The Data Science Universe

Data science is an exciting discipline that allows you to transform raw data into understanding, insight, and knowledge. Data science is a vast field, and there’s no way you can master it all by reading a single book. This book aims to give you a solid foundation in the most important tools and enough knowledge to find the resources to learn more when necessary. Our model of the steps of a typical data science project looks something like Figure 1.

A diagram displaying the data science cycle: business understanding -> data mining -> data cleaning -> data exploration -> feature engineering -> predictive model -> visualisation
Figure 1: Understanding the Data Science Lifecycle. Image taken from https://www.sudeep.co/data-science/2018/02/09/Understanding-the-Data-Science-Lifecycle.html

How this book is organized

Below is a list of items we are going to cover in this book:

  1. Data Science and R

  2. Data Exploration and Visualization

  3. Supervised Learning

  4. Unsupervised Learning

  5. Boosting and Random Forest

  6. Natural Language Processing

  7. Image Processing/Computer Vision

  8. Reinforcement Learning

  9. Big Data & Cloud Computing

Why Learning data science?

As more and more businesses move towards digitalization, there is a growing demand for professionals who can analyze and make sense of the vast amounts of data that are being generated. This has created a significant shortage of skilled Data Scientists, making it a highly sought-after and well-compensated profession.

Another point is that, Data Science is a highly interdisciplinary field that combines knowledge and techniques from statistics, computer science, and domain-specific areas. This means that learning Data Science can enhance your critical thinking skills, improve your ability to solve complex problems, and provide you with a unique set of skills that are highly valued in the job market.

The Role of R

R is a programming language that is widely used in the field of Data Science. Its role in Data Science is multifaceted and can be summarized as follows:

  • Data Wrangling: R has a powerful set of libraries that allow you to manipulate and transform data, which is a critical step in any Data Science project.

  • Statistical Analysis: R has a rich set of statistical libraries that allow you to perform a wide range of statistical analyses, including hypothesis testing, regression analysis, and time series analysis.

  • Data Visualization: R has an extensive set of libraries for creating high-quality data visualizations, such as plots, charts, and graphs, that enable you to communicate insights effectively.

  • Machine Learning: R has a comprehensive set of libraries for building and deploying machine learning models, such as decision trees, random forests, and neural networks.

  • Reproducibility: R provides a framework for creating reproducible data analyses, which is essential for collaborating with others and ensuring that your work can be verified and replicated.

Overall, R plays a critical role in the Data Science process by providing a powerful and flexible toolset for manipulating, analyzing, and visualizing data, building and deploying machine learning models, and ensuring reproducibility.