What I want this week

Do you have a favorite afternoon cake? Something sliced and pretty that tastes amazing with a cup of tea or coffee? I think a coffee and walnut from an ancient cooking book is the one for me, but since I have no idea where the recipe is I decided to try something new today: pistachio and blackberry.

I ate a delicious light and fluffy pistachio cake at Miro’s tea house a while ago and have been dreaming of it since. I wish I’d taken a picture but it is a three-layer creation beautifully decorated with what I think is cream cheese frosting and blackberries. Elegant, maybe slightly eighties in colors: jewel-like berries nestled on the palest jade-green frosting. Then they stopped making it.

I looked up a few recipes and picked this one .I normally substitute or mess around with a few of the proportions e.g. put in some almond flour for all purpose flour, but this time, I followed the recipe exactly. Despite that, it was much more dense than it looked in the pictures. I don’t know what it is but lately I just can’t get light and fluffy cakes. Maybe I was supposed to whip the egg whites separately, like a genoise? The recipe has baking powder and soda, so I had assumed that was not necessary. In any case, it’s super tasty, full of fresh pistachio flavor and crunch, but much denser and less light than I was expecting. I used the suggested cream cheese frosting, adding in some blackberry jam in the sandwich layer as well. The jam was too sweet along with the frosting, so next time I’d reduce down some fresh blackberries for the filling.

I decided not to go for the honey frosting suggested in the recipe, but instead for a good old plain cream cheese frosting.

I’d definitely attempt this cake again, obviously trying to lighten it with whipping the egg whites. It was gone in two days, yum!

How common is miscarriage? 10 to 20% of (known-about) pregnancies end in miscarriages¹. This is also possibly an underestimate as many women may be having them and not even realize it, as it happens so early in the process that they might assume that it’s just a late period.

How often do you hear about people having miscarriages though? I feel like there is a huge taboo around talking about it: at work, amongst friends, wherever really, apart from with those extremely close to you. When someone asked me how I was doing after mine last year, I wanted to say,

“Actually, not so well, I have blood clots and pieces of fetus coming out of me and I’m freaking out.” What I actually said of course was “Oh, a little stomach ache, no big deal.”

Why is it that we are not talking about this common and traumatic event more, to make it normal and to mark with respect something that is very real and sometimes frightening. Even taking the best case scenario where one knows that it’s going to happen, is bad enough, I cannot imagine what it must be like if it just happened spontaneously.

I found that as soon as I shared my experience with others, many people came forward with their own stories, far more common than I thought; friends who had suffered in silence, people who felt that they couldn’t talk about it as it would let people know that they were planning on starting a family and that wouldn’t reflect well on them. There were also partners, boyfriends and husbands who I am sure wanted to talk about the sense of loss and helplessness but we almost never hear from these people. It is a normal and frequent process for humans. It would be amazing if – like headaches, colds, broken and sprained limbs – we could talk and share about miscarriage to ease the burden and not feel so alone.

1 The Johns Hopkins Manual of Gynecology and Obstetrics (4 ed.). Lippincott Williams & Wilkins. 2012. pp. 438–439. ISBN 9781451148015. Archivedfrom the original on September 10, 2017.

According to this article which references Linkedin, data scientists saw a 56% increase in job openings in the US in 2019. IBM also predicts demand for data scientists will increase by 28% by 2020; so what’s the fuss all about? Never has there been a time where it’s so easy and cheap to record and store so much data about ourselves. Companies ultimately predict what customers are going to do and buy next, and how they can more effectively target customers. Research institutions wants to gather all the data they might possibly need. Governments wish to gather as much data about their citizens as possible. Naturally, therefore, those who can manipulate this data are in demand.

What does a data scientist do?

This seems hugely variable depending on the organization but it can range from working closely with software engineers to optimally collect the correct data from customers, pulling and cleaning that data from its source, building and using statistical models to fit data and predict outcomes or visualizing the data in graphs and tables. The common theme is command of basic programming using languages such as Python and R and know some elementary statistics. Many of the job openings mention experience with the database querying language SQL, and also the data visualization software such as Tableau.

What about all the people already trained to do some of these things like economists, physicists and operations research people?

I don’t think that these posts will be obsolete, but data science is certainly the subject of the moment, along with machine learning. Having trained as an economist, I covered a lot of the relevant topics such as statistics and basic programming (more so with more basic packages such as Stata) but have gaps to fill before I’d be eligible to apply for data science roles. If you’ve had some technical training that covers areas such as modeling and statistics, there is something to build on if you want to switch over. I’ve also come across quite a few people who trained as physicists and have made the transition to data science/applied scientist etc.

Where is data science going in the future?

At the moment, as this Forbes article says, there will surely be a a more refined classification of sub roles within data science, as there are currently so many different definitions.

Whilst the labels of “machine learning” and “data scientists” may fade, the ability to gather data, ask the right questions about what we can know and infer, plus rigorous modeling will always be useful.