Landing a Job After Taking Data Analytics Course

 

Getting into the data analytics field is plain difficult.

 

You hear people talking about the rise in its demand, the higher salaries (the main driving factor here!), and the cool nature of a tech job.

 

Industries are relying heavily on data analytics for their smooth functioning, and it’s no secret!

 

But it’s definitely not easy to land a job as a data analyst. When I decided that I wanted to switch careers and become a data analyst, I had no idea how I was going to make it happen.

 

My degree was in Biological Sciences, and while there were some analytical components to that field, it wasn’t the same as a specialised data analytics degree.

 

Thankfully, I developed my own way of acquiring new data skills quickly and found an excellent course that taught me everything I needed to know about data analytics.

 

And then, just <6 months after finishing the course, I landed a job as a data analyst in the healthcare sector at a government hospital!

 

In this blog post, I’m going to share with you how I did it so that you can follow in my footsteps.

 

Read on for more!

 

How I Landed A Job After Taking A Data Analytics Course (No Degree!)

 

1) Getting Exposure to Data

 

We all have some kind of exposure to data in one way or another, whether that be in your statistics class in university, or just the Elementary Mathematics course in your Os!

 

For me, my first encounter with data on a deeper level was when I took a course on Biostatistics in the first year of my university course. This taught me the basics – the types of data, the 6Vs of data, and basic data visualization skills.

 

While this knowledge was really basic, it helped me to build a good foundation on which I can build upon for accumulating more data analytics skills in the future.

 

I encourage you to listen up in any statistics classes as they are a good start to your learning journey!

 

2) Learning the Data Analytics Tech Stack

 

Learning the Data Analytics Tech Stack

 

At this point, I was really interested in learning more about statistics but had no programming experience. I knew that coding was going to be important in a world where tech is king.

 

As such, I started finding out more about what skills I would need to learn as a Data Analyst. I call this the Data Analytics Tech Stack.

 

The Data Analytics Tech Stack is a group of specific tools that all data analysts need to master, as they are highly used in the data analysis process.

 

My Data Analytics Tech Stack

 

  • Microsoft Excel
  • R programming language
  • Python programming language
  • SQL
  • Tableau

 

Microsoft Excel

 

If you’re completely new to data analytics, you may only recognise Excel. While it may sound easy to learn, you’ll need to master how to make pivot tables, perform data cleaning, and more using this tool. This tool is required for quick analyses when time is tight!

 

R Programming Language

 

The R programming language is a programming language that’s very frequently used in data analytics – especially in the research world, biosciences, and the healthcare industry.

 

Python Programming Language

 

The Python programming language is a programming language that’s very frequently used in data science and machine learning – its easy deployability is best for MNCs and startups.

 

SQL

 

SQL – short for Structured Query Language, is a language used to pull out data from databases. A very important skill to learn for MNCs and companies with large amounts of data.

 

Tableau

 

Tableau is a commercial data visualization software that produces beautiful graphics with a few clicks. This tool is actually more important to learn than programming languages (contrary to the layman’s opinion!)

 

Note that you may need a decent laptop for data science if you’re attending the lessons, as I’ve seen some others face lagging issues.

 

Okay, I get it, it may be intimidating to learn all this if you’re new to the data world. Don’t let any data analytics terms scare you. Here’s how I took to acquiring data skills rapidly.

 

3) Accelerating My Learning With Smartcademy’s Data Analytics Course

 

Why learn data skills by yourself when you can get help? Learning to be humble and enrolling in a data analytics course can help accelerate your learning.

 

I went online to search for institutions offering data analytics courses conducted by local instructors and found Smartcademy’s Data Analytics course.

 

If you’re self-sponsored, it’s up to 90% IBF subsidy as an eligible Singaporeans or PRs. You can even use SkillsFuture credit to pay the remaining balance!

 

The course is 100% delivered via zoom, and it was conducted over the course of a month on the weekends.

 

I found the course outline to be great because I saw that it provided training in Python, SQL, and Tableau – the 3 skills I wanted to acquire in my Data Analytics Tech Stack.

 

Man, was it really challenging yet insightful! The content taught within the course was very comprehensive, which came with many resources provided in the form of slides, Jupyter Notebooks, code, and other digital products.

 

The pace of the course was relatively quick, but I found that I could still follow along well, because the Teaching Assistants and instructors will try to regulate the speed of the teaching according to the class.

 

I would say that beginners can take this course without any prior experience, but I suggest that you download and familiarise yourself with the software interface first before the lesson. (The Smartcademy team helps out with that!)

 

What I took the most from the course would be the Capstone Project at the end. We had to make and present a series of data analyses to the course instructor, who will then comment and critique our visualizations.

 

What I liked about the Capstone Project was that we had the freedom to experiment with any skill we learnt during the course.

 

We could plot using matplotlib libraries in Python or just use the Tableau software. I did my project on video game analyses, as I’ve always been the biggest video game nerd.

 

Note that you need to finish the Capstone Project and achieve 75% attendance to get the Certificate of Completion!

 

I’m proud to share that I got a Certificate of Completion with Distinction!

 

I felt so much more confident in Tableau after I completed the Capstone Project, which later on helped me SO much while developing and maintaining dashboards as a data analyst at SGH.

 

4) Building My Data Analytics Portfolio

 

Building My Data Analytics Portfolio

 

It was during my year 3 out of 4 years in university that I started becoming really serious about data.

 

I had to slowly build up my data analytics portfolio with experiences and skills that matter to employers.

 

Beyond the course that I took with Smartcademy, I took to building up my own personal projects that I could show future employers. You’ll want to have some data science project ideas in mind before you begin working on them.

 

Here are the projects I have built up over time, and I suggest you do the same too:

 

  1. Smartcademy’s Data Analytics Capstone Project
  2. Tableau Public Profile
  3. Rpubs Profile
  4. Github Page
  5. Online Portfolio Website
  6. LinkedIn Profile
  7. Personal Blog

 

By doing a variety of data mining projects, data science analyses on your own, and data analytics projects based on your passion, I built an online presence that I could show to future employers. I recommend you do them too!

 

I personally did one analysis on birds using Tableau skills from the course and it was well received during one of my interviews!

 

5) Stacking Up On Internships & Experience

 

Armed with the knowledge from learning so much from projects and courses, I thought it made lots of sense to rack up internships as soon as possible.

 

Before I took the Smartcademy data analytics course, I only knew minimal knowledge about R and excel, and basically no knowledge of Python, SQL, and Tableau.

 

Learning about the 3 skills (Python, SQL, Tableau) from the Smartcademy Data Analytics Course, I went on to apply for a 6-month internship with IMDA (Infocomm Media Development Authority).

 

During the internship, I applied the Python skills I learnt and coded a GUI that could help automate data analytics processes in the tech research team.

 

I also challenged myself to utilise the SQL skills learned from the course and made a database for storage of large amounts of data to replace flat Excel files.

 

Pro tip: You should learn to communicate your data analyses well to your colleagues! I realized that not all of them are as knowledgeable as I was about data.

 

6) Cycling Between Learning, Practising, and Gaining Experience

 

This has to be the most important part of the data analytics journey that I took to land a job at SGH.

 

In order to develop data skills well, you need to learn how to cycle! (Not using a bicycle)

 

I cycled between learning data skills, practicing them using personal projects, and applying for internships to gain experience in them.

 

Learn -> Practise -> Gain Experience -> REPEAT!

 

You’ll need to repeat this process for each tool/skill in the data analytics tech stack to really develop the skills well. It’s like training each muscle group in the gym – it helps build up a robust repertoire of data skills.

 

That also means if you need to take more data science online courses, please do! They will help you out when you finally land that data science interview and get that dream data analytics/data science job!

 

Final Thoughts

 

I hope this article has helped to show how you can land a job as a data analyst without any analytics degree!

 

The key is to really focus on building up your skill set, experiences, and portfolio in data analytics as data analytics really benefits businesses.

 

If you’re stuck on where to start, I suggest checking out Smartcademy’s Data Analytics Course – it was how I got started on my data analytics journey!

 

I wish you all the best in your data analytics endeavors. Thanks for reading! 🙂

 

Author Bio

 

Justin Chia is the founder of Justjooz. He seeks to educate everyday people about crypto, tech, analytics, and home improvement.

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