A collection of post related to my upcoming book “Data Science and Analytics with Python”Take a look and enjoy.
There you go, the first checkpoint is completed: I have officially submitted the completed version of "Advanced Data Science and Analytics with Python".
The book has been some time in the making (and in the thinking...). It is a follow up from my previous book, imaginatively called "Data Science and Analytics with Python" . The book covers aspects that were necessarily left out in the previous volume; however, the readers in mind are still technical people interested in moving into the data science and analytics world. I have tried to keep the same tone as in the first book, peppering the pages with some bits and bobs of popular culture, science fiction and indeed Monty Python puns.
Advanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow up from the topics discuss in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and others. The development is also supported by the use of frameworks such as Keras, TensorFlow and Core ML, as well as Swift for the development of iOS and MacOS applications.
The book can be read independently form the previous volume and each of the chapters in this volume is sufficiently independent from the others proving flexibiity for the reader. Each of the topics adressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book
Time series analysis, natural language processing, topic modelling, social network analysis, neural networds and deep learning are comprehensively covrered in the book. The book discusses the need to develop data products and tackles the subject of bringing models to their intended audiences. In this case literally to the users fingertips in the form of an iPhone app.
While the book is still in the oven, you may want to take a look at the first volume. You can get your copy here:
Furthermore you can see my Author profile here.”Read
It has been a few months of writing, testing, re-writing and starting again, and I am pleased to say that the first complete draft of "Advanced Data Science and Analytics with Python" is ready. Last chapter is done and starting revisions now. Yay!”Read
Using the time wisely during the Bank Holiday weekend. As my dad would say, "resting while making bricks"... Currently reviewing/editing/correcting Chapter 3 of "Advanced Data Science and Analytics with Python". Yes, that is volume 2 of "Data Science and Analytics with Python".
On my way back to London and making the most of the time in the train to work on my Data Science and Analytics Vol 2 book. Working with #StarWars data to explain Social Network Analysis #datascience #geek
Earlier this week I received this picture of the team in New York. As you can see they have recently all received a copy of my "Data Science and Analytics with Python" book.
Another "Data Science and Analytics with Python" Delivered. Thanks for sharing the picture Dave Groves.”Read
I’m very pleased to see that my “Data Science and Analytics” book is arriving to the hands of readers.
Here’s a picture that my colleague and friend Rob Hickling sent earlier today:
"Data Science and Analytics with Python" was published yesterday and now it is already appearing as a suggested book for related titles.
You can find it with the link above or in Amazon here.
Very pleased to see that finally the publication of my "Data Science and Analytics with Python" book has arrived.
You can get your copy here:
Furthermore you can see my Author profile here.”Read
It has been a long road, one filled with unicorns and Jackalopes, decision trees and random forests, variance and bias, cats and dogs, and targets and features.
Well over a year ago, the idea of writing another book seemed like a farfetched proposition. Writing the book came about from the work that I have been doing in the area as well as from discussions with my colleagues and students, including also practitioners and beneficiaries of data science and analytics.
It is my sincere hope that the book is useful to those coming afresh to this new field as well as to those more seasoned data scientists.
This afternoon I had the pleasure of approving the final version of the book that will be sent to the printers in the next few days.
Well, I am very pleased to show you the cover that will be used for "Data Science and Analytics with Python" book. Not long to publication day!”Read
I have now received comments and corrections for the proofreading of my “Data Science and Analytics with Python” book.
Two weeks and counting to return corrections and comments back to the editor and project manager.
I am very pleased to tell you about some news I received a couple of weeks ago from my editor: my book "Data Science and Analytics with Python" has been transferred to the production department so that they can begin the publication process!
The book has been assigned a Project Editor who will handle the proofreading and handle all aspects of the production process. This was after clearing the review process I told you about some time ago. The review was lengthy but it was very positive and the comments of the reviewers have definitely improved the manuscript.
As a result of the review, the table of contents has changed a bit since the last update I posted. Here is the revised table:
- The Trials and Tribulations of a Data Scientist
- Python: For Something Completely Different!
- The Machine that Goes “Ping”: Machine Learning and Pattern Recognition
- The Relationship Conundrum: Regression
- Jackalopes and Hares: Clustering
- Unicorns and Horses: Classification
- Decisions, Decisions: Hierarchical Clustering, Decision Trees and Ensemble Techniques
- Less is More: Dimensionality Reduction
- Kernel Trick Under the Sleeve: Support Vector Machines
Each of the chapters is intended to be sufficiently self-contained. There are some occasions where reference to other sections is needed, and I am confident that it is a good thing for the reader. Chapter 1 is effectively a discussion of what data science and analytics are, paying particular attention to the data exploration process and munging. It also offers my perspective as to what skills and roles are required to get a successful data science function.
Chapter 2 is a quick reminder of some of the most important features of Python. We then move into the core of machine learning concepts that are used in the rest of the book. Chapter 4 covers regression from ordinary least squares to LASSO and ridge regression. Chapter 5 covers clustering (k-means for example) and Chapter 6 classification algorithms such as Logistic Regression and Naïve Bayes.
In Chapter 7 we introduce the use of hierarchical clustering, decision trees and talk about ensemble techniques such as bagging and boosting.
Dimensionality reduction techniques such as Principal Component Analysis are discussed in Chapter 8 and Chapter 9 covers the support vector machine algorithm and the all important Kernel trick in applications such as regression and classification.
The book contains 55 figures and 18 tables, plus plenty of bits and pieces of Python code to play with.
I guess I will have to sit and wait for the proofreading to be completed and then start the arduous process of going through the comments and suggestions. As ever I will keep you posted as how things go.
Ah! By the way, I will start a mailing list to tell people when the book is ready, so if you are interested, please let me know!
Keep in touch!
PS. The table of contents is also now available at CRC Press here.
A few weeks ago I was invited by General Assembly to give a short intro to Data Science to a group of interested (and interesting) students. They all had different backgrounds, but they all shared an interest for technology and related subjects.
While I was explaining some of the differences between supervised and unsupervised machine learning, I used my example of an alien life trying to cluster (and eventually classify) cats and dogs. If you are interested to know more about this, you will probably have to wait for the publication of my "Data Science and Analytics with Python" book.. I digress...
So, Ed Shipley - one of the admissions managers at GA London - asked me and the students if we had seen the videos that Facebook had produced to explain machine learning... He was reminded of them as they use an example about a machine distinguishing between dogs and cars... (see what they did there?...). If you haven't seen the videos, here you go:
Intro to AI
Convolutional Neural Nets”Read