Excel has been around since the early 1990s way before sophisticated Business Intelligence tools like Power BI and Tableau. So, people trust Excel, are familiar with it, and can start with their analyses without spending time learning a new tool.
Data wrangling and exploratory analysis are part of data science and play an important role in the data analysis process as they help in properly structuring the data through data detection, data cleaning, data summarizing, etc.
EDA is generally cross-classified. It can be done non-graphically or graphically and is further divided into either univariate or multivariate. We will look at the different types in this blog.
Exploratory data analysis is like doing a detective’s work of digging deep into piles of data to find clues that will aid the actual data analysis.
The primary intent of EDA is to determine whether a predictive model is a feasible analytical tool for business challenges or not.
EDA helps data scientists to manipulate data sources to get the answers they need, and as a result making the data analysis process easy for discovering patterns, testing a hypothesis, spotting anomalies, or checking assumptions.
Maps help us to analyze geographical data by plotting 3D data on a 2D plane. They are usually used when we want to answer a spatial question using data.
We all have been learning about bar charts since our school days, but we will learn about some exciting variations and additions that we can perform to get more insights than a simple bar chart.
we will learn about scatter plots, which are simple plots giving us insights into trends of the data. We will go deeper with some advanced features that make scatter plots an invaluable gift for effective data visualization.
Top Data Analytics terms are explained in this article. Learn these to develop competency in Business Analytics. This will also help you in preparing for the IIBA CBDA Certification.