Statistics is an incredibly powerful tool. It allows us to better understand the world around us by deriving patterns from data. In today’s world, statistics is being applied in almost every industry — in medical investigations, advertising, sales, and even law.
However, statistics and numbers can be used to lie. They can be used to exaggerate, blow incidents out of proportion, and push political agendas.
In the world we live in today, the consequences of misusing statistics can be disastrous. Misinformation can be spread like wildfire on the Internet, with claims that they are backed up by “scientific proof.”
All you need to do is pick one graph that backs up your agenda and post it on the Internet with a misleading title, and it will spark public outrage in no time. …
You have spent countless hours doing YouTube tutorials, taking paid online courses, and reading introductory programming articles. Yet, it feels like there is a barrier you simply can’t break through. There are people out there writing complex code you don’t understand, and solving complex programming problems.
“I can never become like them,” you think, awestruck. “How did they learn to do it?”
I’ll tell you one thing — they certainly weren’t born knowing how to code, neither are they more intelligent than you.
In this article, I will break down the steps you can take to overcome the fear of programming. …
As data scientists, most of our work is done in a Jupyter notebook. Whether it is building a machine learning model, training it, or using it to make predictions, our work usually doesn’t make it out of iPython notebooks.
While this is a great environment to create and test our model, what happens when we want to get our model into the hands of another person?
Let’s say we want a potential employer or colleague to use our model. Wouldn’t it be nicer to have a functional application for them to play around with?
An interactive environment that anybody can use easily is a lot more interesting than showing people a bunch of code lying in your Jupyter notebook. …
Data science undergraduate degrees are not common.
Most data science qualifications are at a Masters or PhD level, and candidates who pursue these fields already hold a degree from a quantitative background — such as Computer Science or statistics.
The university I’m attending was the first in the country to offer a data science program at an undergraduate level.
This was really exciting, since we would be the first batch of students in the country to graduate with a data science degree.
And with the increasing hype around the field and the shortage of data scientist, we would immediately be swept up by companies. …
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. …