presentation – THE MIGHTY DATA

Why should Data Scientists BE SCARED ABOUT AI coming as well?

Debate on if Artificial Intelligence (AI) will slash some jobs (or entire professions) transformed from obscure omen reading into mainstream heated issue. Truck drivers, financial intermediaries and few other professions are nervously looking ahead if they gonna join red-listed endangered species. They certainly have good reasons to be worried …

… but have about Data analytics? Are Data Scientists on the AI replacement to-do-list as well? What a silly question, isn’t it? Ultimately, the Data Scientist are the oil of the AI solutions. Thus, they will be the ones eating others’ jobs and they do not need to worry about their own future. Or should they maybe?

How sure you should (not) be

Few months ago, the economic expert commentaries were still shy in indications that the world economy state might deteriorate in quarters to come. Back then it was a message per week on this issue. In last 2 weeks the matter got visibly more dramatic, the black omens pop up now on, literally, daily basis. As we learned from past, every economic crisis usually slashes substantial number of jobs opportunities on the market. In this sense the crises to come will be no different. We learned not to worry about that too much, as new jobs are recreated back, when the economy walks from crises back to good times. The problem is that on this account the nearest crises will be different. It will kill some jobs that will be never be recreated again.

Almost any week you can see some profession striking for salary increases. As the economy is booming employees push to reap a slice from the victory cake. However, there are some jobs where salaries kept on rising without any push from labor unions. Data Science is one of that areas where annual income has been on crazy adventure to the north. Driven by over-demand on (weak) supply, companies were raising the pay-level to swerve people from competitions (or motivate more people to get re-qualify to Data Scientist). But no more. Data from US (largest free Data science labor market) indicate that the entry salary of the Data Scientist stagnated in 2017 and corrected few percentage points down in 2018. The reasons for that is the price of Data Science talent got over the level fitting business case for their possible impact in company (to justify their pay). Not many people realize that higher remuneration of these years are last dances before DJ calls off the party.

In both cases, the strike- or surge-driven salaries will make the AI replacement scenario more severe. When we come out of the crises, the employers will be facing the dilemma if to rehire stuff again or to replace some part of it by automation. The higher the annual salary level of employees, the easier the case for AI solutions to be cost-saver. Especially, the area of super expensive (and still scarce) Data scientists offers a lot of room for rethinking, as one year cost of Data Scientist in US is, literally, 7 digit figure.

The (seemingly strong) peace of mind of the Data community about their jobs security has roots in fatal attribution error. For most of the manual jobs the replacement will come with automation, presumably intelligent computers running on data. Therefore, data processing industry might be perceived as the lubricant of the whole automation process. Hence, the strong believe that data scientists are on the right side of this transformation river. While data might, indeed, be the oil of the AI transformation, it is ill conceived that humans necessarily need to take part in extracting it. If we stick to analogy, most of the things on oil rig is not human labor force but automation itself. Similarly, the repetitive and easy-to-automate jobs in Data analytics will not be run by humans. If you take two steps back and impartially review the work of most of recent data analysts their work is much more well-defined and repetitive than driving of the autonomous car. Therefore, data community should not wall into trap of illusions, that AI job revolution will take detour from their domain.

Time for panic?

The omens are out there, time for panic? Well, we as humans were having difficulties facing the previous industrial revolutions. And we will probably struggle this time around as well. Almost any time disrupting technology arouse in past, first answer was to push back by, literally, beating the machines. However, there are ways how we can face the AI job hunt properly. I have been invited to speak about HOW TO SURVIVE the (first) AI ATTACK on DATA SCIENCE JOBS at the DataFestival 2019 in Munich this week. This is a short teaser about the topic, and I offer you exclusive sneak-peek into

PRESENTATION >>> FILIP_VITEK_TeamViewer_SURVIVAL_TICKET

here as you are precious members our TheMighyData.com community. As this topic hits all of us, any comments or views from you on this topic are highly welcomed in comments to this blog or at info@mocnedata.sk ; Enjoy the reading and see you on some other event soon.

Späť na domovskú stránku

Berlin Meetup: Cool Feature engineering [my slides]

Dear fellows,

on Wednesday 20th Feb 2019 I have been invited to speak at AI IN ACTION Meetup organized by ALLDUS in Berlin. The topic was one of my favorite issues, namely Feature engineering. This time we looked at the issue from the of How To Do Cool Feature Engineering In Python. If you had the chance to be in the Meetup crowd and failed to note down some figure, or if you are interested to read about what were the ideas discussed even thought you were not there, here you can find attached the presentation slides from that MeetUp.

slides >>> FILIP_VITEK_TeamViewer_Feature_Selection_IN_PYTHON

If you have any question of different opinion on some of the debated issues, do not hesitate to drop me a few lines on info@mocnedata.sk ;

Späť na domovskú stránku

Presentation from Data Natives Berlin 2018

Dear all,

as you probably have noticed in my LinkedIn I had the pleasure to talk at the DATA NATIVES conference in Berlin on Feature Engineering Is Your Ticket To Survival In Analytics. As some of you probably could not make it I decided to publish the slides here on the blog, for you to take benefit of them:

FILIP_VITEK_TeamViewer_Feature_Selection_EN

Looking forward to any feedback of yours!

When using info from this presentation, please honor the author’s rights to be properly cited as source of your ideas.

Späť na domovskú stránku

Unconventional methods of Feature engineering

My dear fellows,

after settling down in Berlin Datascience community and visiting few interesting, local meet-ups, I agreed to contribute to the community knowledge sharing as well. Thus, if you have a while you can join Sebastian Meier [Technologiestiftung Berlin], Sandris Murins [Iconiq Lab] and me on June 14th 2018 18:00 at AI IN DATA SCIENCE – BERLIN at Wayfair DE Office, Köpenicker Str. 180, 10997 Berlin, Germany.

To ensure you that it really pays off to reserve some time for this meet-up let me make a sneak-preview of what you might get from this event:

In my presentation, titled “Feature engineering – your trump card in Machine Learning” you will find out what are the 2 key reasons for feature engineering future. First explains how variables can become competitive advantage of your models. The second one goes even beyond that and hopefully opens your eyes why Feature engineering might be actually essential for Data Scientists’ future job security.

After getting warmed up on the overall role of features, we shall jump into how variables are actually designed/generated in real life. What are the most used approaches to design the features? But more importantly, what are the common pitfalls that we all often fall into while designing our feature sets for Machine Learning predictions? Are there tricks to prevent us from failing on these ?

However, just to demote the traditional ways and solely point out their notorious downsides without naming the alternative(s) would be a bit unfair. Therefore, in second half of my talk I want to walk you through unconventional ways how to design your features. What is more, I will take help of 4 specific case studies where unconventional features were needed. I hope you might get inspired for your own work.

If you cannot make it (for whatever reason) to this talk and you are member of MighytDataCommunity, you will find the presentation slides here after event, in member-only-restricted blog post. If you are not member of the free MightyData Community yet, you can get your free membership to this community in less then 2 minutes, by registering here.

Looking forward to seeing you all at the AI Meet-up event !

Späť na domovskú stránku