The 3 Habits to Have to Become a Highly Effective Beginner in Data Science

In this blog post, I elaborate how someone who’s learning data science may increase their effectiveness based on the principles that I’ve learnt from a book called The 7 Habits of Highly Effective People. I specifically focus on how someone might increase their contributions on websites like GitHub and Kaggle by adopting the 3 Habits discussed here. Contributing on such websites may effectively build the contributor’s reputation amongst the data science and/or the software development community. What I essentially did here is transfer learning; I simply took the information and strategies that I learnt from a really good book essentially about productivity and applied all that to a new context, the data science context.

The Seven Habits of Highly Effective People has sold over The 7 Habits of Highly Effective People has sold more than 25 million copies in 40 languages worldwide. At the time of this writing.

So Why Did I Write This Blog Post ?

Well, firstly, some people may end up finding jobs in data science and software development by showcasing their skills online and also networking with other people. However, there’s one issue which actually inspired me to go through this book:

• A lot of people, including me and especially aspirants/beginners, do not regularly post their data science projects on GitHub, Kaggle or somewhere else online. Instead many, if not most, of the beginners tend be focused on just learning data science concepts from textbooks and online courses. Which is actually not learning, because to learn and not apply is really not to learn. Furthermore, some of these skills take so much time to truly master, especially machine learning and it’s easy to really spend a lot of time absorbing concepts from textbooks and online courses without actually applying the concepts on coding or data science projects. The meme below is somewhat relatable to many and highlights the issue I am concerned with throughout this blog post.

A popular meme from the internet that I took from a data science influencer.

Well, although learning new skills is important, the issue here is not showcasing them on some platform.

Overview

There are 3 important definitions made by the author Steve Covey that reoccur throughout this book: Dependence, Independence and Interdependence. Precisely,

Dependence means you need others to get what you need. All of us began life as an infant, depending on others for nurturing and sustenance. I may be intellectually dependent on other people’s thinking; I may be emotionally dependent on other people’s affirmation and validation of me. Dependence is the attitude of “you”: you take care of me… or you don’t come through and I blame you for the result.

He then says this about independence,

Independence means you are pretty much free from the external influence [and] support of others. … Independence is the attitude of “I”. … It is the avowed goal of many individuals, and also many social movements, to enthrone independence as the highest level of achievement, but it is not the ultimate goal in effective living. There is a far more mature and more advanced level.

And finally,

We live in an interdependent reality. Interdependence is essential for good leaders; good team players; a successful marriage or family life; in organizations. Interdependence is the attitude of “we”: we can co-operate; we can be a team; we can combine our talents.

So life by nature is highly interdependent, to try to archive maximum effectiveness with independence is like trying to play tennis with a golf club. A although employers seek for someone who can work “independently,” what an aspiring data scientist or developer, however, should aim to be is being intellectually interdependent. Meaning that they should realize that they need to join the best thinking of others with their own thinking, as an interdependent data scientist or developer they can share themselves meaningfully and deeply with others and that they should have access to vast resources & potential of other people. That’s what it means to be intellectually interdependent. Interdependence is a choice that can be made by independent people, but not dependent people. Habits one, two and three essentially deal with self-mastery; they move a person from dependence to independence.

So what is effectiveness?

The author explains effectiveness from the greedy farmer and the golden goose story. In a nutshell, there’s a farmer who had a golden goose that lays a golden egg every morning. One day he decides that instead of waiting day after day to get the golden eggs, he’ll just kill the golden goose, split it wide open and get all the golden eggs at once, but he was surprised when he found that there were no eggs inside the golden goose. Most people see effectiveness from the golden egg paradigm: the more you produce/do, the more effective you are. The author, however, suggests that effectiveness is actually a function two things:

1. What is produced (the golden egg) and,
2. the producing asset or capacity to produce (the golden goose).

If someone learning to be a data scientist only adopts the pattern of life that focuses on the golden eggs (what is produced) and neglects the golden goose, they may soon find themselves without the golden goose (that is the capacity to produce). However as we saw from the meme above, most aspirant data scientists have a tendency of focusing on certifications (i.e the capacity to produce; the golden goose) and they neglect doing projects (i.e what they should produce; the golden eggs) to upload on websites like GitHub, Kaggle etc. The author also suggests that effectiveness lies in the balance, what he calls the P-PC balance, the P stands for what is “produced” and the PC stands for “production capability.”

Habit One: Be Proactive

So what is proactivity ? Well, proactivity according to my Cambridge English dictionary means “taking action by causing change and not only reacting to change when it happens,” however according to the author, proactivity means something more than just taking initiative. It means that as human being we are responsible for our own lives; our behavior is a function of our decisions not our conditions. Proactive people focus their energy on what the author calls the circle of influence, which is something that they can do something about. They work on things that they can do with positive energy and they cause their circle of influence to grow or expand as well.

So as an aspirant data scientists or developers, what that essentially means is that you should focus on what you can do, rather than focusing on what cool or interesting projects to do that can dazzle the data science community. Some coding and data science projects that I am interested in are somewhat out of my circle of influence and it’s probably the reason why I have done much fewer projects that I intended to do last year. So what I should do is projects that I know are within my skill level, in other words, within my circle of influence. After all, the most important thing in doing a personal project as a beginner is to understand. That’s what’s important. That should be the purpose of a project.

The author challenges us to work on things within our circle of influence for 30 days and to make small commitments and keep them, then make a little larger ones and keep them, and so on. This could mean that as an aspiring data scientist do practice projects on the famous titanic data set, house pricing data set, cancer data set MNIST data set, etc. Don’t mind that these projects aren’t good enough to get you a data science job or to dazzle data science communities on GitHub and Kaggle. Again, the important thing is to understanding. Then eventually, the goal is to work on unique projects using real world data. That’s what I can infer and suggest based on the information that I have gathered from data science experts and this 7 habits book.

Habit Two: Begin with an end in mind

Keeping an end in mind helps make sure that you take every step you take is in the right direction towards a goal that you have deemed supremely important. An end in mind is there to help you understand where you are now relative to your destination, so that you know what to do each day, week or month. It is possible to be very busy and very efficient without being truly effective.

“Efficiency is doing things right; effectiveness is doing the right things.”

~ Peter Drucker

What I can infer from the book is that one that what’s lacking in many aspirant data scientists or developers and many people is personal leadership. It is quite normal for people to be trapped in the management paradigm, that is, demanding efficiency, setting and archiving goals without even clarifying what are their values. As an aspiring data scientist, these values could be what type of data scientist that you want to be or what you want to do. The consensus is that there is so much stuff to learn in data science, even field experts don’t know everything.

One other other suggestion to start applying this begin with an end in mind habit is to identify a project that you will be facing in the near future, then write down the result that you desire and the steps that will lead to that result.

Habit Three: First Things First

The author asks the readers to take a moment to write down short answers to the following two questions, which are important for this section:

1. What’s one thing you could do, that you aren’t doing now, that if you did on a regular basis would make a tremendous positive difference in your personal life ?
2. What one thing in your business or professional life will bring similar results?

Habit three is the practical fulfillment of habit one and two. Habit one says you’re the creator , you’re in charge, you can change the ineffective script that you had been given in your childhood or society to follow and make an effective script. Habit two is based on our imagination, the ability to envision what we cannot yet see with our own eyes. To put it in another way, habit one says “you’re the programmer,” habit two says “write the program” and habit three says “run the program.”

Habit 3 is essentially about personal management. Effective management is putting first things first. While leadership may decide what first things are, it is management that puts them first, day by day, weekly, monthly etc. With that mentioned, it is clear that time management is key. In a single phrase, the essence of time management can be encapsulated as

“Organize and execute around priorities.”

The author asks the readers to draw a time management matrix, which I took from Wikipedia:

Now with this time management matrix in mind, what quadrant do your answers to the first two questions at the beginning of this habit three section fit in?

Well, you’ll find that almost always that people’s answers fit into quadrant two, meaning that they are important to them but not urgent. That’s why we don’t do them! So to effectively manage your time for important but not urgent things is to make a plan, as shown on the matrix above. One way to plan is to make a weekly plan on a Sunday or another day of a week on what time/day and how long you should be working on your important but not urgent things.

Concluding Remarks

Perhaps doing data science projects and uploading them on GitHub consistently is hard because it consistently requires personal management, time management , personal leadership, personal vision, awareness of one’s ignorance just to name a few topics covered in the book.

You may be wondering why did I leave out the remaining 4 Habits from the book, why not The 7 Habits to Have to Become a Highly Effective Beginner in Data Science? Well, the answer is that habits four, five and six are essentially about interdependence (i.e working with others). Most aspiring data scientists tend to work on their own and this is partly because employers look at personal projects over team projects and what an individual can actually do. So it is the first three habits of Steve Covey’s book that I found most appropriate for data science beginners to know, especially because beginners most likely aren’t yet working with other data scientists in a team. There are exceptions of course.

Thank you for your attention. As always, clap and share if you found this blog post somewhat informative.

Remember:

“The only source of knowledge is experience.”

— Albert Einstein