Useful Resources

I love to learn and to help others learn

As a starter blog to sharing some of what I know in data science I thought I’d start with the resources that help me day to day. This is by no means a complete list and I’ll probably update this list when I come across more useful titbits of information.


I’m going to start with meet-ups.

To anyone looking to get their foot in the door with data science I always recommend going along to meet-ups. Meet the people doing data science. Find out what the people doing data science are doing. Start picking up the language on how data scientists communicate and learn what they are communicating about.

I also recommend meet-ups for more experienced data scientist. Go find out what other data scientists are doing. Share your knowledge of data science. Practise presenting the data science work you are doing to smart people.

We’re lucky in Manchester to have a wealth of meet-ups for data scientists (perhaps too lucky). Below are the ones I try and attend regularly as well as the ones I know are good but I don’t have the time to attend as much as I would like.

  • MancML ( A quarterly event organised and sponsored by Octavia and Cathcart Associates. This meetup gets a permanent entry in my calendar as the talks are always really interesting. It’s a popular event so get your tickets early. It attracts some of the best data scientists in Manchester so always a good one to go along to for some smart discussions.

  • Manchester R ( Also a quarterly event, Manchester R is organised by Mango and normally has a mix of talks from university and industry R users presenting tips and tricks in R. I enjoy hearing about peoples personal projects in R - a recent talk was a “Battle of the bands” between Little Mix and Radiohead which was good fun.

  • Peak Ensemble This year we have been organising monthly meet-ups at Peak where we invite academics researching in areas of data science, statistics, computer science and mathematics to come and talk to the Manchester tech community about their work. These talks are normally more technical than you would find at other meetups.

  • R Ladies Manchester ( If you identify as a women and like R or are quite new to R then this is a great meetup that happens bi-monthly. Sessions have been a mix of tutorials, practicals and discussion sessions. If you like meetups but don’t like the normal pizza and beer affair then make sure and go to this one as it definitely has the best food.

  • Her+ Data MCR ( Her+ Data is a very supportive meetup for women that happens monthly. I’ve only managed to attend a couple of times but follow on Twitter. Women supporting women in a mix of events including presentations and panel discussions. It’s a meetup I really need to make more of an effort to attend as the support network in Manchester in invaluable.

  • PyData Manchester ( As I delve into my learnings in python I’ve stated to attend the odd pydata session. There are a lot to choose from with regular code nights, presentations and panel discussions. Despite the name it’s not just Python!

  • Data Science Festival ( A fairly new meetup in Manchester is the Data Science Festival. If I am honest it’s a bit hit or miss. I keep an eye out on who is speaking and will attend if there is anything I’m super keen to hear about.

A women in tech panel night

Introductions to data science tools

I’ve never formally trained as a “data scientist”. In fact anyone around a similar age to me or older will have come into data science from another analytical background, in my case maths and statistics.

In terms of an introduction to data science the best places to start are probably online courses such as those that you can find on Coursera.

Where possible I think a masters in data science seems like a good idea to get a good overall foundation for a data science career. If I were to choose a masters I’d go for the one in Lancaster (although I am biased) which has a 3 month summer project with links to industry, a good opportunity to get into a company for after your studies.

I always recommened the R for Data Science Book by Hadley Wickham for a good solid foundation in using R ( and recently I’ve been following the Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow book ( to develop my Python knowledge which so far I’ve found to be the best resource to really help excel my python training.


For more specific forecasting resources we’ve been using the Principals of Business Forecasting book by Keith Ord and Robert Fildes to train the data scientists at Peak on forecasting and I always recommend the Forecasting: Principles and Practice book ( which has become my bible.

R packages


A lot of the time on my commute I scroll through instagram but I’ll occasionally read blogs when I feel like I should do something more productive. Some of my favourite go to blogs are:

Nikolaos Kourentzes ( – Nikos writes blogs on forecasting in an easy to understand way. I particularly enjoyed his blog on the one number forecast.

Rob Hyndman - Hyndsight Blog ( - arguably the best name for a blog, Rob Hyndman’s blog is a super useful way of keeping up to date with recent developments in forecasting.

R-Bloggers ( a blog aggregator of content contributed by bloggers who write about R.

Partly what inspired me to start a data science blog was blogs from other Manchester data scientists -, and


I’m not a huge podcast listener, I find my mind wanders too much for me to really listen to a podcast. I normally find I need to listen to a podcast about 3 times to really catch the majority of it. That being said there are 2 podcasts which I will occasionally put on when I need a break from my repetitive Taylor Swift listening. These are:

  • PyData Manchester ( the same people who organise the PyData Manchester meet-ups also have a podcast. I like listening to these podcasts as many of the guests that have been on are people that I know from the local data science scene. You can hear me talk on episode 3 about forecasting.

  • Not so standard derivatives ( - This podcast is hosted by Roger Peng and Hilary Parker and has various different topics about data science in academia and industry.


I’d love to hear what your go to data science resources are. Get in contact if there are any meetups/podcasts/blogs/books etc that I should check out and they might get a mention on here in future!

Kaylea Haynes
Data Scientist

I know quite a lot about time-series analysis, demand forecasting and changepoint detection but I’m currently trying to broaden my data science skills.