|Order ID: DHT6H23GDHY82HD
|Delivery: 3 HOURS & ABOVE
|Writer Classification: PHD-PROFICIENT
|Formatting Style: APA/HARVARD/MLA
|Plagiarism Grading: 0-0.3%
|Quality Standard: 99.999%
|Course Level: UNIVERSITY/COLLEGE
|Revision Request: ALLOWED
Q1: Explain the steps involved in data science?
Q2: Explain variable and different types of variables?
Q3: Explain Categorical measurement?
Q4: Explain Binary variables?
Q5: Explain Nominal measurement?
Q6: Explain Ordinal variable?
Q7: Explain Continuous variables?
Q8: Explain Discrete variables?
Q9: Is it possible to convert continuous values to discrete and vice versa?
Q10: What are interval variables?
Q11: What are ratio variables?
Q12: What are Univariate and Bivariate variables?
Q13: What is measurement error?
Q14: Explain Validity?
Q15: Explain Reliability?
Q16: What are the different ways to test hypotheses?
Q17: Explain the different types of variation?
Q18: Explain repeated-measures design?
Q19: What is independent design?
Q20: Explain the role of randomization w.r.t variation?
Q21: Explain various summary measures.
Q22: Explain alternate hypotheses and null hypotheses.
Q23: What is p value?
Q24: What happens when null hypotheses is rejected?
Q25: Explain directional and non-directional hypotheses.
Q26: Explain fit of model?
Q27: What is relation between sample and population?
Q28: What is estimation?
Q29: Explain deviation score?
Q30: Explain variance?
Q31: Explain Standard deviation.
Q32: Explain standard error.
Q33: What is precision?
Q34: Explain confidence intervals.
Q35: Explain confidence level.
Q36: Explain alpha.
Q37: Explain Beta.
Q38: Explain Accuracy.
Q39: Explain Bias.
Q40: What is central limit theorem?
Q41: Explain Absolute value?
Q42: What is degree of freedom?
Q43: Explain cluster sampling.
Q44: Explain Correlation coefficients?
Q45: Explain sample space.
Q46: What is non parametric algorithm?
Q47: How can learning be classified?
Q48: What is classification?
Q49: Explain the steps involved in classification.
Q50: What is regression?
Q51: Explain the similarities and differences between Classification and Regression.
Q52: Explain various terms encountered during classification algorithm.
Q53: Explain multi class classification?
Q54: Explain multi label classification?
Q55: Explain how multi label problem can be solved?
Q56: Explain some important metrics with respect to testing a model?
Q57: What is logistic regression?
Q58: Explain Naïve Bayes.
Q59: What is Stochastic Gradient Descent?
Q60: Explain decision tree algorithm.
Q61: What is Gini index?
Q62: Is Gini index the only means which can be used in decision tree?
|FOLLOWING OF INSTRUCTIONS
|NEATNESS AND OTHERS
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