You will require Python and R installed to run most of the code throughout this book, and you can find the installation code at the following link: https://github.com/PacktPublishing/Hands-On-Data-Science-for-Marketing/tree/master/Chapter01.

As the amount of data available and gathered increases exponentially every year and access to such valuable datasets becomes easier, data science and machine learning have become an integral part of marketing. The applications of data science in marketing range from building insightful reports and dashboards to utilizing complicated machine learning algorithms to predict customer behaviors or engage customers with the products and contents. The trends in marketing in recent years have been toward more data-driven target marketing. We will discuss some of the trends we see in the marketing industry:

• Rising importance of digital marketing: As people spend more time online than ever before, the importance and effectiveness of digital marketing have been rising with time. Lots of marketing activities are now happening on digital channels, such as search engines...

We have discussed the trends in marketing and how the trend has been toward more data-driven and quantitative marketing, often using data science and machine learning. There are various ways to apply data science and machine learning in the marketing industry and it will be beneficial for us to discuss the typical tasks and usage of data science and machine learning.

In this section, we will cover the basics of machine learning, the different types of learning algorithms, and, typical data science workflow and process.

As we work through the exercises and projects in the upcoming chapters, there are mainly three different...

Now that we have discussed some of the basics of data science and its applications to marketing, let's start getting our development environments ready for the upcoming chapters and projects. For those of you who will be using the R language for the exercises, you can skip this section and move to the Setting up the R environment section. For those of you who are planning to use the Python language for the exercises, it will be beneficial for you to follow these steps to install all the required Python packages and get your Python environment ready, even if you are already familiar with Python.

For data science and machine learning tasks in this book...

For those of you who are planning to use the R language for the upcoming exercises and projects, we will discuss how to get your R environment ready for data science and machine learning tasks in this book. We will start by installing R and RStudio and then build a simple logistic regression model using R to familiarize ourselves with R for data science.

Along with Python, R is one of the most frequently used languages for data science and machine learning. The fact that it is very easy to use, and that there is a large number of R libraries for machine learning, attracts many data scientists. In order to use this language, you will need to download it from the following...

In this chapter, we discussed the overall trends in marketing and learned the rising importance of data science and machine learning in the marketing industry. As the amount of data increases and as we observe the benefits of utilizing data science and machine learning for marketing, companies of all sizes are investing in building more data-driven and quantitative marketing strategies.

We also learned different types of analysis methods, especially the three types of analysis that we will be using frequently in this book—descriptive, explanatory, and predictive —and different use cases of these analyses. In this chapter, we covered different types of machine learning algorithms, as well as the typical workflow in data science. Lastly, we spent some time setting up our development environments in Python and R and testing our environment setup by building...