A collection of Data Science and Data Visualisation related posts, pics and thoughts. Take a look and enjoy.
I am reaching out as volume 2 of my data science book will be out for publication in May and my publisher has made it possible for me to offer 20% off. You can order the book here.
This follows from "Data Science and Analytics with Python" and both books are intended for practitioners in data science and data analytics in both academic and business environments.
The new book aims to present the reader with concepts in data science and analytics that were deemed to be more advanced or simply out of scope in the author's first book, and are used in data analytics using tools developed in Python such as SciKit Learn, Pandas, Numpy, etc. The use of Python is of particular benefit given its recent popularity in the data science community. The book is therefore a reference to be used by seasoned programmers and newcomers alike and the key benefit is the practical approach presented throughout the book
More information about the first book can be found here.Read me...
With all the changes that have taken place in the las couple of weeks, I was thinking of the support that we can provide to each other while keeping to the new ways of working around us. Working from home is nothing new for some, but not for many. Socialising is an important part of the human experience.
I therefore thought of putting an open invite for a virtual coffee to the data science/physics/maths community dealing with the new ways of working, business, mental health and general stuff:
The response was great and I promptly created a new page in this site dedicated to some information for the new Jackalope Data Science Community. The first call took place on March 26th, 6.30pm via Meet. There were about 12 attendees mainly from the UK, with some from Cyprus, the US and other places around the world.
It was great to see so many friends there and the chat ranged from how to distinguish between weekdays and weekends these days, to how we are coping with working from home and how companies and businesses are reacting. It was entertaining, and personally I found it very useful.
We are planning to get together again in a couple of weeks. If you are interested to join us and learn more the Jackalope Data Science Community, get in touch.Read me...
Well, this are the final corrections for my latest book "Advanced Data Science and Analytics with Python". Next stop publication!
Super excited to have received the proofread version of Advanced Data Science and Analytics with Python. They all seem to be very straightforward corrections: a few missing commas, some italics here and there and capitalisation bits and bobs.
I hope to be able to finish the corrections before my deadline for March 25th, and then enter the last phase before publication in May 2020.Read me...
I have received the latest information about the status of my book “Advanced Data Science and Analytics with Python”. This time reviewing the latest cover drafts for the book.
This is currently my favourite one.
Awaiting the proofreading comments, and I hope to update you about that soon.Read me...
If you are interested in #DataScience you surely have heard of #pandas and you would be pleased to hear that version 1.0 finally out. With better integration with bumpy and improvements with numba among others. Take a look!
— Read on www.anaconda.com/pandas-1-0-is-here/
It was great to invited to give the joint Physics Astronomy and Maths + Computer Science research seminar today at the University of Hertfordshire. I had a good opportunity to meet old colleagues and meet new faculty. There were also many students and they with many questions.
I was glad to hear they are thinking about offering more data science courses and even a dedicated programme. I would definitely be interested to hear more about that.Read me...
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 me...
It was a pleasure to come to the opening day of ODSC Europe 2019. This time round I was the first speaker of the first session, and it was very apt as the talk was effectively an introduction to Data Science.
The next 4 days will be very hectic for the attendees and it the quality is similar to the previous editions we are going to have a great time.Read me...
Last October I had the great opportunity to come and give a talk at the Facultad de Ciencias Políticas, UAEM, México. The main audience were students of the qualitative analysis methods course, but there were people also from informatics and systems engineering.
It was an opportunity to showcase some of the advances that natural language processing offers to social scientists interested in analysing discourse, from politics through to social interactions.
The talk covered a introduction and brief history of the field. We went through the different stages of the analysis, from reading the data, obtaining tokens and labelling their part of speech (POS) and then looking at syntactic and semantic analysis.
We finished the session with a couple of demos. One looking at speeches of Clinton and Trump during their presidential campaigns; the other one was a simple analysis of a novel in Spanish.
Thanks for the invite.
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 me...
I know there are a ton of posts out there covering this very topic. I am writing this post more for my out benefit, so that I have a reliable place to check the commands I need to add a new conda environment to my Jupyter and nteract IDEs.
First to create an environment that contains, say TensorFlow, Pillow, Keras and pandas we need to type the following in the command line:
$ conda create -n tensorflow_env tensorflow pillow keras pandas jupyter ipykernel nb_conda
Now, to add this to the list of available environments in either Jupyter or nteract, we type the following:
$ conda activate tensor_env $ python -m ipykernel install --name tensorflow_env $ conda deactivate
Et voilà, you should now see the environment in the dropdown menu!Read me...
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".