Bookands-on data science for marketing and sales

Then half of the book is dead weight the R chapters and vice versa Why not have Hands On Data Science for Marketing with Python and Hands On Data Science for Marketing with R I have no issues recommending this book to students in an introductory class as long as there s some adult supervision I d be slightly concerned that the exposition nonchalance might lead some practitioners into thinking data science is just a couple of one liners and boilerplate API calls Hands On Data Science for Marketing Improve your marketing strategies with machine learning using Python and R Optimize your marketing strategies through analytics and machine learning Regardless of company size.

Ebook hands-on data science for marketing and communications

But only requiere to do update with numpy Nice selection of datasets and methods Machine Learning methods good applied. Bookands-on data science for marketing pdf Besides that it s an excellent primer Hands On Data Science for Marketing Improve your marketing strategies with machine learning using Python and R Easily a book that could have been a five star contender unfortunately there are some shortcomings Overall the text is a fine but basic introduction to data science through applications of common marketing analytics to problems such as engagement conversion retention churn recommendations forecasting segmentation experimentation lifetime value etc What it does well is that it provides exposure to a wide range of applications of data science techniques to some pretty good datasets that somewhat replicate what you d see out in the wild The author also shows some data manipulation and aggregation techniques that I was not familiar with like the use of Groupers combined with timestamp resampling inside of group_by operations His notebooks are all neatly organized in the book s repo and the code is very readable From here on out the book is a bit of a letdown The most salient of its shortcomings involves the depth or lack thereof of its exposition when it comes to the technique or algorithm being discussed There are a couple of simple formulas here and there and very short explanations of what the algorithms are doing behind the scenes I guess that s ok if it is meant to be an intro and not a full fledged encyclopedia however I cannot overlook the sidestepping of common pitfalls and best practices when it comes to leveraging these techniques in other places For instance in chapter 6 collaborative filtering the author walks the reader through the application of user user and item item collaborative filtering using unary matrices and cosine distances without mention of assumptions made of the data you need a lot on the shortcoming of the technique collaborative filtering doesn t work well for new users or new items why you d want one vs the other user user CF is much computationally expensive than item item since one is likely to have users than items or why you d use cosine distance vs Pearson mean centering etc This is pretty much the case in each section. Epubands-on data science for marketing and communications There are some editing quibbles as well Chapters 5 7 and 8 are pretty much rehashed from earlier chapters so there s no reason why the material couldn t have been consolidated Also I wonder what the folks at Packt were thinking in packing essentially the same book twice into the same print one for R and one for Python If you are a Python user the adoption of data science and machine learning for marketing has been rising in the industry With this book you will learn to implement data science techniques to understand the drivers behind the successes and failures of marketing campaigns This book is a comprehensive guide to help you understand and predict customer behaviors and create effectively targeted and personalized marketing strategies This is a practical guide to performing simple to advanced tasks to extract hidden insights from the data and use them to make smart business decisions You will understand what drives sales and increases customer engagements for your products You will learn to implement machine learning to forecast which customers are likely to engage with the products and have high lifetime value This book will also show you how to use machine learning techniques to understand different customer segments and recommend the right products for each customer Apart from learning to gain insights into consumer behavior using exploratory analysis you will also learn the concept of A B testing and implement it using Python and R By the end of this book you will be experienced enough with various data science and machine learning techniques to run and manage successful marketing campaigns for your business If you are a marketing professional data scientist engineer or a student keen to learn how to apply data science to marketing this book is what you need It will be beneficial to have some basic knowledge of either Python or R to work through the examples This book will also be beneficial for beginners as it covers basic to advanced data science concepts and applications in marketing with real life examples Hands On Data Science for Marketing Improve your marketing strategies with machine learning using Python and R.

Ebookands-on data science for marketing and communications

Got moved to the marketing analytics section where I am interning at This book was recommended to me It helped ease me into the department, Hands-on data science for marketing books pdf The only problem is the lack of in depth explanation you need to look other resources to understand most difficult concepts like random forest