How to be a Data Analyst 101
Data Scientist, or Data Analyst has become a hot topic and I can definitely say that it is the job of the century.
I got into data analytics because everyone was doing it, but I didn’t know how significant it was, until now.
If you want to get into analytics, here’s a simple guide to get started with.
SQL: SQL is one of the most crucial skills a data scientist needs. You can say that SQL is the key a person needs to enter into the data castle.
So practice SQL as much as you can. It’s pretty easy to learn from resources like w3Schools and Tutorials Point to get to know about the concepts and the syntax ; and DataCamp or Hackerrank for some hands on.
Python: The only way we can make predictions and identify some forms of patterns in data is Python. It is used significantly in the industry and guess what?! If you don’t know how to code at all, Python is a good way to start. It is one of the easiest languages to learn.
YouTube is the best place to start with; Check out Telusko Python to get to know about the basics or DataCamp for practice.
Statistics: Statistics is one of the pillars of data science. According to Wikipedia,
Statistics is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data.
It is used to gain insights about the data mathematically. It is used to analyze patterns in data over time, the trends and the possibilities it can have in the future. There are some really good courses on Coursera for Data Science .
Data Visualization: Data Visualization is one of the most important skills to master for data analytics. It is a tool to convert information into graphical representations that are easy to analyze and is preferred when used for presentations to stake holders. Udemy is a great tool to learn Tableau in, where you can learn step-by-step on how to make relevant charts .
Projects: It is essential to brush up your skills to analyze actual data and come up with some insights. Pick up some projects on Kaggle to deal with raw data and do some exploratory data analysis, get to know the data, clean it up and then run some machine learning algorithms to make predictions of key factors in the data. You can also find public databases on Kaggle, data.gov, Google Big Query and World Bank.
Once you start learning these skills, you will automatically have an idea on where to go next, based on your interests. Remember, the path towards analytics is not the same for everyone. So get started and never stop learning!