Questions for a Data Science Interview Based on Statistics:
As Josh Wills famously said, "A Data Scientist is the person who is better at statistics than any programmer and better at programming than any statistician." In fact, statistics forms a crucial foundation in data science, helping us analyze and interpret vast amounts of data effectively.
Here's a simplified rundown of 10 essential statistics concepts every Data Scientist should know:
- Population & Sample: A population represents the entirety of all items being studied. A sample is a subset selected to represent the entire group due to cost or size considerations. Examples: a census (population) versus a survey (sample).
- Descriptive & Inferential Statistics: Descriptive statistics describes some sample or population, while inferential statistics helps us infer from some sample to the larger population.
- Quantitative & Qualitative Data: Quantitative data refers to numerical data like how many, how much, or how often, whereas qualitative data, also known as categorical data, measures types and may be represented by a name, symbol, or number code.
- Standard Deviation: Standard Deviation is a measure of variability in a dataset. A high standard deviation means values are usually far from the mean, while a low standard deviation indicates values tend to cluster close to the mean.
- Long & Wide Data Formats: Long format stores multiple variables for each observation in separate rows, while wide format stores multiple observations across rows, with each column representing a unique variable.
- Median vs. Mean: The median is a better measure of central tendency when the data distribution is skewed or if there are outliers.
- Sample Size Calculation: To calculate the sample size needed for a survey or experiment, define the population size, margin of error, confidence level, and standard deviation. Based on these factors, you can use formulas or online calculators to find the required sample size.
- Types of Sampling: Simple random sampling, cluster sampling, stratified sampling, and systematic sampling are the main types of data sampling.
- Outliers & Bias Correction: Outliers are data points that deviate significantly from others. Bessel's correction is used to correct bias in variance calculations by using n-1 instead of n, where n is the number of observations.
- Normal Distribution & Hypothesis Testing: A normal distribution is a bell-shaped distribution with most data points centered around the mean. Hypothesis testing is used to determine if there is enough evidence supporting a hypothesis about a population based on sample data.
Happy learning!
In the realm of health-and-wellness, data science plays a pivotal role in advancing mental-health care by leveraging machine learning algorithms to analyze extensive patient data, enabling better treatment plans and interventions.
Moreover, understanding essential statistics concepts such as standard deviation, sample size calculation, and types of sampling can empower data scientists to effectively interpret mental-health data, thereby improving mental-health services and overall well-being.
