Data Science Learnings

How many times have you got baffled with the beginner part in Data Analysis? I am sure many times as I faced the same. So in this post, I will guide you as how to do Exploratory Data Analysis (EDA) with Python. EDA is one of the basic steps involved in Data Analysis. Before you begin with the analysis part, understanding data is very important. You should be familier with certain terminologies of the type of data on which you are working. For instance, you are working with any financial data let’s say stocks and thus, you should be familier with terminologies like open, close, high, low, volume and so on. Alright, we now know understanding data is important so let’s move ahead with the analysis part! Why Data Analysis? To get inferences from the data and to know future possible outcomes based on historical values of any data. Continuing the above example, with analysis of certain stock, let’s say I analyzed performance of a particular stock from year 2000 to 2020. I will find certain t

Analysis of the data before running into predictions is very important. Understand a few rows and a few columns is very nominal task and we can easily examine the data. However, with a little larger data, suppose 10,000 rows with 50 columns, we really need to do analysis of the data so that we can come to know which factors are going to affect our prediction. Data Analysis with Python is a bit tedious task as we have to prepare the data i.e. cleaning, pre-processing and normalization. We use Seaborn and Matplotlib for our data visualization. But before plotting the graphs, we need to know which columns are inter-related. For that, we need a co-relation matrix which we can create using Python. However, PPS matrix is more better than co-relation matrix. Fig1: Co-relation Matrix of Covid19 dataset Fig2: PPS Matrix of Covid19 dataset It is always a tedious task when we code for Data Analysis. So, we have certain tools available in the market for it like Power BI, Tableau, etc. I have done