What to learn to be a data analyst

Considering a career switch, or broadening your skill sets to ride on the data revolution? People with proficiency in data analytics skills are increasingly sought after. Even if you are not in a specialised data analytics role, these skills can help you to be more efficient and effective in your position.


Two years ago, fresh out of college with a general business degree, I dived head first into the world of data analytics. It was a steep learning curve, and while everyone’s adventures into data analytics may be different, here are some skills I’ve found to be essential in many data analyst roles. This is not exhaustive, but I hope this give you an idea of where to start, or if you’ve mastered them, then an indication to move on to the next level!


1. Excel/ GoogleSheets

Manipulating spreadsheets and extracting insights from small datasets. Even after moving to way larger datasets, keeping grounded in the basics is still important. Sometimes for a quick 5 min analysis of an issue, Excel is still the most efficient solution. Get familiar with formulas (vlookups, etc), pivot tables, plotting charts, and what these can achieve (as well as limitations). Bonus (or getting more basic nowadays), knowing VBA (for Excel) or Google App Script (for GoogleSheets).


2. Data Visualisation

Knowing what kind of chart is best for which kind of data, or the best way to convey the idea/ information. At a time where most of us still rely on charts to observe trends and point out potential issues, effective visualisation can make a lot of difference to your cause.


3. SQL

I’ve noticed that this is not always in the required section of data analyst job descriptions, but increasingly so. When you start to deal with large datasets, it is no longer feasible to open the entire dataset in Excel to see what is there. With SQL you extract and transform only the relevant parts of the dataset from a database for analysis. Hopefully small enough to open in Excel by then.

Furthermore, with advancements in database technologies, products such as Google’s BigQuery not only allows querying of data, but to perform more complex analysis with just SQL, at high speeds across large datasets. It is just the beginning, but it is the beginning, to taming huge data.


4. Business Knowledge

Finally, beyond just extracting insights, the most important step would be how to turn these insights into impact for your company or organization. Beyond understanding what CPC, CTR, CVR, etc mean, if you understand the implications of each, what can be improved in the context of your organization, and understanding the resources required versus expected result, you’d be all ready to getting started turning the collected data from meaningless strings of characters into gold. This may be harder to learn, but you can get started by reading up on the the industry as well as developments in data analytics.


What’s next?

To perform larger scale data analysis, different systems are required. Learning Python, statistical analysis methods and understanding databases may be next. And after that, we’ll see again.


I hope that has provided you a gauge of where to start in learning data analytics. As you can see, getting started in this field is not rocket science. With some effort, anyone can start analysing data and extracting insights that may change the world. You can do it too!


How did you find the article? Any points you’d like to add on to the list? Do you think this is a manageable/ realistic list? Let me know in the comments below! Thank you for reading!

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