Picture this

You studied data science for years, hoping to break into the industry. Finally, after countless rejections, you hear back from an interviewer. You’re getting hired. All your hard work finally paid off.

While this sounds like a really happy ending, the story doesn’t end here.

Months into working for the company, you feel demotivated, drained, and tired. Your manager is constantly breathing down your neck because sales are low. The models you build aren’t converting into purchases.

Finally, you decide you can’t handle it anymore. You start looking for a new job and hand in your resignation letter.

Sounds scary, right?

I know.

…

Introduction

Customer segmentation is important for businesses to understand their target audience. Different advertisements can be curated and sent to different audience segments based on their demographic profile, interests, and affluence level.

There are many unsupervised machine learning algorithms that can help companies identify their user base and create consumer segments.

In this article, we will be looking at a popular unsupervised learning technique called K-Means clustering.

This algorithm can take in unlabelled customer data and assign each data point to clusters.

The goal of K-Means is to group all the data available into non-overlapping sub-groups that are distinct from each…

I taught myself data science through online courses, books, and YouTube videos. After almost a year of self-learning, I am now working as a data scientist.

Somewhere along this journey, I’d often find myself lost in self doubt.

I read countless articles emphasizing that the only way to break into data science was to gain a strong grasp of statistics, mathematics, linear algebra, and predictive modelling.

While this is true to some extent, it has led to an assumption that only a data science Master’s graduate can become a data scientist.

How much math is required for a data scientist?

Looking for an entry-level data science job is difficult. I like to call it the chicken and egg problem, because nobody will give you a job without experience, and you can’t gain experience without a job.

Most of the messages I get on LinkedIn are from people asking me how they can overcome this problem. The gist of the messages is as follows:

“I know Python and SQL, and have taken online courses in data science. It is still really difficult for me to secure a job in the industry. …

Why code along to machine learning tutorials?

There are two approaches you can take to study any topic — top-down, and bottom-up.

When you learnt subjects like math and science in school, you would’ve been taught using the bottom-up approach.

First, you were taught the foundations of the topic, and then gradually progressed through the material.

However, when learning subjects like data science and machine learning, the top-down approach is often easier to grasp — especially if you don’t have a strong mathematical background.

With the top-down approach, you can first get your hands dirty by implementing machine learning models.

This is easy to do due to the number of packages available in languages like Python and R.

The democratization of machine learning…

As an aspiring data scientist, you must have heard the advice “do data science projects” over a thousand times.

Not only are data science projects a great learning experience, they also help you stand out from the crowd of data science enthusiasts looking to break into the field.

However, not all data science projects help your resume stand out. In fact, listing the wrong projects on your portfolio can do more harm than good.

In this article, I am going to walk you through the projects that are must-haves on your resume.

I will also provide you with sample datasets to experiment with for each project, along with associated tutorials that will help you complete the project.

Skill 1: Data Collection

You have taken a data science MOOC and completed all the lectures and assignments. You even got a certificate. Now what?

Once I completed my first data science online course, I was confused as to what to do next. I didn’t know what course to take next, or how I could apply concepts learnt to real world problems.

I spent a long time looking for an intermediate level data science course that would help be bridge the gap in my understanding, introduce me to case studies, and strengthen my understanding of statistical concepts.

It took me almost a year to realize that there was no one course that could teach me these things.

I created a roadmap outlining the concepts I wanted to learn, and started looking for resources that covered those specific topics.

These resources helped me strengthen my theoretical…

We have seen great advancement in Artificial Intelligence in the past couple of years. It has applications in industries like healthcare, fashion, education, and agriculture, and is predicted to be one of the next big digital disruptions.

As Andrew Ng puts it:

Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don’t think AI will transform in the next several years.

However, AI is a tool that can be misused. …