Sampling is the part of statistics that involves the data collection, data analysis, and data interpretation of the data which is collected from a random set of population. T-test: A t-test is a statistical test that is performed when either the variance of the population is not known or when the size of the sample is small.It is used when the mean of two populations is different, and either their variances are known, or the size of the sample is large. Z-Test: Z-test is another way of testing the Null Hypothesis statement.Here ‘a’ is some significant value which is almost equal to 0.5. If p-value < a, then the Null Hypothesis is False, and we reject it. If p-value > a, then the Null Hypothesis is correct. P-value test: It is the probability value that helps to prove that the null hypothesis is correct or not.Below are some of the tests that help in the acceptance or rejection of the Null Hypothesis. Test of significance is a set of tests that helps to test the validity of the cited Hypothesis. The Alternate hypothesis is the contradictory statement of the Null hypothesis. The null hypothesis is the general statement that has no relation to the surveyed phenomenon. Null hypothesis and Alternate Hypothesis. There are two types of hypothesis as part of hypothesis testing viz. Hypothesis testing is to test the result of a survey. UpGrad’s Exclusive Data Science Webinar for you – Kurtosis: It defines whether the data has a normal distribution or has tails.Skewness: It measures the symmetry of data distribution and determines if there is a long tail on either or both sides of the normal distribution.Mode: The value repeating most in the data set column.Median: It is the central value in the ordered data set.Mean: It is the average value of the data set column.There are different ways to measure the central tendency: The central tendency of a data set is a single value that describes the complete data by the identification of a central value. Therefore, the dimensionality reduction concept resolves all these problems and offers many potential benefits such as lesser redundancy, fast computing, and fewer data to store. This further increases the complexity of data analysis. This is because there are many factors in the high dimensional data set and scientists need to create more samples for every combination of features. Dimensionality Reductionĭimensionality reduction means reducing the dimensions of a data set so that it resolves many problems that do not exist in the lower dimension data. Conditional probability is the probability of occurrence of any event having a relationship with any other event. Independent events are the two or more occurrences of an event that are independent of each other. There are different types of probability, depending upon the type of event. The higher the value, the event is more likely to happen. Probability is a number whose value lies between 0 and 1. As an example, a toss of a coin predicts the probability of getting a red ball from a bag of colored balls. Probability is the mathematical branch that determines the likelihood of occurrence of any event in a random experiment. Inferential statistics, on the other hand, help in finding insights from data analysis. It is different from inferential statistics as it helps to visualize the data in a meaningful way in the form of plots. Descriptive statistics offers a way to visualize the data to present it in a readable and meaningful way. Descriptive Statisticsĭescriptive statistics help to analyze the raw data to find the primary and necessary features from it. īelow are the basic Statistics concepts that a Data Scientist should know: 1. The descriptive statistics and knowledge of probability are must-know data science concepts. This can be inferred from the fact that statistics help to interpret and organize data. Data scientists must know the statistics very well. Statistics is a broad field that offers many applications. Statistics make a central part of data science. Read: Highest Paying Data Science Jobs in India Statistics Concepts Needed for Data Science Learn Data Science Courses online at upGrad Whether you are a beginner in the field or want to explore more about it or you want to transition into this multifaceted field, this article will help you understand Data Science more by exploring the basic Data Science concepts. In this article, I will share the basic Data Science concepts that one should know before transitioning into the field. Therefore, a person should be clear with statistics concepts, machine learning, and a programming language such as Python or R to be successful in this field. It helps to analyze the raw data and find the hidden patterns. Data Science is the field that helps in extracting meaningful insights from data using programming skills, domain knowledge, and mathematical and statistical knowledge.
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