Hi there!

It’s Kan from Exploratory.

We are at the final stage of getting ready for v4.3 release. This release focuses on improvements on the existing features and the overall product quality, though we have managed to introduce some cool new features.

Anyway, before starting this week’s update, our Data Science Booster training’s enrollment is still open. We have a student discount (50% off). If you are interested in learning Data Science without programming, sign up today!

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Now, here’s this week’s update!

What We Are Reading

Tim Harford’s guide to statistics in a misleading age - Link

The word ‘Fake News’ has increased our awareness on the fact that there are continuous floods of attention-grabbing headlines filled with suspicious claims and data published and shared by media, both traditional and social, every day.

As we have more data and more tools available in our hands, the technical part of the data analysis has become relatively easier than before. But the ‘analytical thinking’ (or statistical thinking if you will) part of the data analysis - understand the problems to solve, ask questions about data, build our own hypotheses and evaluate them, and then conclude to make decisions - is still greatly left out even in the world of Data Science.

Tim Harford, a Financial Times columnist, is suggesting a few very useful tips to understand the data better, without getting fooled by suspicious stats and statistical claims.

Tech companies should stop pretending AI won’t destroy jobs - Link

I tend to take a position that we shouldn’t worry too much about AI based on my understanding of the limit of AI, at least for the near future. I’d rather think that AI is one of the tools human can use to understand the world better. But, as Kai-fu Lee claims in this article, it is true that there will be a lot of jobs that will be replaced by AI, and those will end up losing their jobs tend to be the ones who are not familiar with AI or Data Science at all. And that’s, to me, a real problem, especially for our society.

The speed of technological advancements has been only accelerating. Even for me who have been in this industry for 20 years, it’s becoming harder and harder to keep up with the latest. This makes it harder to expect the existing education systems alone to provide the kind of training that will benefit people for the next 10 years.

I think anybody in Data Science field has a great opportunity to share the knowledge and experience with others who are not in the world of Data Science today. We need to make sure we are not widening the opportunity gap for the future.

Learn with Google AI: Making ML education available to everyone - Link

I don’t think this is for ‘everybody’, as expected with Google, it’s more gearing towards to engineers. But, it’s still great that they are trying to make it easier to learn ML/AI.

Algorithmic Impact Assessments: Toward Accountable Automation in Public Agencies - Link

As more AI-powered automated decision-making systems emerge we expect more governments will be weighing in to ensure that such systems are fair. EU’s GDPR (General Data Protection Regulation) is one, and New York City has just announced a task force around Algorithmic Impact Assessments (AIA).


  • Become A Full Stack Data Science Company - Link
  • Conversational AI bots aren’t improving at the rate we thought they could - Link
  • How do you test if a company is AI first or not? - Link
  • AB Testing is Dead - Link

Quote of the Week

Faith: not wanting to know what is true

Friedrich Nietzsche

What We Are Writing

As mentioned in the last Weekly Update, Linear Regression is a simple yet super useful algorithm in the world of Data Science, yet not many people outside of Data Science field use it for their daily analysis. In order to increase the awareness I have started a series called “A Beginner’s Guide to Exploratory Data Analysis with Linear Regression”, and here is the second post I have published last week.

  • A Beginner’s Guide to Exploratory Data Analysis with Linear Regression  - Link

This post explores the multiple linear regression and investigates which variable of Mother Age and Father Age could be more of the direct influence on the gestation week length.

What We Are Working On

We are improving many of the existing functionalities in the coming v4.3 release. One of the areas getting such improvement is Dashboard. With v4.3, you will be able to add more than 4 charts (up to 12) without counting the single value type Viz. Also, it will be much easier to configure the layout of the page by drag and drop.

Data Science Booster Training

As mentioned at the beginning, our Data Science Booster training’s enrollment is still open. We have a student discount (50% off). If you are interested in learning Data Science without programming, sign up today!

Enroll April Booster Training!

That’s it for this week.

Have a wonderful week!

Kan CEO/Exploratory

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