Skip to main content

Differentiate between univariate, bivariate and multivariate analysis.

Univariate analysis are descriptive statistical analysis techniques which can be differentiated based on one variable involved at a given point of time. For example, the pie charts of sales based on territory involve only one variable and can the analysis can be referred to as univariate analysis.

The bivariate analysis attempts to understand the difference between two variables at a time as in a scatterplot. For example, analyzing the volume of sale and spending can be considered as an example of bivariate analysis.

Multivariate analysis deals with the study of more than two variables to understand the effect of variables on the responses.

Comments

Popular posts from this blog

Data Analytics Interview Questions - Part 1

Q1. Python or R – Which one would you prefer for text analytics? We will prefer Python because of the following reasons: Python  would be the best option because it has Pandas library that provides easy to use data structures and high-performance data analysis tools. R  is more suitable for machine learning than just text analysis. Python performs faster for all types of text analytics. Q2. How does data cleaning plays a vital role in the analysis? Data cleaning can help in analysis because: Cleaning data from multiple sources helps to transform it into a format that data analysts or data scientists can work with. Data Cleaning helps to increase the accuracy of the model in machine learning. It is a cumbersome process because as the number of data sources increases, the time taken to clean the data increases exponentially due to the number of sources and the volume of data generated by these sources. It might take up to 80% of the time for just c...

Data Science Skills

Below are some of the data science skills that every data scientist must know: 1. Change is the only constant It’s not about “Learning Data Science”, it’s about “improving your Data Science skills! The subjects you are learning currently in Grad School are important because no learning go waste but, the real world practicality is totally different from the theory of the books which is taught for decades. Don’t cramp the information, rather understand the big picture. A report states that 50% of things that you learn today regarding IT will be outdated in 4 years. Technology can become obsolete but, learning can’t be. You should have the attitude of learning, updating your knowledge and focusing on your skills(Get your Basics clear) and not on the information you learn! This will help you to survive in this tough and competitive world (I am not scaring you, I am just asking you to prepare your best! You should start focusing on the below skills for becoming a data scientist –...

Important Python Libraries for Data Science

Python is the most widely used programming language today. When it comes to solving data science tasks and challenges, Python never ceases to surprise its users. Most data scientists are already leveraging the power of Python programming every day. Python is an easy-to-learn, easy-to-debug, widely used, object-oriented, open-source, high-performance language, and there are many more benefits to Python programming.People in Data Science definitely know about the Python libraries that can be used in Data Science but when asked in an interview to name them or state its function, we often fumble up or probably not remember more than 5 libraries. Important Python Libraries for Data Science: Pandas NumPy SciPy Matplotlib TensorFlow Seaborn Scikit Learn Keras 1. Pandas Pandas (Python data analysis) is a must in the data science life cycle. It is the most popular and widely used Python library for data science, along with NumPy in matplotlib. With around 17,00 comments on GitH...