Data Science is a combination of these three skills:

1. Statistics/Mathematical Skills
2. Coding Skills
3. Domain Knowledge/Business Knowledge

Data is about numbers - and when you are working with numbers, you have to be confident with statistical and mathematical concepts.

Coding skills are required because the data that you will work with is often hard to access, broken, messy, has missing values, and so on. Fixing these things in an excel sheet is not so easy. Coding will give you full flexibility, so it is a must-have skill if you are seriously thinking about getting familiar with the basics of data analytics.

Domain knowledge and Business thinking is a soft factor, but as essential as statistics and coding. If you don't have Business or Domain knowledge then you will not be able to provide better solutions. It is like having a gun in war without knowing how to operate it.

Most of the people would have taken their last breaths after knowing about Stats and Coding. But one should remember, these are just a part of the whole scenario. One of the most important skills that you should possess is Business/Domain Knowledge. For Example, if you are in Marketing then you should be having well-versed knowledge about all the concepts, then only you can test various data with their validations.

There is also one more thing to keep in mind, you will learn coding eventually. It is not one of the most important skills. You can hire someone from outside in order to clean data and do some basic analytical procedures over them. But if you don't have knowledge about the subject, then you cannot find solutions to issues.

What Tools to Learn?

It is possible that as a data analyst you are not coding at all, but using smart tools like Google Analytics, Heat mapping Tools, and A/B Testing Tools instead. If you start learning some tools. here are some to begin with:

• SQL: SQL (Structured Query Language) is a super simple query language. It is well structured and easy to interpret. It is basically used as a database and some of the examples include Oracle and MySQL.

• Python: Python is easy to interpret and easy to learn as well, but much more complex than SQL. Now python is used for analytical modeling and Machine Learning as well, because of it being open-source software, it has a growing library of analytical tools. Python is usually used for front-end developments as well as with a combination of SQL Database and Python Front End, it becomes super easy to crunch data and churn out meaningful insights.

• R or RStudio: It is an open-source programming language used for Statistical Analysis. It is one of the best tools to use as it is created specifically for Analytical usage, but we can also use it for creating web apps.

There are many other Analytical tools like Bash, Tableau, and others that can be used for handling a large amount of data. But it all depends on the person, if he or she wants to use Advance Excel, there are no limitations for the same.