Data science has many definitions depending on who is doing what. However, in simple terms, Data Science is a combination of skills, expertise, and acumen in Mathematics, Technology, and Business Strategy. To do well in Data Science, you need a combination of domain expertise, data engineering, scientific method, math, statistics, advanced computing, visualization, and a hacker mindset.

The following questions vary from easy to difficult in no particular order. Also, ** you will find the answer to each question in bold and italicized text**. If you find any issue with the questions or you have some questions and answers you want to suggest, you can contact me as you wish.

**A Type I error occurs if you do which of the following?**

- fail to reject the null hypothesis when it is false.
*reject the null hypothesis when it is true.*

**A Type II error occurs if you do which of the following?**

*fail to reject the null hypothesis when it is false.*- reject the null hypothesis when it is true.

**Alpha (α) is which of the following?**

- the probability that you correctly reject the null hypothesis.
*the probability of committing a Type I error.*

**Power is which of the following?**

*the probability that you correctly reject the null hypothesis.*- the probability of committing a Type I error.

**Sample size influence refers to which of the following?**

*the effect of the number of trails on the**p*-value.- the difference between the observed statistics and the hypothesized value.

**Effect size to which of the following?**

- the effect of the number of trails on the
*p*-value. *the difference between the observed statistics and the hypothesized value.*

**In general, what information does a histogram depict?**

- the changes in an outcome measurement over time.
*the number (or fraction) of obervations that falls within certain ranges of a particular outcome measurement.*

**True or False? By hunting for correlations in data samples, one can often find entirely spurios patterns.**

*true*.- false.

**True or False? Probability and statistics provide mathematical tools for estimating the likelihood of random events**

*True.*- False.

**What is one way of characterizing the relationship between data science and artificial intelligence (AI)?**

*AI nearly always involves data science, but only some data science projects involves AI.*- Data science and AI are different names for the same thing.

**What is the best use of the rules that are developed in neural networks?**

*for automating decision-making processes.*- for helping humans make decisions based on general principles.

**Application Programming Interfaces (APIs) generally serve what functions in a data science project?**

*APIs allows for accessing data and including it in a data science programming.*- APIs allows for acquiring data that was not structured for sharing.

**What are the elements that constitute “big data”?**

*volume, velocity, and variety of data.*- programming, maths/statistics and domain expertise.

**How does linear regression provide rules for decision-making?**

*Linear regression uses coefficients to combine multiple input variables into a single output variable; these coefficients can then be used for decision making.*- Linear regression finds the observations that best serve as examples for decision making.

**What elements make up data science?**

*hacking/programming, math/statistics, and domian expertise.*- descriptive analysis, prescriptive analysis, and predictive analysis.

**Why do the functions COVARIANCE.P and COVARIANCE.S return different values when using the same data?**

- COVARIANCE.S adds one to the number of data.
*COVARIANCE.S substracts one from the number of data.*

**To determine the probability of a result, Bayes’ rule combines accuracy, false positives and ….?**

- sample deviation.
*base rate.*

The few questions and answers are just the start. More will be added.

Enjoy!