Blog | Data Science and Technology

The knowledge of this essential math is particularly important for newcomers arriving at data science from other professions, Specially whosoever wanted to transit their career in to Data Science field (Aspirant). Because mathematics is backbone of Data science , you must have knowledge to deal with data, behind any algorithm mathematics plays an important role. Here am going to iclude some of the topics which is Important if you dont have maths background.  1. Statistics and Probability 2. Calculus (Multivariable) 3. Linear Algebra 4.  Methods for Optimization 5. Numerical Analysis 1. Statistics and Probability Statistics and Probability is used for visualization of features, data preprocessing, feature transformation, data imputation, dimensionality reduction, feature engineering, model evaluation, etc. Here are the topics you need to be familiar with: Mean, Median, Mode, Standard deviation/variance, Correlation coefficient and the covariance matrix, Probability distribution...

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...

The Central Limit Theorem is at the core of what every data scientist does daily: make statistical inferences about data. The theorem gives us the ability to quantify the likelihood that our sample will deviate from the population without having to take any new sample to compare it with. We don’t need the characteristics about the whole population to understand the likelihood of our sample being representative of it. The concepts of confidence interval and hypothesis testing are based on the CLT. By knowing that our sample mean will fit somewhere in a normal distribution, we know that 68 percent of the observations lie within one standard deviation from the population mean, 95 percent will lie within two standard deviations and so on. In other words we can say " It all has to do with the distribution of our population. This theorem allows you to simplify problems in statistics by allowing you to work with a distribution that is approximately normal."  The CLT is...

Q1. What is the difference between “long” and “wide” format data? In the  wide-format , a subject’s repeated responses will be in a single row, and each response is in a separate column. In the  long-format , each row is a one-time point per subject. You can recognize data in wide format by the fact that columns generally represent groups. Q2. What do you understand by the term Normal Distribution? Data is usually distributed in different ways with a bias to the left or to the right or it can all be jumbled up. However, there are chances that data is distributed around a central value without any bias to the left or right and reaches normal distribution in the form of a bell-shaped curve. Figure:   Normal distribution in a bell curve The random variables are distributed in the form of a symmetrical, bell-shaped curve. Properties of Normal Distribution are as follows; Unimodal -one mode Symmetrical -left and right halves are mirror image...