One of the key aspects of getting into AI,ML is to understand the data, what the data is trying to represent, is there data a god sample for the business problem one is trying to solve. In order to answer these questions, there needs to be scientific way to understand the data, so this is where Exploratory data analysis come into picture. In order to do EDA once can use tools or it is also possible to start getting to know the patterns with tools such as Excel. One need not feel the pressure of not knowing tools such s Python or R. You can gradually learn and getting into those techniques available in the tools.
The first step is to get the data from a trusted source, make sure it is a source that is relevant to the business problem we are trying to solve. Now the data can be in different forms such as text files, csv, excel spreadsheets, logs or relational databases. Once you identify the source, choose a platform you want to bring the data into. For example let us assume spreadsheet is how you want to analyse the data.
2. Understand the Business problem you are trying to solve, also know the domain area of the business.
3. Try as much as possible not to be biased, review the data points.
4. Discard data points that are redundant, would not offer any value. In case you have doubts ask the SME or folks who know the business/data.
5. Identify how many missing observations are there. if there is lot of data missing, discuss with the data source and get valid data.
6. A certain margin rate of missing values is acceptable, some say 5%. Identify those data attributes.