With an increasingly digitized world, we are at an age where data sets are larger than ever before in history. Naturally, it has become important to find ways to analyze these large data sets to provide meaningful information and insights, which can help businesses make better and more informed decisions.
Data science is a term used to describe the process of sifting through these large data sets and finding patterns, trends, and associations among them. This can be done via statistical or machine learning methods for prediction and forecasting purposes. Oftentimes, this is done in conjunction with extracting information from data using programming languages like Python and R.
If you want to learn the ins and outs of this promising field, you're in luck! We’ve compiled 5 great books you can pick up and read at your own pace during your commutes or coffee breaks.
5 Handy Books That Will Help You Upskill in the Field of Data Science
Python for Data Analysis by Wes McKinney
Data science is one of the hottest professions on the planet, and Python is one of the hottest programming languages out there. Why? Because Python makes data science so easy that even a monkey could do it. OK, maybe that's an exaggeration!
Python for Data Analysis presents a concise, focused introduction to the fun but challenging world of data analysis. With this hands-on guide, you'll learn the essentials of NumPy, Pandas, and IPython—three powerful open-source tools used by professional data scientists. Make no mistake, if you can learn to program in Python, you will have access to an endless stream of fun and lucrative jobs for decades to come.
Python Data Science Handbook by Jake VanderPlas
Data science is the key to understanding and predicting future trends, but many of its methods are complex and hard to access outside universities and research labs. The Python Data Science Handbook brings together the most powerful Python tools, libraries, and techniques for visualizing, organizing, analyzing, and understanding data. Written for developers new to data science, this book introduces you to using NumPy arrays and Pandas for high-performance data analysis; Matplotlib for creating informative visualizations; scikit-learn for machine learning; PySpark for MapReduce; NLTK for natural language processing; GraphLab Create for graph analytics; Scikit Flow for workflow automation; and NumExpr and Scrapy for web scraping.
Data Science from Scratch by Joel Grus
Data Science from Scratch is a self-paced introductory data science book whose content is valuable for many different audiences, regardless of background or motivation. You'll start by learning the basics of Python and data analysis, then dive deep into machine learning methods commonly used in the industry. With no prior knowledge of programming is required, this book teaches the entire data science pipeline from scratch, enabling you to write code, wrangle data, build a model, and explore its performance.
Hands on Machine Learning with Scikit-Learn, Keras & Tensorflow by Aurellen Geron
Hands-On Machine Learning with Scikit-Learn, Keras and Tensorflow provides all the tools needed to successfully complete a project using machine learning. This book requires a basic understanding of computer science and Python, but no prior knowledge of machine learning or deep learning is needed. When you’re finished with the book, you’ll have a newfound understanding of data science and a whole new set of possibilities in the field to explore.
Read Also: What do data scientists do?
Practical Statistics for Data Scientists by Peter Bruce, Andrew Bruce & Peter Gedeck
Practical Statistics for Data Scientists is the must-have, one-stop guide to the techniques used by working data scientists in the real world. It's a great textbook for those learning data science on their own, but also provides in-depth coverage of statistical concepts for those who are already familiar with the subject and are looking for practical ways to implement them in data science projects.
Practical Statistics for Data Scientists presents the art of data science through its design, execution, and interpretation. Infused with real-world examples, this guide offers a problem-solution approach and is ideal for data scientists who have to interact with other engineers.
Alternative Ways to Advance Your Data Science Skills
While books are a great way to amass new knowledge, they’re not for everyone. If you’re someone who prefers a more hands-on approach, Xccelerate’s bootcamps are the way to go!
Our Full-time Data Science & Machine Learning Bootcamp, Part-time Data Science & Machine Learning Course, and Part-time Introduction to Python Course will help you build the skill-set necessary to become a successful data scientist. Towards the end of the course, students will go through an insight session which comprises a rapid-fire pitching contest where they will have to showcase their knowledge and skills in front of a panel of industry leaders. Simply read this student success story to see the amazing results.
When you’re ready to start the search for your dream job in data science, make sure to brush up your skills on your resume, familiarize yourself with the frequently asked interview questions, and walk into the interview room like the job is already yours!
Career Paths
Career pathways include; Data Scientist, Data Analyst, Data Engineer, Machine Learning Developer. Explore our job ticker to learn about Hong Kong's Data Science industry and make career decisions based on actual data! To learn more about relevant job openings in Hong Kong, check out the first-ever tech job search portal - Xccelerate Jobs.
