Statistics for Data Science

1. Statistics for DataScience

2. Introduction:- The processing of data is the most critical part of any Data Science method. When we speak about gaining information from results, we're simply talking about exploring the possibilities. Statistical Analysis is the term for these possibilities in DataScience. The majority of us are perplexed as to how Machine Learning models can process data in the form of text, photographs, videos, and other highly unstructured formats. But the fact is that we transform the data into a numerical type that isn't exactly our data, but it's near. As a result, we've arrived at a critical feature of DataScience.

3. 1 Using different statistical measures, determine the value offeatures. Importanceof Statistics for DataScience To remove the risk of duplicate features, find the connection betweenfeatures. 2 Data normalization and scaling This move often entails determining the distribution of data as well as the nature ofdata. 3 Taking the data for further analysis and making the requiredcorrections. 3

4. Key Concept ofStatistics This are the core principles for learning and speeding up the foundations of statistics for datascience.

5. Probability Understanding the possibilities requires a basic understanding of probability. To begin, consider the following scenario: What are the chances that Team A would win the football match against Team B? To arrive at this conclusion, we will need 100 people to cast their ballots – Number of Samples. We may predict which team to win the game based on certain votes. Sampling As we saw in the previous example, sampling is the process of identifying the appropriate group of people. The question is, who are the appropriate individuals? To continue our previous example, we will need 100 people who are knowledgeable about football, are familiar with Team A and B's history, and are not biassed towards a team because of their personal preference. As a result, various statistical approaches can be used to identify the appropriate sample. Simple random sampling,systematic sampling, Stratified sampling, Clustered sampling, and other sampling methodsexist.

6. Tendency and Distribution ofData The distribution of data is a crucial factor. The significance of a well-known distribution such as the Normal Distribution is enormous. When we talk about the world's height and weight distribution, for example, we're talking about normally distributed data that demonstrates nature's symmetry. At the central peak of the normal distribution, the Mean, Mode, and Median must all coincide. These are supposed to be extremely precise numbers. As a result, determining the distribution and skewness of data is a crucialconcept. HypothesesTesting If we know whether or not to take a certain action. Will those actions produce a positive or negative outcome, and if so, we will have the added benefit of doing the right thing. Hypotheses Testing identifies situations in which action should be taken or not taken based on the expected outcomes. Other tests with similar relevance include A/B Testing, Z Test, T-Test, and NullHypothesis.

7. AboutUs LearnBay is a Bangalore based Data Science Institute which is dedicated to help students , professionalstobecomeIndustry–readyinData Science. LearnBay provides IBM Certificates in DataScience. OurKeyfeaturesare: 200+Hoursofclassroomsessions Liveclasseswithrecordingofalltheclasses FlexibilityinSchedulingclasses Classstrengthnotmorethan10 Highly qualifiedtrainers 12+Realtimeprojectandcasestudies JobAssistance–Resumebuilding,mockinterview andjob referrals https://www.learnbay.in/

8. ContactUs Address:Learnbay,19/1,2ndFloor,Classic Aura(Beside Aricent),Marathahalli - Outer Ring Road,Kadubeesanahalli, Bengaluru,Karnataka Email :contact@learnbay.in CellNo :+918861279311