Choosing to become a data scientist

Basty’s Scrapbook

Hi, everyone! My name is Basty, and I’m a data scientist and educator from Metro Manila. If there’s something I value so much, that is education. Education has allowed me to not only dive into the amazing world of code and data, but also to encourage and inspire others to do the same. Read more about me here.

The Journey continues...

Welcome back to the continuation of last month’s blog on starting your Journey into the World Data Science. Since we’ve already tackled how to get started in the world of data, it’s now time to decide on which pathway/career you want to pursue. We’ll be mainly talking about 3 possible careers you can take: data scientist, data analyst, and data engineer. Specifically for this blog, we’ll be focusing on becoming a data scientist. Let’s get started!‍

What is a data scientist?

Let’s first start off by defining what a data scientist is. Oxford defines a data scientist as someone who analyzes and interprets complex data in order to assist a business in its decision-making. This definition basically encapsulates what being a data scientist already is about, but I would define a data scientist somewhat differently.

A data scientist is a detective whose job is to gather all the clues (data) and connect them all together (analysis) to solve a mystery or problem (business problem). How exactly? That’s up to you as long as you utilize the tools available out there! 

Responsibilities

Now that we’ve defined what a data scientist is, let’s now take a look at the different responsibilities you’ll be taking if you decide to take on this career. 

Usually, you’ll be hearing the term “data cleaning” in this career a lot, and that’s because most of the work of a data scientist falls under cleaning and manipulating the data. Data scientists usually do this step to prepare the data to use it for predictive modeling using machine learning!

A lot of data scientists focus mainly on the technicalities of the job; however, to be a great data scientist, it's important to master the skill of storytelling. After doing the analysis, usually you are supposed to present a story to the stakeholders involved in solving the business problem.

Here’s a real-life example of what you can expect in the industry: Let’s say your manager asked you to figure out how the company can lessen the time that it takes a customer to fill up an RFQ (request for quote) on the website from 30 seconds to 15 seconds. As a data scientist, it’s now your task to first figure out what is the root of the problem, and identify how solving this problem can bring value to the company. You first gather the necessary data needed, clean and manipulate it, and do some exploratory data analysis to uncover some insights. After doing some analysis, you would then build a machine learning model that would better predict product categories, and in return lessen the amount of time to fill up an RFQ. 

As you can see, being a data scientist is all about solving business problems through the use of data. This is just one practical example of what data scientists do, and there are a countless number of problems you can solve with data science. 

Tools and Skills

We’ve talked about the different responsibilities of a data scientist, now let’s talk about how you can carry out those responsibilities. Being a data scientist entails being interdisciplinary. So here are the different skills you need to possess:

• Python/R Programming - This is your number 1 tool for gathering, cleaning, manipulating data, and building machine learning models.
  
• SQL databases - You’ll be needing SQL to interact with databases
  
• Statistics - Mainly, you need to learn descriptive and inferential statistics
  
• Data Visualization/Storytelling -  You must learn how to effectively communicate your insights to stakeholders
  
• Machine learning - Supervised learning, unsupervised learning, and reinforcement learning

There are also tools that will help you learn and implement these different skills:

• Pandas, NumPy (Python) - for data cleaning and manipulation
  
• MySQL, PostgresQL (SQL) - for interacting with databases
  
• Tableau, Power BI, Matplotlib, Seaborn - for visualizing insights
  
• Scikit-learn, TensorFlow - for building machine learning models

Becoming a data scientist

As someone who is still starting out in the world of data, there are different ways for you to become a data scientist. 

• Getting a Bachelor’s Degree - If you are still a student, getting a degree in data science or any relevant field will help you learn in a structured format. In addition, having a degree in data science will also help you in getting a job.
  
• Earn certifications - If you don’t have the means to get a degree, getting tool and skill-specific certifications are a great way to show that you’ve touched upon the relevant skills needed for the role
  
• Internships - Internships are probably the greatest way for anyone to get immersed in the world of data. By joining internships, you’ll be able to learn hands-on what exactly being a data scientist is like.
  
• Bootcamps - An intensive program that usually lasts for months designed to prepare you for a data science role. It is somewhat similar to getting a Bachelor’s degree, except this one is more intense.

Alright! To recap, we talked about:

• What a data scientist is
• The responsibilities that come with the role
• The skills and tools needed for the role
• How to become one. 

But my final call to action for everyone is to just go out there and start wearing your detective hat. Find a dataset that you’re interested in, and some analysis with it. Don’t delay your journey on actually working with data.   

Next steps

Lastly, speaking of bootcamps, if you want a more structured—and at the same time—social approach on learning data science, make sure to check out the 15-week Data Science Fellowship of Eskwelabs. Aside from the portfolio of projects you will make, you will also benefit from the bootcamp’s 1:5 mentor to student ratio. 

I hope you found this blog post helpful. See you in the next one!

Never stop learning, and see you in the industry!