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

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 A report states that 50% of things that you learn today regarding IT will be outdated in 4 years.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 –...

20 Must know Data Science Interview Questions by kdnuggets

The Most important questions which is generally asked by the technical panel : 1. Explain what regularization is and why it is useful. 2. Which data scientists do you admire most? which startups? 3. How would you validate a model you created to generate a predictive model of a quantitative outcome variable using multiple regression. 4. Explain what precision and recall are. How do they relate to the ROC curve? 5. How can you prove that one improvement you've brought to an algorithm is really an improvement over not doing anything? 6. What is root cause analysis? 7. Are you familiar with pricing optimization, price elasticity, inventory management, competitive intelligence? Give examples. 8. What is statistical power? 9. Explain what resampling methods are and why they are useful. Also explain their limitations. 10. Is it better to have too many false positives, or too many false negatives? Explain. 11. What is selection bias, why is it important and how can you avoid i...

What is P Value ?

In Data Science interviews, one of the frequently asked questions is ‘What is P-Value?”. According to American Statistical Association, “A p-value is the probability under a specified statistical model that a statistical summary of the data (e.g., the sample mean difference between two compared groups) would be equal to or more extreme than its observed value.” That’s hard to grasp, yes? Alright, lets understand what really is p value in small meaningful pieces to make it very clear. When and how is p-value used? To understand p-value, you need to understand some background and context behind it. So, let’s start with the basics. p-values are often reported whenever you perform a statistical significance test (like t-test, chi-square test etc). These tests typically return a computed test statistic and the associated p-value. This reported value is used to establish the statistical significance of the relationships being tested. So, whenever you see a p-valu...

Blog | Data Science and Technology

Search This Blog