There are so much information around what you need to learn in order to be a data analyst or to just get started in data roles. Here’s a list of skills you need to get into data analytics. This is from my experience, but you should not over-complicate the process, you learn only what you need, you get good at it, and you’ll be set for a entry level data role.
1. Learn SQL
What is SQL?
Why Learn SQL?
It’s been so many years since SQL was invented (1970s) and people started using it. It’s used til this day. There are so many tools out there including new and old ones, but they always have a way to work in SQL because it’s adapted in the industry so well and many people are already familiar with it. If you see any job posting for roles such as data analyst, BI analyst, and data engineer, you’ll definitely see SQL in the job description. And that’s why SQL is a must-have skill.
Make sure you learn important relational database concepts as well as data modeling/warehousing concepts as you learn SQL. I don’t mean things like “joins” or “select” statement in SQL when I say database concepts. Here’s a sample list:
- Grain of data/table
- Primary keys, Foreign keys, Composite Keys
- Relationships between tables (e.g. many-to-one, one-to-many, one-to-one, etc)
- Data types for a field/column
- Dimensions tables and Fact tables
- Table vs view vs materialized view
2. Learn a Business Intelligence Tool
You’ve probably heard Tableau or Power BI. They are called business intelligence tools (BI tools). Business intelligence can be translated as “decision making system”, meaning they’re for decision making through data visualizations.
Every data project involves getting your analysis visualized so that others can benefit from it. And this is where BI tools come into play. BI tools allow you to create a business friendly data model (semantic model/layer) and visualizations to drive insights. BI is pretty big that there are so many roles that are specific BI tool focused (e.g. Tableau developer). You can totally make your career out of BI tool if you want to.
In terms of which BI tool to pick up, this totally depends on your preference. What’s important is that you get good at one BI tool so that when you need to use another, you’ll have a good grasp of BI and data concepts by then. There are pros and cons for each tool and some have free tier and some don’t. A few free options are: Tableau, Power BI, and Google Data Studio. You could use open source tools as well but ones provided by vendor usually have an easier process to set it up.
In order to build effective visualizations, you need to learn what’s good and bad. I’d highly recommend reading at least one or two books on data visualization. You’ll be surprised how much you’ll gain an understanding of how to read/build good data visualizations. Check out another blog post on the data visualization books I recommend.
3. Build a Portfolio or Get Work Experience
Your potential employers need to trust your skills that you can do the job. Essentially, what you need is real-world experience. There are only 2 ways to prove that you have the skills:
- You get work experience. Whether it be an internship or volunteer data role, having worked in the industry definitely gives you an advantage over others that don’t.
- The other option is to build a portfolio. So many people think your portfolio needs to be perfect, but don’t even think about making it perfect. Because it’ll never be perfect. What’s hard is getting started in creating one, so just go get a free wordpress or wix website or whatever tool you want to use and create one. Many companies offer you a blog or website for free. If you want to do more, then you can go get your own domain at company like Namecheap.
Don’t forget to create accounts on social media such as LinkedIn and Twitter to share your work!
4. Learn Python or R (optional)
Python and R are the programming languages that are most used in data analysis. They’re both also used for advanced analytics such as Machine Learning and Deep Learning. Python is very popular in data engineering type tasks as well (R can be used, too though). I’d personally recommend learning Python if you don’t have any preference and go with R if you have specific reasons why you want to use R.
They both have packages/libraries you can utilize to work with data. e.g. pandas package is available in Python and tidyverse package for R.
I’d say learning Python/R is optional because most entry level data analyst positions should only require the use of SQL and a BI tool. If you’re only looking to get into more technical roles such as data engineer, then you should learn it.
The most important pieces are the first 2, learning SQL and a BI/reporting tool. What I’d say is that if you want to get into data analytics, focus on SQL a lot along with database concepts. And learn to be decent at a BI tool while learning about data visualization best practices.
I omit the importance of communications in this post because it’s a prerequisite for any data role. You should be brave enough to ask questions that you may think are dumb. You should be able to communicate clearly enough what you’re doing to your colleges and users.
Lastly, I would like to encourage you to keep going or get started if you’re really trying to get into data because it’s totally doable if you put in efforts. The more time you spend on something, the better you get at it.
Hope this summary is helpful! Drop a comment if you have any questions you may have.