3 Things I Wish I Knew Before I Became A Data Scientist

Over the years, I have mentored and trained lots of individuals from different fields, to become data scientists.

While I’ve often preached that you don’t need prior experience to become a data scientist, I’ve found the most interesting students to be those who have taken some sort of training before – usually online classes (MOOCs) or crash courses.

They are people who are looking to transition into a data scientist career. Why? Because a data scientist career while is a lucrative one, has tons of perks to it, including career future-proofing. But I’ll reserve that for another post.

Back to students and individuals who are looking to become data scientists. I’ve found many has good concepts about being a data scientist, but more often than not, there’s a bunch of misconceptions that prevent them to being a data scientist, especially lost opportunities.

If you’re looking to become a data scientist and is clueless about it, don’t worry. I have experienced the same doubts and misconceptions that you had, so I’d like to explain them to you in this post.

Misconception #1 – Data Comes In A Clean Format

Most data scientist newbies think data gathering is a simple task, where data usually come in a clean format. Just download data from the sources and plug and play, done – right?

Unfortunately, that is never the case.

In fact, the bulk of a data scientist’s time, up to 85% in fact, is spent cleaning data. That may sound like an exaggeration, but if you consider the time you spend looking for accurate data, asking around and trying to make sense o the data, 85% of your time seems like a right number.

Back then, I had to figure out how to download over 16GB worth of raw tweets without breaking my university’s network. Imagine cleaning over 50 million text data with vague field headers like A1, B2, ZYC, TMO, etc. And guess what, there are no such things as a documentation when it comes to data gathering and cleansing.

Lesson learnt: Data gathering and cleansing will take up 85% of your time as a data scientist. Be prepared for that.

Misconception #2 – Choosing Cool Algorithms Make You Appear Smarter

One of my favourite thing while I was doing my PhD, was a weekly session, where I would have to share my weekly report. Being the person who loves technology, I’d presents results to my supervisors and explain the latest algorithm that I have implemented.

However, that does not warrant a PhD.

I had to spend extra time and hours to explain, why the Hans Linear Allocation model is better than LDA. Eventually, I felt all the time spent wasn’t worth it. So I took a step back and focused on working the data using existing models, getting much better performance.

From the beginning, I should have spent more time understanding the data and its quality, rather than looking for the next coolest algorithm.

Lesson learnt: Always choose good data over cool algorithms, all the time.

Misconception #3 – Forget About Visualization

No matter how good your algorithm, model and data is – if you can’t show it on a chart that your audience can understand, then your findings, algorithms and models are as good as useless.

Many students who I’ve taught, often put little effort into mastering visualization. After all, data scientists are supposed to work on data, not visualizations, right?

Well, visualizations are in fact, the meaning to all your effort. This rule is applied to all ages of your audience, whether if you’re presenting your data to an 8 -year kid, to a professor or a CEO. In fact, you’ll be surprised to find even professors cannot comprehend things beyond a bar chart.

And please, no pie charts.

Lesson learnt: In data science, visualizations rule, period.

Conclusion

What are other data science stories or things you wish you knew before you became a data scientist?

If you’re still early on in your data science journey, feel free to run thoughts with me before you move further into your data career. I’ll be happy to help!

p/s: If you want to learn more about data science and begin a career in data science, you should join the 48-hour data science Bootcamp.

Why Javascript Should Be Your First Programming Language

Every time you see a dynamic interaction happening on a website you visit – ticking seconds and minutes or an animation while clicking or hovering over a button — that is all powered by Javascript. If you didn’t know, Javascript is often abbreviated as JS.

But what is Javascript really? And why do so many people recommend it as the best starting programming language that a beginner, like you, should pick up?

What is Javascript?

You can easily find all types of Javascript definitions on the internet.

But let’s make it easy for you. Imagine a website as a person, and Javascript is the movement of that person. For example, the gesture of hand to hold a cup, dancing, or waving.

Javascript allows you to do animations like this on your website.

A website is usually built with HTML, but you can put in interesting and attractive interactions by implementing Javascript. Because of this, Javascript is commonly used in website construction as a front-end language (client-side).

However, in recent years, because of its popularity, it is also used to create backend framework (Nodejs). In short, once you master Javascript, you can use it to create a website, both front-end, and back-end. The web developer who knows how to do front-end and back-end coding is commonly referred to as ‘full stack developers’.

If you’re keen to be a full stack developer, read on. I’ll quickly explain why Javascript would be the perfect programming language to learn, assuming you’re new to coding.

Why Learn Javascript As The First Coding Language?

1. It Is Easy To Get Started

Javascript is a web programming language that runs on the front-end and back-end, which basically covers everything you need to build great websites for your business or startup. It supports Object-Oriented Programming, event-driven and functional programming styles. This doesn’t mean that Javascript is more shallow or simple compared to other programming languages.

But the reason that it’s easy to learn is that Javascript works in every modern browser. There’s no need to go through tedious software installations to get started. Website apps such as codepen and jsfiddle are perfect for beginners to practice Javascript without having to set up anything.

2. Extremely Powerful For Web Applications

Back in the early years, Javascript was built for the front-end environment, where it allowed developers to create animations and interactive elements on the front-end of most websites.

However, in the past few years, Javascript went through a ton of development and now it could do a lot of things, from front-end to back-end, server-side code. Think of it as a language that can build really beautiful websites and at the same time, code very efficient servers.

The development of JSON has made it possible to transfer data between a website and server. The existence of Node.js next was what made it possible to build servers using the very same Javascript language. And more and more exciting development is underway, as the programming language becomes even more mature and in-demand.

You probably guessed it as well, with mobile being the main device for user consumption these days, Javascript can be used to create beautiful and functional mobile web applications and websites.

3. It’s A Highly Needed Skill

Javascript ranks top #3, for the most in-demand programming language

Being a powerful programming language, makes Javascript a highly sought-after skill by corporations.

With strong Javascript skills, you never have to be afraid being out of a job. Most programming jobs posted in any job sites will require the job seeker to have Javascript skills. Even if you don’t see Javascript listed as a needed skill, you’ll most likely see a requirement in skills like Angular, React, Jquery, AJAX — which are all Javascript frameworks. Not to mention, that developers with Javascript skills can often negotiate for a higher salary!

Javascript used to be a skill that belongs to the front-end developer, however, with NodeJS, lately, more job openings are requiring Javascript as a backend language.

In short, learning Javascript and mastering the language will pay off for you as an aspiring web developer.

4. Strong & Supportive Javascript Community

You’ll find huge communities discussing Javascript on places like Stackoverflow

One of the very important factors to choose a coding language to start with is the communities which are supporting it. In the case of Javascript, it has the largest Stackoverflow community, discussing it every day and it also happens to be the most tagged language in Github.

As a beginner, this means that it’s easy to get questions answered. Throughout your coding journey, all you really have to do is to look for answers to coding problems you have in the large community. And if you were to ask questions within the community, you can expect your questions to be answered really quickly.

Then when you become a master at Javascript, do your part by contributing back to the community that once helped you.

5. Existing Frameworks For Faster Coding

So what’s next after you’ve mastered the basics of Javascript?

The one thing that I truly love about Javascript is the existence of numerous frameworks that are free & available to explore, once you mastered the basics of Javascript. For example, some of the more famous frameworks like Nodejs, AJAX, AngularJS, ExpressJS allow you to explore backend architecture, routing and much more.

Frameworks generally help you program faster and at the same time, create tons of amazing web applications, right off the bat. Javascript has truly evolved beyond simple animations on websites, but once you master it, combine what you’ve learned with frameworks and you’ll be able to build anything you can imagine for the internet.

Where Do You Go From Here?

We believe that the best way to learn Javascript is to actually practice on real-working files. If you like to get started, make sure you stay subscribed to our newsletters at LEAD, and watch out for free webinars and courses that we throw out from time to time.

Other than that, let me know about yourself! Why do you want to learn Javascript? What are you planning to do with it? Let us know in the comment section below as we’d love to hear from you.

How Do You Know If You Should Learn Python?

How Do You Know If You Should Learn Python?

The most popular question we get from students is whether learning Python is going to worthwhile?

Learning a new programming language is hard work. Should you spend a weekend to learn it?

To make things simpler, let us show you what’s possible with Python. And let the applications speak for themselves. Let’s get started.

Vehicle And Motion Detection

One of the very cool things you could do with Python (among thousands of other applications), is to use motion detection to calculate the number of cars in a video. Watch the 2-minute video below to see how it’s done.

What else can you do with Python for data science?

Python is coined as the ‘second-best language for everything’ in the Python community. Take it as a programming language that does everything, much like a Swiss Army knife for the coding world.

It supports object-oriented programming, structured programming and functional programming patterns.

Here’s a list of some (not all) applications, possible with Python:

  • Finance (Algorithmic Trading) – Much of the hype of many Malaysians, Python can be used to perform financial analysis, creating a trading strategy and to build automated financial tools.
  • Machine Learning – Machine learning is the basis of artificial intelligence as we know it. It focuses on getting programs to access data and learn it.
  • Data Mining – The primary use of Python is for data mining and data analysis. Some corporations use it to collect data from purchasing habits from their customers and even customer journey patterns.
  • Text Mining – Python is also used to scrape text data. One particular case was to scrape Twitter text updates to find out information about users.

So Now What?

So what are you going to do now?

Are you going to spend the next 10 minutes, feeling inspired by seeing all these applications that can be done with Python and get back to your daily lives?

Even if you’re not a programmer, individuals from all walks of life, no matter the job role, can make use of data science skills.

If you want to ACTUALLY increase your net-worth, no matter your industry, turn your skillset to be ready for our data-driven world. Everything you touch today involves data.

Launch your Facebook app, and immediately you’ll have your personal data recorded & collected for future advertising purposes. Take a drive along the roads and you’ll have CCTV cameras collecting data of your road usage. Take a stroll at the park and your phone records your location and the steps you took.

The point is, data is a major part of our lives today. The question is, whether you want to be the person who sits in it and have every piece of data collected of you, or do you want to be the person who creates systems to collect & analyze data.

If becoming fluent in Python and data science sounds exciting to you, then I recommend you check out Learn Python for Data Science, our 2-day physical course from 24th – 25th March, at Six Hatch, Petaling Jaya.

— Edmund Hee

P.S  — Leave a comment below and let me know about one Python application you can think of. I’d want to hear from you.