My 2018: A year-in-review

Traditionally we look back at the past year around these times before jumping into new challenges after the new year. In this article I want to highlight the things that have added most value for me this past year and hopefully it contains some good resources and nuggets of inspiration to all who are reading this. I share not only my biggest accomplishments, but also my favorite MOOC’s, books and podcasts that I’ve familiarized with this year.

Master’s degree

In spring I was finally able to finish my master’s degree in technology based management. I wrote my final thesis about learning organizations. Learning organizations is a topic that is crucial in today’s society where everyone is struggling to keep up with the ever increasing pace of requirements and changes. I found many similarities learning organizations and companies using lean or agile methodologies. My key takeaway from the master’s degree as a whole, was that all courses included writing a paper and reflecting the course topic on my own workplace. It has helped better understand the organization that I work for.

MOOC’s

In autumn 2017 I had gotten more interested in the world of data science, but my master’s studies were taking all of my spare time. When I finally graduated I went on a MOOC spree! In 2019 I will most probably slow down with the MOOC’s and start to focus on more end-to-end project(s) so that I learn to apply the learned skills in practice. Here’s a list of my favorite courses that I took this year:

Data Analysis with Pandas by Boris Pashkaver provided a comprehensive course on handling data with the Pandas Python library. It has been of tremendous help both at work as well as helped me work more efficiently with other online courses.

Machine Learning by Andrew Ng helped me realize that there is not magic but mathematics behind machine learning. :) Although I don’t remember all the formulas and intuitions by heart, it prepared me for learning to apply machine learning in practice.

Machine Learning A-Z and Deep Learning A-Z by Kirill Eremenko and Hadelin de Ponteves served as an excellent overview of several algorithms, their intuition, their use cases, and how to apply them in practice. These work as a good reference/starting point for me when I want to use an algorithm in practice.

Python for Data Science and Machine Learning Bootcamp by Jose Portilla felt like the most polished course of all that I’ve taken. I really liked Jose’s emphasis on exploratory data analysis and home work assignments with related notebooks. Content-wise it was a bit overlapping with the courses from Kirill and Hadelin (although I took this course first).

Books

For the third consecutive year I’ve participated in the Goodreads reading challenge. As the year is coming to an end I’m barely exceeding my target for this year, which is 40 books. Here are the books that I read this year, which I rated 5/5 stars.

Born a Crime by Trevor Noah. This is an incredible story of a mixed colored boy’s childhood in the middle of apartheid South Africa. A story about a boy who is trying to find his place and community in a family filled with violence, abuse, discipline but in the end also unconditional love. A story filled with action, humor, joy, grief and tragedy. A story about Trevor Noah growing up.

The Goal by Eliyahu M. Goldratt is a novel about Alex Rogo who is about to lose his job as a plant manager. He’s not doing much better with his family either because of the long days at work to save the plant. This, until he meets his friend from the past who, by coaching, starts introducing him into the theory of constraints. Will he be able to save the plant? An honorable mention goes to Vasco Duarte and his book about #NoEstimates. In a similar manner, by using a fictional story, he presents how to succeed with software projects by defining and splitting the work into small similarly sized stories and features. using active scope management and relying on forecasts rather than project estimates.

Factfulness by Hans Rosling presents ten instincts that might distort our perspective of how we see the world. It teaches the reader to rely on data but to be critical on how it is presented. At the same time the book shows us some facts about the world where even the smartest people have been proven to know less than monkeys (=random answer). For those of you who speak Swedish, I recommend reading his memoir “Hur jag lärde mig förstå världen”. The books conveys the same message through incredible stories that Hans (and his family) got to experience throughout his entire life.

Deep Learning with Python by Francois Chollet introduces you to deep learning using the Keras library. The book is well written, the intuition is easy to understand and the code examples are elegant. In case you’re just learning Keras or want to learn a few more advanced tricks, I recommend grabbing this book and giving it a read.

Podcasts

For the past years I’ve mostly preferred listening to podcasts instead of listening to music. Here’s a list of a few great podcasts that I’ve found this year.

Artificial Intelligence by Lex Fridman. I find this podcast highly interesting not only because of the topics, but also because of the great high profile guests he has had on the show and interviewed.

SuperDataScience by Kirill Eremenko. I like Kirill’s podcast because to the interviews and the real life examples how data science is utilized in various fields of work. In addition, it is a great source of interesting people in the field of data science to follow and/or connect with.

Boss Level Podcast by Sami Honkonen. I’ll let his podcast subtitle speak for itself: “Interviews with interesting people doing awesome things”. Topics often relate to leadership, organizations, agile and so on giving me good insights and ideas for work.

Let’s Know Things by Colin Wright is probably my favorite podcast when I want to unwind and just set my thoughts elsewhere. For each episode he chooses a new topic from a varying range of alternatives and shares his thoughts/studies around that topic in little less than an hour. The podcast teaches me new things about topics that I never would’ve though of looking into otherwise!

Writing

A good way to strengthen your understanding of a certain topic is to write about it. As a result, I thought that I would challenge myself and start writing articles here on LinkedIn and on Medium. This was a huge step for me as I’ve been very cautious about putting myself on display because I felt afraid of what people would think of me and my texts. A couple of articles later I’m happy that I started writing. I get to share topics that I find interesting! The highlight thus far is my article on using Python in Power BI that has reached almost 10000 people in total. With the same blog post I was accepted as a contributor for Towards Data Science.

Next

Now it is time to slow down for the holidays and spend some with the family and friends. I look forward to a new year and continued networking, sharing and writing of interesting stuff here on LinkedIn. There is an abundance of interesting topics to learn. See you on the other side!