A week ago, I would not have thought I had the chops to write something like this. I would have shrunken away from the idea. It took me helping someone navigate this space to realize that I had valuable understanding that could guide others who were considering doing the same.

It’s important to note that this won’t be just a single post. I’m going to compile a few different posts and make a separate page that you can navigate to from the main page of this blog.

One of the things I feel most passionately about with my own data journey is helping other people find where they feel best that they fit. I want to give them as much of a chance to arm themselves with knowledge and push into a space that’s probably the most inclusive of all of tech. That’s saying something, given that there’s still a wide gap ( Medium, Analytics India Mag, Wisconson.edu).

For the moment, my plan is to cover jargon, types of careers, and certifications and programs that can be joined to help expand your understanding. For those who hear about data analytics or data science and don’t know how to start, this is for you. A comprehensive series of posts on how to get going and which direction to take based on what sounds cool, what you like, or what would be the most engaging.

The good and the bad thing about a career in data is that there is so much available to you for free and low cost. If you want to go back to school, there are grants available for women and for people of color getting into tech.

Another thing worth mentioning is that a lot of women get pigeonholed into admin because of customer facing positions: reception, admin assistant, exec assist, customer service rep. And you’d be surprised at the overlap that admin has with data, which comes down to one thing, in my experience: Microsoft Excel (to a lesser extent, Google Sheets).

So if you happen to be transitioning from admin, you’ve already got a good foundation. As someone from Admin myself, that experience, the projects I’d need to do, the amount of tasks being done and streamlined through Excel, helped me become an expert in it over the years. That expertise, though in a different field, can help your transition into data.

There’s so much possible in Excel, from formulas to charts and regression, to predictions. Though I know plenty of people turn their noses up at Excel, it’s nothing to sniff at. Some big companies even still use it as their main source of data warehousing.

I do want to give each specific topic its own post, because it’s worth that, and there’s so much to cover, but I don’t want this post to be all fluff, either. This isn’t a cleverly disguised “follow me for more tips and tricks!” post.

To give a detailed rundown of what I’m planning to cover, I’ve created a list:

1. Solid Foundation: Breaking down jargon and identifying skills/knowledge you already have.
2. Universal skills: Dashboarding, visualization, reports, presentations
3. Career paths: How do you want to work with data?
4. Certifications and Programs: What to focus on for the path you want to take.
5. Personal Projects: Building a Portfolio

Some of this, again, is stuff I’ve only just done, and you’re on what’s acting as my portfolio!

I definitely feel a little self conscious about this endeavor. It feels a little like the blind leading the blind, but there’s no doubt I’ve learned a lot in the past six months, and I’m not sure I could have done it without a solid support system and an idea of where to go or what to do, what I’m interested in pursuing, and how to show I’ve got the chops for it.

I’m hoping that this will reach someone who needs it, and if not, I hope you have fun reading it!

I intend to post throughout the week on this series, I don’t want to drag it out, so keep an eye out for that.

With that being said, I hope that all of your data cleaning is swift and easy, and I’ll see you next week!