Are you looking forward to building a career as an applied data scientist? But you don’t have any technical training? Well, that shouldn’t stop you from going after your dream job. It’s never too late to change your academic path, and here’s how you can get started in data science even if you don’t have a technical background.
Starting From the Beginning
Even if you have not worked with data before, you can start by learning how organizations use data and how it is used in the business world. Then, one can put together a curriculum to get the technical skills needed. For example, you can sign up for courses to learn about programming languages and other important ideas.
One must also know how to solve simple math problems like linear algebra, calculus, probability, and statistics. This is important because data science tools and technologies will keep changing quickly, but the math behind them won’t.
One can also enroll himself in certification programs for data science. Getting a certification can improve your skills and make you a better candidate for a job as a data scientist.
Excel Must Be Learned
Excel is a trendy piece of business software mainly used to collect and store data. Because of this, you can find most of the data you need to work with in Excel.
If you don’t have a technical background and don’t know how to code, it will be easier for you to learn how to manipulate and analyze data in Excel. This will help you get started on your way to becoming a data scientist. You can start by learning how to reference cells, write basic formulas, and use aggregate functions like SUM, AVERAGE, MAX, and MIN and logical functions like IF, AND, and OR.
Once you are well versed with the basics, you can start learning how to use conditional aggregate functions like COUNTIF(S), SUMIF(S), and AVERAGEIF(S) and lookup functions like INDEX/MATCH, VLOOKUP, NESTED IFS, etc. to get information from data. After that, the next step will be to learn how to use Pivot Tables, which will make it simpler and more organized for you to sum up and group data. You can also learn how to use Pivot Charts, Slicers, Filters, and other tools to make visuals and dashboards.
By learning these ideas, you’ll have a good idea of how to find meaning in raw data and use visualization to show what you’ve found.
Software for Learning SQL
When you’re more comfortable working with data in Excel and other familiar places, you might want to learn SQL to work with relational databases. SQL is a common way for companies to store structured data, and it can hold a lot of information.
As a data scientist, you will be associated with SQL a lot. PostgreSQL and MySQL are the two most common SQL programs you should learn, and you can start with one of these as a beginner.
Conclusion
The key to landing a high-paying data science job is gaining hands-on training and experience. Building a portfolio of projects that address actual bottlenecks and inefficiencies in the real world is essential to this. As a result, rather than taking the traditional academic route, students would benefit from narrowly focused project learning. You can check out an online bachelor’s degree to enroll yourself today and start your learning journey in no time.