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  • Writer's pictureKelly Adams

My Journey Towards a Data Career (Update)


A screen showing various financial data metrics. The ones on the right are CIR and Quality Score.

It's been over a year since I've taken the Google Data Analytics Course. I took the course because the topic seemed interesting. I wanted to expand my knowledge. I didn't expect to fall in love with data analysis. Since then I've dedicated myself to a career in data. Specifically looking for a data analyst position. If you want to read my review on the Google Course you can view it here.

When I first started out I wrote this article, My Plan Towards Becoming a Data Analyst. Which included my goals and specific skills I wanted to learn. I also wrote a separate article called, Learning Path for Data Analysts which gives advice for aspiring data analysts. Specifically career switchers with no prior experience in the field. Today's article is an update to my original article, My Plan Towards Becoming a Data Analyst. I'll be talking about what I've done since then and what I've learned about the field. This is an ongoing series on my blog where I re-write old posts and update on what's changed since then.

Original Plan

First let's go into my initial basic plan of becoming a data analyst. I wanted to learn the following skills:

  • Excel

  • SQL

  • Tableau

  • Python

How was I going to do this?

  1. Finish the Google Data Analytics Project;

  2. Work through online courses from sites like Udemy to strength my skills;

  3. Learn Python;

  4. And finally add projects to my portfolio.

After that I would apply for jobs and try to get referrals from mutual connections.


How did that go?

I'll be the first to admit the plan above was pretty basic. After spending time on LinkedIn and connecting with other data analysts like Annie Nelson, Jess Ramos, and Megan Lieu. Who are some of my favorite data content creators. I realized there were problems with my plan:

  1. It wasn't specific, what does it mean to "learn SQL" or "learn Tableau". How would I do that? What was the specific plan?

  2. There was no clear timeline. How long was it going to take me? Did I have a deadline I wanted to meet?

  3. No plan for learning. I didn't actually set a specific plan or decide when I would spend my time learning. I had no routine to stick to.

Since then, I've answered all of these questions. I created a specific plan, with a timeline, and developed habits to cultivate my learning time.

What I did


Learning Data Analytics Skills

For learning specific skills like SQL here is my general learning path, which I also wrote about in a LinkedIn Post.

  1. Learn the basics of each skill from online courses that you can find on Coursera, Udemy or through sites like Datacamp.

  2. From there I would complete mini projects or work on practice problems. For SQL using sites like Hackerrank, Stratascratch, or DataLemur with specific SQL interview questions.

  3. Finally, for each skill (SQL, Tableau, Excel, and Python) publish one project with an accompanying article. The article didn't happen on all of them though.

    1. SQL - Weightlifting Project (at work in progress);

    2. Tableau - Google Capstone Project (view the article here);

    3. Python - Restaurant Picker (view article here);

    4. Excel - Maven Magic Challenge (no article on this project).

This has been a great 3-step way for me to not only learn (the basics) of a skill, but be able to implement my knowledge and work on real projects.

My Timeline and Learning Plan

To answer the other questions about my timeline and learning plan.

  • Finish with at least 5 portfolio projects by September 30th.

  • Commit to 30 minutes of learning data analyst skills on weekdays. I completed this during breaks in my workday or after work.

Networking

Next, I wanted to work on building my network and meet other data analysts. For this I primarily used LinkedIn. Below is my plan:

  • Every weekday (I take weekends off from social media and content creation). I would comment on at least 5 posts. These were insightful comments that said more than "thanks for sharing" or "insightful". You can read my process for commenting in this LinkedIn post

  • I also reacted to at least 10 posts

  • I connected with at least one new person in the data field

  • And the past month I've started working on coffee chats, basically informal chats with professionals in your network. To get to know each other better. You can read more about my method for coffee chats here.

  • I also publish original content twice a week. This lets me get my name out there (in the data community) and I get to meet some great content creators. Like Christian Wanser, Matthew Blasa and Thais Cooke. I also met other aspiring data analysts and professional data analysts through my content.

Content Creation

In addition to creating content on LinkedIn I have been creating content on my blog. Which let me dive into more detail about various topics in data analytics. I wanted to showcase my skills in writing and communication specifically. But also demonstrate my knowledge and passion for learning about data analytics. I wanted to stand apart from other job seekers. It's also become a fun way for me to express my thoughts. It has been helpful creating articles for commonly asked questions I get on my LinkedIn like: How do I become a data analyst? Or what projects I should do?

How it's going

Finally the job searching process. Here's my method for searching for a remote data analyst job. Note, as of writing this I'm still looking for a job. Once I (hopefully) get a position as a data analyst I will either update this article or write a new one. But here's my job searching process.

  • 50% of my time I use EasyApply on LinkedIn or on sites like Indeed or ZipRecruiter, basically where only a resume is required. This method is focused on quantity rather than quality. I'm trying to get as many job applications out there as possible.

  • The other 50% of my time I tailor my resume and write a cover letter to job applications. These are typically ones that I am referred to by someone or I apply directly on their company website. This is focusing on quality > quantity. I only get about 2-4 of these applications done a week

What I've Learned

Here's a few things I've learned since starting my transition to data analytics.

Focus on one tool at a time

There's no need to spread your time and energy trying to learn 5 different tools at a time. You can either be like me and learn one tool after another (if you're interested in a more generalist approach). Or you can focus on two or maybe one tool like SQL and Tableau.


Take breaks for yourself

Your mental health is important. We are all human and we shouldn't except to work constantly without breaks. Take breaks when you need them. Whether that's short ones like going for a walk or going to the gym. Or longer ones where you take a few days off from job searching or upskilling.


Build Relationships

When building a relationship with someone don't focus on what they can do for you . Focus on fostering the relationship. People get tired (especially those with a bigger following) of people asking for things when they meet. People want others to get to know them first before asking for favors. If you're new to networking don't start off an interaction with these questions:

  • "hey can I get a job?" (unless you're emailing a recruiter, even then there's better ways you can message them, see this post from Jess Ramos about how she messages recruiters).

  • Or "can you help me get into data analytics?" (take time to view their content first and see if they've posted about it, they probably already have and are tired of getting the same question, or don't have time to answer).

  • "Do you have a few minutes to pick your brain?" This question has negative connotation. Instead ask for coffee chat instead. If they say no that's okay and either move on or work on building the relationship

Realize finding a job takes time

Don't get discouraged by seeing everyone else's posts about finding a new job instantly. Most likely it took them a while. There were probably times they felt discouraged, unsure, or hopeless.

Conclusion

It's been over a year since I've decided to become a data analyst. I've improved my skills in SQL, Tableau and Python. And created several projects demonstrating my knowledge. But the best part of the journey has been connecting with others in the data community. Almost everyone I've met have been welcoming, friendly and encouraging. It's one of the best professional communities I've been a part of.

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