When I walked into my first data science class at university, there was one thing that stood out to me more than anything else. The lack of female students in the class.
There were only around five girls in a class of 20 students, which I found pretty strange. My classes at school had approximately an equal number of boys and girls, and this was the first time I found myself in a male dominated environment.
After being in the field for a couple of years, I am now used to it.
Most of my classmates, lecturers, and advisors are male. …
When I first started my data science internship, I didn’t really know what I was getting myself into.
I had only worked on personal projects up until that point, and I wasn’t at all familiar with what the working environment would be like, the data I would have to work with, and the tools I’d have to use. I was told that strong Python and SQL skills were a requirement for the position, and spent a couple of weeks brushing up on my programming and querying skills. …
I was 16 years old when I first entered the field of data science. I vaguely remember sitting across the hall from my dad, and being given a list of career options to choose from.
Around an hour later (and him telling me that pursuing a degree in journalism was a terrible idea), we settled on a career path.
At that time, I had absolutely no idea what I was getting myself into. I was the least technical person I knew, with absolutely no idea of anything computer related.
A few days later, I enrolled into my university’s computer science program, and chose to major in data science. …
A data analyst uses programming tools to mine large amounts of complex data, and find relevant information from this data.
In short, an analyst is someone who derives meaning from messy data. A data analyst needs to have skills in the following areas, in order to be useful in the workplace:
As of 2020, many companies have adopted big data and data analytic technologies. Due to this, the demand for data analysts is skyrocketing. In fact, many studies predict data analytics to be the job of the future.
Before I go into the detail of how you can make a career transition into the field of analytics, I am going to break down the field for you. Many people have misconceptions about what the field of data analytics is, and it is often confused with the field of data science. …
In the year 2012, Harvard Business Review called data science the “Sexiest Job of the 21st Century.”
There are thousands of videos and articles all over the Internet that paint a beautiful picture around the field of data science.
They tell you about the thick pay-checks and flexible working hours the field offers.
“All you have to do is learn skill A, B, and C,” they’ll say. “Sign up for a bootcamp and do three online courses. Then you’re ready to get a job in data science!”
Due to the hype around data science, many people end up having unrealistic expectations on what the field really is about. …
A couple of months ago, I heard a horror story about how data scientists were faced with unrealistic expectations.
The story featured two characters — a stakeholder, and a data analyst.
The stakeholder asks the analyst to find a useful rock from a mountain. The analyst, in turn, asks the stakeholder if there is any specific type of rock he wants.
The stakeholder says no.
After days of hard-work and mining, the analyst comes across a big, beautiful rock. Content and pleased with herself, she returns to the stakeholder and shows him the rock she found.
To her dismay, the stakeholder was unhappy. “It is a nice rock,” he says. “Just not quite what I wanted.” …
Sentiment analysis is a technique that detects the underlying sentiment in a piece of text.
It is the process of classifying text as either positive, negative, or neutral. Machine learning techniques are used to evaluate a piece of text and determine the sentiment behind it.
Sentiment analysis is essential for businesses to gauge customer response.
Picture this: Your company has just released a new product that is being advertised on a number of different channels.
In order to gauge customer’s response to this product, sentiment analysis can be performed.
Customers usually talk about products on social media and customer feedback forums. This data can be collected and analyzed to gauge overall customer response. …
When trying to learn anything all by yourself, it is easy to lose motivation and get thrown off track.
In this article, I will provide you with some tips that I used to stay focused in my data science journey.
There are just too many resources available online.
Data science is a very deep field, with branches in statistics, mathematics, programming, and development.
Due to this, it is very easy to get sidetracked during the learning process.
There are so many online courses that promise to make you a data scientist in three months, and many students end up in tutorial hell. …
In this article, I would like to showcase what might be my simplest data science project ever.
I have spent hours training a much more complex models in the past, and struggled to find the right parameters to create machine learning pipelines.
Despite its simplicity, if I could only display one project on my resume, it would be this one.
Let me explain why.
As a child, I would always get excited about holidays because I could get gifts. (Just humour me here, I do have a point, I promise). …