Basic Statistical Concepts required to start a career in Data Science

As a data scientist, it is important to have a strong foundation in statistical concepts and techniques. These concepts and techniques help you to understand and analyze data, draw meaningful insights, and make informed decisions.

These 9 concepts would help you get an overview of how exactly statistics and math play a key role in a career as a Data Scientist.

Here are some of the important and basic statistical concepts that you should be familiar with:

1. Mean, Median, and Mode: These are measures of central tendency, which give us an idea of the “middle” or “typical” value in a dataset. The mean is the average of all the values, calculated by adding up all the values and dividing by the total number of values. The median is the middle value in a dataset when the values are ordered from least to greatest. The mode is the value that occurs most frequently in a dataset.
2. Range, Variance, and Standard Deviation: These are measures of dispersion, which give us an idea of how spread out the values in a dataset are. The range is the difference between the highest and lowest values in a dataset. The variance is a measure of how far each value is from the mean. The standard deviation is the square root of the variance, and it gives us an idea of how much the values in a dataset vary from the mean.
3. Correlation: Correlation is a statistical relationship between two variables. A positive correlation means that as one variable increases, the other variable also increases. A negative correlation means that as one variable increases, the other variable decreases. A correlation coefficient is a numerical measure of the strength and direction of the relationship between two variables, ranging from -1 (perfect negative correlation) to 1 (perfect positive correlation).
4. Regression: Regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. It involves fitting a line (called the regression line) to the data that best captures the relationship between the variables. Regression can be used to make predictions about the dependent variable based on the values of the independent variables.
5. Probability: Probability is the measure of the likelihood of an event occurring. It is expressed as a decimal or fraction between 0 and 1, with 0 indicating that the event will not occur and 1 indicating that the event will definitely occur. Probability can be calculated using the formula: probability = number of favorable outcomes / total number of outcomes.
6. Normal Distribution: The normal distribution is a continuous probability distribution that is symmetrical around the mean. It is often used to model data that follows a bell-shaped curve. The normal distribution is characterized by its mean and standard deviation, which can be used to calculate probabilities for different ranges of values.
7. Sampling: Sampling is the process of selecting a subset of data from a larger population. Sampling is often used in statistical analysis to make inferences about the population based on the characteristics of the sample. There are different types of sampling techniques, including random sampling, stratified sampling, and cluster sampling.
8. Hypothesis Testing: Hypothesis testing is a statistical procedure used to determine whether a hypothesis about a population is true or false. It involves defining a null hypothesis, which represents the assumption that there is no relationship between the variables being studied, and an alternative hypothesis, which represents the opposite assumption. The data is then collected and analyzed to determine whether the null hypothesis can be rejected in favor of the alternative hypothesis.
9. Confidence Intervals: A confidence interval is a range of values that is calculated from a sample of data and is used to estimate a population parameter. It is often used to quantify the uncertainty associated with a statistical estimate. The width of the confidence interval depends on the size of the sample, the level of confidence desired, and the variability of the data.

These are just a few of the important and basic statistical concepts that are essential for data science. It is important to continue learning and expanding your statistical knowledge as you grow in your career as a data scientist.

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