastic Process

3. Machine Learning Simplified 

This is one of the top data science books to read for developers, students, lecturers and anyone who wishes to learn about machine learning and its applications for business. You gain a “strong intuition” into the inner workings of difficult algorithms, methods and concepts. All topics, like data modeling, data preparation and python code implementations, are described in detail. The key focus is on supervised machine learning, which is part of GeekLurn’s course curriculum.

4. Big Data – A Revolution 

This is a New York bestseller and a ground-breaking data scientist book. It offers a clear outline of the actionable steps to create new business innovations and solutions. But you can also get an idea of the risks involved in the same. Make the most of the technical papers at the end, which are extremely helpful for beginners. The language is simple and conversational, making it easy for first-time learners. 

5. Data Science and Big Analytics 

The topics cover the methods, activities and tools that data scientists use. Learning is supported using instances that can be replicated using open-source software.

This Book Helps You To:

  • Deploy a structured lifecycle approach to data analytics problems
  • Learn how to narrate a compelling story to drive business action
  • Contribute to the data science field
  • Apply appropriate analytics techniques and tools to analyse Big Data 
  • Prepare for EMC proven data science certificate

The principles, practical applications and concepts apply to all industries. You can start analysing, discovering and visualising data in a meaningful manner.

6. Inflection Point 

This book does not offer technical knowledge. Instead, it offers plenty of useful information on technologies like mobility, infrastructure, IT, cloud and Big Data. Readers can learn how to transform operational IT into a business enabler and use it to ensure speed, focus and flexibility. You can also get an idea of how businesses work, with personal stories and experiences from enterprises for holistic knowledge.

7. Practical Statistics for Data Science

The best data science books are detailed and comprehensive, covering all the concepts needed to succeed in this field. In this book, topics are narrowed down to sampling distributions, descriptive statistics, hypothesis testing and prediction. You may also get a fair idea of Python and R, so that you can blend theoretical and practical concepts. Readers will learn how to use regression to estimate outcomes, statistical machine learning methods, the key classifications techniques, unsupervised learning and random sampling.

8. Business Analytics: The Science of Data-Driven Decision Making

This data analytics book has 17 chapters with descriptive, predictive and prescriptive components. Basic statistical concepts have been discussed, along with confidence interval, discrete and continuous random variables, clustering, linear programming, integer programming, stochastic models, Six Sigma and analysis of variance and correlation. The book also tells you about smart learning techniques that make high-level concepts easy.

9. Generative Deep Learning 

This is a practical book that offers crystal clear ideas of deep learning models like variational autoencoders, after which you proceed to cutting-edge algorithms in the field. It also covers generative adversarial networks and world models. There are plenty of tips and tricks that help you learn efficiently and creatively. The references help to start the data science journey from scratch with a practical approach and keep you motivated throughout. You will also learn why data science is the right choice. 

10. Data Mining Techniques

Besides GeekLurn’s data mining concepts, this book helps you gain a clear understanding of the field. You will learn about the types of data that can be mined and the patterns, with examples, predictive analysis, cluster analysis and correlations. The book introduces you to the fundamentals of data mining, like OLAP, data processing, classification and prediction, cluster analysis and time-series database. Numerous algorithms are presented in pseudo code to be applied in real projects.

The Bottom Line

There are thousands of data science books. It may leave you overwhelmed as to which ones are ideal for you. This list covers the top books from credible authors. Do check the depth, comprehensiveness, readability and applicability to make an informed choice. 

Neel is a Product Manager with an interest in Data Science, Machine Learning, Cloud Computing, DevOps, and Blockchain with expertise in Python, R, Java, Power BI and Data analytics.

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Neel Neeraj

Neel is a Product Manager with an interest in Data Science, Machine Learning, Cloud Computing, DevOps, and Blockchain with expertise in Python, R, Java, Power BI and Data analytics.
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