30 Days of Data Analytics: 1 of 30

Welcome to my #30daysofdataanalytics blog!

Background

I stumbled across the career field of data analytics right after my son was born, and the more I read about it and watched videos from other data analysts, I fell in love. I immediately started researching the skills necessary for the job, and I came across Alex the Analyst on Youtube. He spoke about the Google Data Analytics Professional Certificate as a great starting point for entering the field of Data Analytics, and I jumped in. I was also finishing a second bachelor's degree at the same time and adjusting to being a new mom, but I found a new passion - data.

As I was changing diapers and staying up late with my son, I completed the Google Analytics course in April. I went on to finish my bachelor's degree in Theology in June. Since I've finished university, I have much more time to dedicate to expanding my knowledge of data analytics, but I've been stumbling around looking for projects to complete and where to go next with data. Data is a huge field and I feel particularly overwhelmed most of the time. I feel so far behind so many others, and I can't seem to focus on one thing at a time. There are so many technical roadblocks and glitches when I try to follow a YouTube project. Excel formats matter a lot. I have a Mac so that limits the programs I can use. It gets frustrating.

Then I had an idea: a 30-day data analytics challenge. I will make a program to follow, and each day I will focus on that one thing. That's it. One thing. One skill. One project. Easy enough. And then I could write a blog each day about my experience with the project/skill/learning that I did. It would also help me to organize my projects and give me a creative outlet to write about my experiences, challenges and wins.

So, here it is. Day 1.

Day 1: Preparation

Here's my plan for this challenge. I'm going to spend 5 days on 5 different platforms: BigQuery to practice SQL, Excel, TableauPublic, R programming using RStudio, and Python using Spyder v.5.3.2.

I will be using various data sets from the BigQuery public data, Kaggle and Github, and I will cite the sources of each data set. I looked for datasets that interest me. I found some about coffee, population, hurricanes, and SEC quarterly financials.

Anyone who's observant would see that 5 days on 5 platforms only comes out to 25 days. So what am I going to do with those other 5 days? Day 1 is today, preparation. The first step to any data project is to familiarize yourself with the data, explore, search, plan, ask questions and get to know what the goal is. That's what I'm doing today. I've downloaded the datasets. I've done test runs with all the programs. I'm ready. The last 4 days of the 30-day data challenge is open, possibly to try some Kaggle competitions or go back and dive deeper into a previous project. I'm going to leave those days open and see where the data takes me.

Closing Thought

I'm so excited to start and commit myself to this 30-day challenge. I'm also very nervous because I'm putting myself far outside of my comfort zone. I have a million insecurities and doubts because I've never done something like this before. I've never studied programming or data science. I'm a complete newbie. But there's something exhilarating about learning about data analytics and seeing small successes. So I can't wait to share my journey with you. See you tomorrow!