Data analysis has evolved with the advancement of computers and an ever-snowballing move toward a technological revolution. Want to learn more about this high-demand career and find out what skills you must have in order to become a data analyst, then you have landed on the right page. We have developed a synopsis of a more detail-oriented Learn path in this domain. You can find the link to Data Analytics courses that helps you learn those skills towards the end of this article. What is data analysis? Data Analysis is a robust process of identifying and collecting data that you want to analyze, cleaning and sorting the data as groundwork for analysis, analyzing and interpreting the results, and presenting it in a meaningful visual format with the help of charts and graphs. A Data Analyst is an individual who has the expertise and skills to process raw data extracted from large datasets into meaningful information and insight to make more informed business decisions. Data is everywhere Businesses in all industries are more and more dependent on data to make crucial business decisions like what products to produce, which markets to enter, and which customer segment to target. Data is also being used to diagnose weak areas in a business that need to be addressed. They can work in several industries, including agriculture, banking, entertainment, education, and so on. Data science versus data analytics Data Science is an umbrella domain that encircles Data Analytics. Data Science is an amalgamation of multiple skill sets like Machine Learning, Mathematics, Artificial Intelligence, and Statistics. It encompasses concepts like data mining, predictive modeling, and ML algorithms to analyze multiform datasets and transform them into scientific business strategies. Whereas, Data Analytics deals with Mathematical and Statistical Analysis. In simple words, Data Analytics is a subdivision of Data Science that is mainly concerned with deriving answers to specific questions that Data Science brings forth. Both the roles entail multiple responsibilities of collecting, cleaning and analyzing data. Hence, their job roles often overlap.