Personality traits for a data scientist
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Personality Traits for a Data Scientist; My top 3 explained.

My top 3 personality traits are probably not the standard, it has to do with how I define a great data scientist. This is a troublesome endeavour in general. Data Scientists are everything and everywhere these days and the field of data science is very broad indeed. It is however important to see these top 3 personality traits for a data scientist from the right perspective.

A great data scientist facilitates value growth within a company by making connections with others and helping people and projects forward in a data-driven way.

Let me explain this shortly, I see a data scientist as someone who drives innovation through data-driven decision making. You know all the technical stuff, the difficult bits. As a great data scientist you move beyond that. You create impact by working with people and helping them think and act differently. You end up as a bridge between the technical expertise and the way people work on a daily basis. Understanding one, and not the other, makes you destined to fail.

Noone likes failure, and I’m not gonna write this blog from a perspective of how to avoid failure. I want to talk about succes. Because in my own experience, there are some important traits to learn to cultivate within yourself, to become a great data scientist.

Personality traits 1: Horizontal curiosity

To be curious, almost childlike, should without a doubt be deeply ingrained in your system. There are many facets of curiosity, starting out in a new industry or company we need this skill to adapt to our new environments.

Luckily in such a fresh new environment curiosity comes natural to many. I am also looking for when it does not come natural. When it no longer gets triggered from outside stimuli. What happens when we dont emphasize curiosity from within? We slowly descent into set rhytms which where the only way out is a change in surroundings. This is no healthy and durable strategy, finding and maintaining curiosity from within yourself gives you longevity in your daily life.

Curiosity has another very important facet, it is not only about duration but about direction. To become a successful bridge between teams and find business value within your company. You need to be directing your curiosity horizontally.

Horizontal curiosity means broadening your field of interest, learn about people and their jobs, find parallels and bring them into your own daily routines and work. This is different from vertical curiosity, vertical curiosity is about deepening your knowledge on an ever specializing skillset. This distinction is so often overlooked and in my experience everyone talks about vertical curiosity. We need to be curious about deepening our knowledge. But as a functioning, great data scientist that brings people together to innovate towards new data-driven processes, we desperately need horizontal curiosity.

The great news is cultivating this is easy, if your new to horizontal curiosity, start slow and ask questions to people you speak about areas of their life. Eventually you will see that in every conversation you ever have, you can be horizontally curious.

Personality trait 2: Creativity

I’ve started to read this great book, “Big magic”, by Elizabeth Gilbert, its relatively old and printed in 2015, but new to me (You can find it here ). It talks about creativity,  how it is the gateway to our personal hidden deep treasures. Our life becomes so much richer if we learn to open this gateway. I’m not that far in yet, but it got me curious.

If creativity enriches your own life, which I’m pretty sure it does, I definitely will argue it enriches the lives of the people around you. And if it does that, then it enriches your company and workplace. As a data scientist we share our creativity with our coworkers by imagining new solutions and products. We are in the business of envisioning new horizons, talking to people about them and making it a reality.

“To create”, in other words, “bringing something into existence”, is an actionable skill. You need to do, talk, write, code, visualize to create. We can cultivate creativity by learning to start this one step at a time in a conversation, an essay or even a drawing of our vision. The key aspect for me about creativity is to take that step. It will soon turn into a sprint, get those creative thoughts out of your head and onto something, it will thank you for it.

“The only limit to your impact is your imagination and commitment” –  Anthony Robbins

Personality trait 3: Tenaciousness towards people

This is the final trait I want to discuss today, I know that sentence is not screaming tenaciousness, why stop here? Do I have other plans, things to go to? Actually I am committed to writing a blog with some clarity and ease of perspective, and that’s why this is my last trait for today.

Tenaciousness is seen as hardship, lonely on the trail we walk and are taught to push on. We are told there is light at the end of this tunnel. We have to commit to our ideas and walk the path to greatness. I too firmly believe in commitment, commitment to higher purpose and the people around you. If we are committed to this greater good, and getting there together, nothing can touch us. We can walk out in front of the group, as a data scientist we have too. We offer paths to a new and different future, what we need to learn is to commit not only to the path, but the people who walk that path and the end destination.

There is a place for the more traditional view of tenaciousness. We need to be able to grind out the work, once we agree on setting off down a pathway, we need the tenaciousness to deliver. You can’t lead if you don’t know how to follow goes the saying. Tenaciousness to our own words and agreements translates to other people. To deliver on this continually, is what makes people feel that you can bring them to this higher purpose.

Wrapping up

These were my top 3 personality traits for a data scientist. If you want to develop your skillset as a data scientist, focus on these 3 personality traits. It will shape the impact you create for years to come. Brought together In the right quantities these 3 traits are on my shopping list when I am looking to expand my team and hire a data scientist.

To make valuable connections we need to be curious. To move forward in new directions we need creativity. Bringing this reality to life we need commitment to our coworkers and peers.

See you in a click!

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My personal backstory; A road to Data Science for a living

As the title says, today I will share my own person backstory and  how it shaped me for data science to come into my life. As every person on the planet will tell you, their story is a little bit different, and so is mine. Now lets go down memory lane.

From a young age I’ve always been competitive, into strategy games and numbers. If I’m honest it wasn’t always completely healthy, I was always looking to confirm my own uniqueness and intellect when I was very young. For example in primary school from 6th grade you could play chess in a school selection, they would select 5 people and the winner would be  table number 1, all the way down to 5 to make a team. We would then as a team of 5 play against all the other schools in the area and number 1 would play against the number 1 of the other schools. I was number 1 3 years in a row, the first time that happened in school history and in my first year I was 9 and playing against these 12 year old’s. I thought it was awesome and it just reaffirmed everything I thought of myself.

Aside from the probably mental health concerns, what I enjoyed so much about chess from an early age is the mix between smart logic and time constraints. At the time I would also play age of empires, a real-time strategy (RTS) game that has the exact same tradeoffs between time and smart logic. This was my next addiction, during high school I competitively played RTS games online, I played a lot of e-sports but this was when the entire esports scene was in its infancy. RTS games taught me 2 things, it taught me how to search for optimal strategies given limited information, and it taught me the concept of studying and improving your strategies outside of the competitive environment, training.

By the time I started my economics studies in 2008 I hit a different period in my life, it was right around when online poker became main stream. This game just perfectly hit my interests, it was a natural evolution after playing RTS games, only the stakes were far more interesting. What was new about Poker? It was more math intensive, it brought me into the world of databases and statistics. It connected to my studies that brought me game theory and behavioral economics.

It was through online poker that I first got into contact with a programming language, there was this professional player who developed his own tool to calculate game theory optimal strategies in a given situation. He did this in python and shared his notebooks with everyone who followed his course. This was a very interesting new challenge for me, by going through his code and altering it to my own needs I self-taught basic Python. Later during my master we did some Stata, which really isn’t programming, but we also had the option to use R. I taught myself R too, just by copying code, looking online and using it for my master thesis.

By the time I finished my studies in Health Economics I was ready for a real job, interestingly enough I was not directly looking for a job in data science, it was economic analysis that was my principal interest. I actually had a job offer in hand from a consultancy in Rotterdam specialist in health economic analysis. At the time of that job offer I just started working part time doing some programming work for a data scientist who worked for this insurance company, MediRisk. He told me that they are looking to expand and got me an interview there. It was my curiosity for something new that brought me to the decision to go with the data science position at MediRisk.

See it can be easy to put something on a page and make it seem like a logical buildup and story, and in a way it was, but the real red line was always my curiosity for something new. Perhaps not completely new, but always one step forward and one step sideways. I love to broaden my view of the world by learning new skills, ideally by doing them. In my job currently I get to constantly explore new tools and technologies, understand them for their potential and excite others to join me in using them to create value. Learning by doing something new is something I aspire to always continue doing.

In a next blog I will share some tips and tricks into what I feel is most important if you want to start as a data scientist.

 

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Project Plan Presentation; Explain & Convince

One of the key business skillsets in data science is doing a great project plan presentation. Now this is true for almost every job, but in particular for data scientists. Why? It has to do with the nature of data science projects. Specifically the innovative aspects of data science projects.

As data scientists we are at the forefront of innovation, changing how people and companies do work. Hence we not only convince people the project is worth doing. We also have to convince people to change their historic way of working. People will be skeptic, part of convincing them is based on your technical expertise. There is a comfort in numbers and projections. But there is also a softer side to your storyline. Today I want to focus on some pointers on how to share your ideas with maximum impact.

In this series we earlier discussed how to write a great project plan, you can check that out here.

Craft a positive outlook

If my project succeeds, this will happen… The way we work will improve, get more cost efficient or we make more sales. All projects strive for something, what is so important is to start off at this final product and build this better future in the minds of your audience. It is however even more important to not build this outlook from a negative starting point. Let’s look at an example:

“Think of a new machine learning algorithm you want to design for an app of the company’s home inspection team, those guys that go to homes and check on the state of window frames for example. There is a reasonable chance you started this project because currently the home inspection team misses a lot of cases, or they call in maintenance crews far to often and find to many faults. In any case there is a problem in the status quo and room for potential improvement.”

It is now up to you to help paint them a picture of a new and better future, this algorithm will make their lives easier. However it is also up to you to realize that their first perspective can’t be that their current lives are bad, difficult, or any other negative connotation. Compare these 2 sentences:

“When this app is successful, we will be able to together work on new services and expand the business.”

“This machine learning algorithm will help our maintenance crews decrease their extreme workloads.  “

Which sentence inspires to move forward? Which sentence implicitly states the status quo is full of problems? I have seen a lot of presentations that explain the project benefits by going into detail on how big the current problems are. The thing is everyone is either aware of these problems or perhaps does not even recognize them as a problem. This loses your audience. Focus on the future, and invite your audience to travel with you.

The gift of responsibility

So we are traveling together, you are probably looking for a project team. What you should try and focus on is to give people their own responsibility as much as possible. Craft clear roles and communicate them. Do this the right way and you will have hit a major milestone in the project’s success. Let me show you 2 sentences again:

“To design the app perfectly for your daily jobs, I need your help to tell me what works for you.”

“You will have to tell me how to design the app, if you can test how to work with it on a daily basis the launch will be successful.”

What I am looking for here is to give a clear purpose and responsibility to your coworkers in the project, make them part of the success or failure as much as possible. Compare sentence 1 and sentence 2, which one conveys this more directly? Communicating this properly helps you in multiple ways, both by allowing you to focus on what Is in your own control and by sharing responsibility for the projects outcome. Put all this together and you are more likely to receive the commitment you need during the project.

Tell a story of your end results

Now that you have discussed where you want to go and who you will need to do what along the way, its time to ingrain your end goal into the minds of your audience. The key here is to tease your end result enough, by showing some examples and mockups, but avoid filling in all the details. Instead let your audience fill out the details and leave some room for questions.

See you know where your going, but what you will do precisely when you get? You may have an idea of how the new algorithm will fit into the daily work of your home inspectors, but  keep an open mind, if someone else has a great idea then leave space for that.

The story you should tell is focused on the practical day to day activities, don’t compare the current workday to the future workday, describe only the future. People know what they do on a daily basis better then you so you run the risk of coming across as someone who doesn’t know their facts. People don’t know the new future you will develop together in the coming months, so share some little bits. Take a look at this:

“When you enter the building, you will be able to take your ipad and take pictures of the current state of the building as usual. From this point on, the app will be there for you whenever help is needed. Unsure of the level of degradation? Unclear if you really saw every part of the window frame? For every question you will either automatically be guided by the algorithm or easily consult a coworker.”

Wrapping up Project Plan Presentation

These 3 pointers should help you convince more effectively that your projects are, worth doing. Craft a positive framework, delegate roles and share responsibility will help you build your future. What tips do you have to tag people along for your projects? Let me know below.

See you in a click!

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Writing a Data Science Project Plan; 5 Components explained

Writing a great Data Science project plan is a special skill. It does not come naturally to many people, me included. Over the past years I have written many project plans. In my own experience it’s easy to get lost on what formats to use and what content to include. Also how do you balance explaining the process of the project to the actual content of the project. There are many tutorials and templates about how to run your project. But in this blog I will give you some tips & tricks for writing it down.

This skill is very important, it’s your project plans that communicate your ideas. These documents are shared extensively with your coworkers and bosses. Being able to write a great plan gives you freedom at work. It allows you to work on your own vision of what is most important to the company. From a data science perspective this is extra important. We are busy with new technology and fresh ideas that need extra convincing. If you fail to develop this skill you are in danger of spending your working life focusing on plans from other people. Always remember that your project plan is meant to inspire, explain and convince. Here are 5 project plan components that helped me along in the past.

Introduce the bigger scope

We all start somewhere, and your data science project plan begins here. I find great benefit in starting with the bigger company strategy your project contributes towards. This gives your readers a clear initial scope and they should be able to relate to your ideas immediately. It is so crucially important to start writing from the bigger scope & vision of your company or client. Explain time and again where you want to go, and how your project aids to this vision. If you get people to buy into the bigger picture off your project plan, they will follow your specifics later.

An example of this concept might be helpful.

Lets say your companies goal is to help speed up the transition to clean energy. You are doing this by selling solar panels. You have a dataset on lifecycle management & repairs of the solar panels. The project plan describes how you want to more accurately predict when repairs might be needed. Now that we have introduced the project within the scope of the company it almost feels unnatural, but lets continue.

The big nono is to start by explaining the current issues with repairs and breakdown of solar panels. Instead, take the time and effort to link your project to the larger vision and how it contributes to that. First explain your macro vision, then get specific.

Visualize & story tell your specifics.

In your first paragraph you’ve reminded everyone why we are doing what we are. People now know how this project fits this framework. Your next goal is to get specific, what a good data science project plan needs is a good and precise technical goal. Link this to a current problem.

is there a statistic or graph that displays your current issues? This is especially very important when your project is focused on analyzing a dataset. Try to visualize your data and problem, and visualize your goal of improving this.

Jumping back to our solar panel company, lets say current lifetime of solar panels is 20 years. Repairs are common in a specific timeframe. Perhaps we show people a survival curve, and we can visualize how this curve can change after the project.

If your project is more focused on working with people, this section is great to feature your end users. Ideally they will explain the problem. If this is not feasible, apply some storytelling of what this person runs into on a daily basis. From that background you can introduce your dashboard or app that will improve their decision making.

The goal here is to immediately ingrain, through visualization or storytelling,  in your readers what the main problem is your focusing on and how this problem will disappear in the future.

Explain your projects process

Now is the time to convince people. Think of it like this, so far your plan is easy to follow and believe in, we have accurately shown the project fits the broader scope and explained the problem and specific technical goal. But how are you going to achieve this? This is where you can lose your readers or have the commit to your idea. As mentioned there are many tutorials on different project processes, use this to your advantage and choose an appropriate one for your analysis.

The most important thing I’ve learned is that it’s always better to apply your process model to the project directly. How are you going to work in this specific case? Your readers are looking for realism, if you expect that the modelling phase of your project is going to be the most important step for success, write it down. This has two benefits, one is that your reader doesn’t have to get lost in how projects are run in theory, and second if you do this step correctly you can reference the project plan later when your actually working.

Deep-dive on specifics, specifically

So far the project plan has been non-technical, if you’ve done it properly then you have not yet explained the specifics of your machine learning algorithm or the nitty gritty of the new to be developed data warehouse architecture. Now is the time to explain in more detail these elements, look back at your problem scope and intended technical goal. What methodology are you going to use to solve this issue? How is your dashboard going to look? This section is, most crucially of all the steps in your project plan, dependent on your target readers.

Think of it like this, remember the purpose of your project plan is to inspire, explain and convince. This section is where you convince people, but everyone gets convinced differently. We have a collective habit to get technical and want to let everybody know our solution, so that they start believing too. This is known as the ‘what’ of your solution. But this step only hits home to your technical colleagues, if they don’t have to read your project plan, don’t write this paragraph.

If you need to convince your boss, then you will have to do more work here, not to further explain and teach, but to simplify and generalize your methodology. If your going to build a dashboard, provide a mockup here and explain the intended use in more detail. Focus on answering why this specific solution fixes the problem scope you made earlier. Stay clear of explaining the ‘what’ too much.

Plan for problems

Planning is the final leg of your project plan. This section also dependent more than others on your company’s culture and the projects specifics. Is it important to be precise? Are there external deadlines that need to be met? Or can you discuss during the project how much priority its getting? These are all questions I can’t answer for you, but they do determine how to write your planning and how much time and effort you need to put in this paragraph. In our company we run many internal projects at once, often juggling them for prioritization. This means that planning and deadlines are often flexible.

What I have learned over the years is that it is crucially important to describe your project planning risks here, where do you expect a high probability of a roadblock? What would that look like and how does it affect the timeline you initially had in mind? This is very important information for your readers too and it shows you have thought off not only how to make things work, but also how things can go wrong. Then when things do go wrong, you can refer to your project plan and discuss with your team what the consequences are.

Writing a data science project plan; wrapup

The 5 components I outlined above are important elements in every data science project plan. I’ve found them to be invaluable for keeping structure and focusing on the main points.

If you learn to do this correctly it will ultimately allow you to work on the project you love, instead of having to learn to love other people’s projects.