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  • Writer's pictureKelly Adams

How I Would Learn Data Analytics (If I Could Start Over)


A woman is at a desk writing in a notebook. She is looking at her phone as well.

I've been began data analytics for over a year. It began with the Google Data Analytics Course on Coursera. The course was a great introduction to data analytics. I learned what a data analyst does along with the basic tools used. Afterwards I supplemented my learning with online courses from places like Udemy. I've also: created projects, produced content and have connected with others in the data community.

Since the start of my journey I've learned plenty. Not only with my own journey but what I've heard from others in the data community on LinkedIn. I've already written a general path should be for new data analyst. You can read the article on that: Learning Path for Data Analysts.

I'll be discussing what I did and what I would do differently if I could start over. Below are the different stages of my learning. Each will include what I did and what I would do differently.

The Google Course

What I did:

I took my time with the course, it took me about 6 months to complete the course. I wanted to take notes and absorb the information as much as I could. I didn't want to rush through the course. I saw this as a fun, introduction course and nothing more. I didn't think I would completely change careers after this.


What I would do instead:

The course is a good introduction course. Great for someone who is interested in learning more about data analytics. But I do have critiques. I felt like it didn't prepare me for the portfolio project at the end. Especially with the lack of tableau practice and R practice. It felt like you were thrown into the project at the end. The only reason I could code using R was because I learned it in college.

I would shorten the time it took for me to finish. Maybe around a month or two at the most to get all of the information. Focus on understanding the fundamentals. This isn't about mastery. It's an introduction course not a fully comprehensive one. This alone won't get you a data job. Afterwards you can supplement your learning with other courses.


As for the portfolio project I wouldn't spend so much time on it. While I'm proud that I went through a whole analysis that included: basic analysis in Excel, a deeper analysis using R, and creating a dashboard using Tableau. It took way too much time. At most this should have taken a month but it took me three. I was so focused on perfectionism which slowed me down.


Supplemental Learning

What I did:

I knew I would need more courses after taking the Google course. I felt like my skills were lacking, specifically in SQL and Tableau. I looked up on Google "the best courses to take for [insert skill here]." I looked up the best courses for SQL, Tableau, Power BI, and Python. I signed up for 5 courses at once. I wanted to learn everything I could at the same time. I had no focus and tried learning three skills at once and would move back and forth between the courses.


What I would do instead:

I would pick one skill to focus on either SQL or a data visualization tool like Tableau or power BI. And pick the top course for it and stick with it. Not trying to do it all once. My focus was spread out in many directions while never making any progress in any skill. I would also focus on consistency. Instead of trying to practice for hours sporadically. I would commit to 15 minutes a day and increase the time as I stuck with the habit.

Projects

What I did:

I had no specific method for what projects I did. I originally wanted to make several capstone projects like I did for the Google Course but realistically I knew it wouldn't happen. It took too much time for each. Instead I created several mini projects. These were guided projects. Projects someone else had already built and I followed along at the time. Which were fine at the time because I need to practice my skills. While that can be great for learning, as a portfolio project it might not be as impressive. It's kind of boring doing the same projects as everyone else. Like the Titanic data set project.


What I would do instead:

Create a project for each specific. After each course on a specific skill (e.g. SQL) I would create a mini project to showcase my abilities. Once I finished a project with one skill I would move on to the next. These projects would be unique. There are two types of projects I would recommend: solved a problem you have or a business problem. Below are the benefits of each:

  1. Solving a personal project. You're going to be passionate about the project. It's easier to talk about the project when you care about it. You also showcase your enthusiasm for the work. It's also a great conversation starter because it's most likely a unique project.

  2. Solving a business problem. You're showcasing your skills as a data analyst and giving a real-world example of how you could potentially solve a company's problem using data.

Content creation

What I did:

I had no specific intentions with my content creation. No plan or trajectory. I wrote about whatever interests me which was all in the same umbrella of self-improvement. I didn't have a strategy like posting at the same time or connecting with others. Sometimes I'd post randomly and not stick around to engage with my audience. I didn't really pay attention to what post did well.

What I would do instead:

For job seeking purposes I would focus my content more. It doesn't have to be an incredibly niche topic like data analytics in the law field for a paralegals as an example. But focusing on data analytics and how you can prove and learn those skills is better. From a job seekers point of view. It demonstrates to potential employers that you not only have the skills (with projects) but the ability to communicate your findings with others (content creation).


Networking

What I did:

I think one of the things I did well was connecting with others in the data community. Note I did all of this using LinkedIn. When I connected with people I focused on how I could help them and not what they could do for me. The person gets to know me beforehand and we build an actual relationship. Usually for more popular data content creators it gets tiring when people constantly them questions or for favors. You need to build rapport a relationship with people first. I commented on other posts often whether or not the person was a popular data content creator. This was how I met most of my connections.


What I would do instead:

I enjoy coffee chats because I use it as a way to get to know my network better and practice my conversational skills. I would ask more people for coffee chats but this is after I've been connected with them for a while. I would also be more selective about who's in my network on LinkedIn. Not to be a snob but to find a balance between adding everyone who wants to connect and never adding anyone. I want to expand my network but at the same time I want to make sure whoever I'm adding at some sort of value to me or I help them in some way.

Conclusion

Those are all my lessons about everything I've done to transition into the data analytics field. Below is a summary of what I'd do instead if I had to start over:

  1. Google Data Analytics Course - take less time on the course and project, focus on getting the fundamentals down less on mastering any skill

  2. Supplemental Learning - focus on mastering one skill at a time (e.g. SQL or a data visualization tool) by taking a supplemental course.

  3. Projects - After improving on one skill that build a mini project to demonstrate my ability in said skill. Make sure this project is as unique as possible.

  4. Content Creation - narrow down on a niche and practice your writing skills

  5. Networking - find a balance between having an open network connecting with others but also adding people who had value to you

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