|
Have you ever tried to explain something to a friend that you didn't understand very well. Maybe it was a difficult math assignment, a complex news story or the way a family member cooks a favorite dish. When you're trying to teach a topic you're not an expert on, it can be challenging to explain the details and give clear instructions. As a data professional, you never want to be in this position with the data you analyze. In fact, your goal should be to know your data very well. When you're reviewing a table of data, it's important to understand where the data is from, what the column headers mean, what the data will be used for, what the imperfections are, and the small details in between. Making sense of raw data is why we are here. Welcome. I'm excited for you to build on the knowledge that you've learned so far to perform exploratory data analysis or EDA on the kind of data sets you'll see on the job. You'll start by learning about the many different data types and data sources that you will encounter in your work and how to study them. After that, you will return to those Python notebooks to start coding the concepts you've learned. We'll also go into more details on the first two practices of EDA: discovering and structuring. While learning about the discovery in practice, we will use popular Python functions to get to know the information contained in the data sets. You'll learn to use different visualization techniques to uncover hidden correlations and connections in the data. As restructuring, you will learn to apply Python functions to large data sets for many different operations including sorting, filtering, extracting, slicing, joining and indexing large data sets. You'll learn to make basic corrections or formatting improvements to hold data columns or entire data sets. All of these techniques and practices will help you learn about the data and find the story that needs to be told. Along with all the Python functions and coding scripts, we will talk about what to do when questions about the data set arise that you can answer, like why there are missing data fields, for example. We'll also make sure the EDA you perform aligns with a pace workflow that we have set. At its core this section of the course is about digging through a data set for the first time and investigating it as meticulously as you can. It is up to you to find the stories in the data. Most data professionals will tell you that comprehensive EDA is the key to useful visualizations and models. If there are questions left unanswered or misunderstanding still present in the data after EDA, any presentation or machine learning model based on it will not be particularly useful. It might feel like trying to explain a math assignment you don't fully understand. Remember, all data sets have stories to tell. Let's get to work and find them.
|