Random thoughts about random subjects… From science to literature and between manga and watercolours, passing by data science and rugby; including film, physics and fiction, programming, pictures and puns.
As you know I am writing a new book. This time it is a book about statistics and data visualisation using Python as the main language to analyse data. It was thinking that I was a bit behind with my plan for the book, but I managed to surprise myself by being bang on time completing the first chapter.
This is the introductory chapter where we cover some background on the importance of statistics, a bit of history and the personalities behind some concepts widely used in stats and data visualisation. We then cover some background in formulating questions to be answered with data and how to communicate our results.
Hello again this is a video I recorded for my publisher about my book “Advanced Data Science and Analytics with Python”. This is a video I made for my publisher about my book “Data Science and Analytics with Python”. You can get the book hereand more about the book here.
This companion to “Data Science and Analytics with Python” is the result of arguments with myself about writing something to cover a few of the areas that were not included in that first volume, largely due to space/time constraints. Like the previous book, this one exists thanks to the discussions, stand-ups, brainstorms and eventual implementations of algorithms and data science projects carried out with many colleagues and friends.
As the title suggests, this book continues to use Python as a tool to train, test and implement machine learning models and algorithms. The book is aimed at data scientists who would like to continue developing their skills and apply them in business and academic settings.
The subjects discussed in this book are complementary and a follow-up to the ones covered in Volume 1. The intended audience for this book is still composed of data analysts and early-career data scientists with some experience in programming and with a background in statistical modelling. In this case, however, the expectation is that they have already covered some areas of machine learning and data analytics. The subjects discussed in this book are complementary and a follow-up to the topics discussed in “Data Science and Analytics with Python”. Although there are some references to the previous book, this volume is written to be read independently.
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. The aim is still to focus on showing the concepts and ideas behind popular algorithms and their use.
In summary, “Advanced Data Science and Analytics with Python” presents each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The material covered includes machine learning and pattern recognition algorithms including: Time series analysis, natural language processing, topic modelling, social network analysis, neural networks and deep learning. The book discusses the need to develop data products and addresses the subject of bringing models to their intended audiences – in this case, literally to the users’ fingertips in the form of an iPhone app.
I hope you enjoy it and if you want to know more about my other books, please check the related videos here:
This is a video I made for my publisher about my book “Data Science and Analytics with Python”. You can get the book hereand more about the book here.
The book provides an introduction to some of the most used algorithms in data science and analytics. This book is the result of very interesting discussions, debates and dialogues with a large number of people at various levels of seniority, working at startups as well as long-established businesses, and in a variety of industries, from science to media to finance.
“Data Science and Analytics with Python” is intended to be a companion to data analysts and budding data scientists that have some working experience with both programming and statistical modelling, but who have not necessarily delved into the wonders of data analytics and machine learning. The book uses Python as a tool to implement and exploit some of the most common algorithms used in data science and data analytics today.
Python is a popular and versatile scripting and object-oriented language, it is easy to use and has a large active community of developers and enthusiasts, not to mention the richness oall of this helped by the versatility of the iPython/Jupyter Notebook.
In the book I address the balance between the knowledge required by a data scientist sucha as mathematics and computer science, with the need for a good business background. To tackle the prevailing image of a unicorn data scientist, I am convinced that the use of a new symbol is needed. And a silly one at that! There is an allegory I usually propose to colleagues and those that talk about the data science Unicorn. It seems to me to be a more appropriate one than the existing image: It is still another mythical creature, less common perhaps than the unicorn, but more importantly with some faint fact about its actual existence: a Jackalope. You will have to read the book to find out more!
The main purpose of the book is to present the reader with some of the main concepts used in data science and analytics using tools developed in Python such as Scikit-learn, Pandas, Numpy and others. The book is intended to be a bridge to the data science and analytics world for programmers and developers, as well as graduates in scientific areas such as mathematics, physics, computational biology and engineering, to name a few.
The material covered includes machine learning and pattern recognition, various regression techniques, classification algorithms, decision tree and hierarchical clustering, and dimensionality reduction. Though this text is not recommended for those just getting started with computer programming,
There are a number of topics that were not covered in this book. If you are interested in more advanced topics take a look at my book called “Advanced Data Science and Analytics with Python”. There is a follow up video for that one! Keep en eye out for that!
Related Content: Please take a look at other videos about my books:
This is a video I made for my publisher about my book “Essential MATLAB and Octave”. You can get the book here and more about the book here.
The book is a primer for programming in Matlab and Octave within the context of numerical simulations for a variety of applications. Matlab and Octave are powerful programming languages widely used by scientists and engineers. They provide excellent capabilities for data analysis, visualisation and more.
The book started as lecture notes for a course on Computational Physics – later turning into a wider encompassing syllabus covering aspects of computational finance, optimisation and even biology and economics
The aim of the book is to learn and apply programming in Matlab and octave using straightforward explanations and examples from different areas in mathematics, engineering, finance, and physics.
Essential MATLAB and Octave explains how MATLAB and Octave are powerful tools applicable to a variety of problems. This text provides an introduction that reveals basic structures and syntax, demonstrates the use of functions and procedures, outlines availability in various platforms, and highlights the most important elements for both programs.
The book can be considered as a companion for programmers (new and experienced) that require the use of computers to solve numerical problems.
Code is presented in individual boxes and explanations are added as margin notes. Although both Matlab and Octave share a large number of features, they are not strictly the same. In cases where code is specific to one of the languages the margin notes provide clarity.
This text requires no prior knowledge and it is self-contained, allowing the reader to use the material whenever needed rather than follow a particular order.
Compatible with both languages, the book material incorporates commands and structures that allow the reader to gain a greater awareness of MATLAB and Octave, write their own code, and implement their scripts and programs within a variety of applicable fields.
It is always made clear when particular examples apply only to MATLAB or only to Octave, allowing the book to be used flexibly depending on readers’ requirements.
I have been using GSuite in the last year or so at work. In general it seem to be fine, good usage of the email capabilities provided by Gmail and the storage, together with shared drives, and things like that are fine.
Calendar is ok and it does the work, however there was a very irritating thing when being invited to see other colleague’s calendars and/or subscriptions to them. On the one hand it is useful to see calendar availability, but I don’t want to see all of those calendars on my mobile device, or the Calendar app on my Macbook all the time.
A quick solution would be to “uncheck” the unwanted calendars on your device, but… The problem is, when you uncheck those calendars, they’re still there. You may not see them, but boy, you do continue getting reminders, notifications, alerts – and most (all?) of the time they are not even for me to act on!
So if you require to remove these extra calendar, bit still access then via the web and Google apps then do the following:
I have been considering moving a considerable photo collection I have amassed for a few years now after getting my first digital camera. I used to take a lot of pictures before that with a beloved Cannon SLR that belonged to my father. Sadly that camera got stolen in a holiday in Cancun… but that is a story for another time.
Anyway, I used to use Picasa to organise my photos into albums and upload or share some with friends and family. Picasa was superseded by Google Photos and I never quite liked losing some control on where my photos went.
I have been an iOS user of Apple Photos — I like the simplicity of taking a picture and it being part of an album that I keep in my phone until I clean the album… I did try using the Mac version, but as I said I never liked just getting a soup of pictures. I wanted to keep them in the album/folder hierarchy I curated myself. It is until now that I have found a way for Apple Photos to respect my hierarchy. Here is what you need to do:
Find the place where your pictures are organised folders and drag the top folder onto the Photos App icon in the Dock. It does not matter if the App is running
NOTE: Do not drag it to the Photo App window. If you do, the applications behaves in a different way and you will end up with a soup of photographs.
At the top of the window you will see a checkbox that reads “Keep Folder Organisation” on the top right (see the screenshot above)
Click the blue button, “Import All New Items”
Et voilà! Your imported photos will show up in an organised folder in the sidebar.