The data you collect from the experiment are generally called the raw data. You should always make sure that you have a copy of these raw data stored somewhere safe, so that you or someone else could start the data analysis from scratch should the need arise (like, say, if your files got corrupted or deleted by accident). Saving a safe copy could be as simple as taking a picture of your recorded data in your lab notes with your phone, or keeping a copy of a file on both your computer and a usb stick.
How do you plan to save a copy of your raw data?
The next step is to plan how to check for any mistakes that might have been made when recording your observations, and how to deal with them. For instance, if one of your data points is an order of magnitude larger than all others (e.g. a "100" instead of a "10"), this is likely a typo. If so, then simply state this and make the correction. But sometimes you'll see an observation that appears unusual compared to the other data points, and it's not a mistake. These are sometimes considered "outliers", and you need a plan in place to deal with these outliers. For instance, an honest and transparent approach is to conduct any analyses you do both with and without the outliers included, presenting both sets of results. If the exclusion of the outlier(s) doesn't change your conclusions, then great! But if it does, then you'll need to discuss this. The key is to have a clear plan in place, and to document what you did, and be honest and transparent about it!
Typos and outliers are often best revealed using graphs; they'll show up as observations that are far from the other observations in your graph. Thus, visualizing your raw data with effective graphs should be the next step in your plan.
Your experimental design determines what type of data you will collect, which then determines the appropriate method for describing, visualizing and analyzing the data.
For your experiment, the data you collect will depend on the question you're exploring, and the hypothesis that you're testing. As such, the way that you will need to describe, present, and analyze the data will likely be different than your classmates', since their hypotheses will be different from yours.