# 7 Important Concepts Of Statistics For Data Scientists

Updated 30 September 2022

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With businesses becoming data-centric, data science contributes primarily to the success of many organisations. An important discipline that is integral to data science is statistics. Understanding the fundamental concepts of statistics can help data scientists extract more meaning and insight from data. In this article, we explore the role and benefits of statistics in data science and also understand key statistical concepts for professionals in this area.

## Importance of statistics for data scientists

An essential use of statistics for data scientists is during data cleaning. This process involves collecting and structuring data so that it can easily serve as input to machine learning algorithms. An important goal in data cleaning is eliminating irrelevant data and redundancies. Statistics are also important for data analysis. It helps data scientists analyse and assess the results of using certain data and to determine the subsequent steps to improve it. Statistical methods can help one calculate various numerical values, such as variance, mean, probability and distribution.

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## 7 Important statistical concepts in data science

Statistics involves the collection, analysis, interpretation and organisation of raw data. With its powerful tools and methods, statistics form the core component in data analytics or data science. Gaining an understanding of key concepts in statistics for data scientists is helpful for a successful career in this field. These are some of the main statistical concepts in data science:

### 1. Probability

Probability is a statistical concept and forms the basis for data science. Probability aids you in making predictions in real-world scenarios. As predictions and estimates are integral parts of data science, understanding this concept is important while pursuing a career in data science. Probability helps data scientists analyse and predict random phenomena, data and occurrences of uncertain events.

### 2. Population and sample

Population and sample are two data sets that are available while performing statistical testing. Population refers to the entire data set available for analysis. A sample is a subset of the population data. We can classify population as finite population, infinite population, existent population and hypothetical population based on the type of data. As performing statistical tests based on the population data can be costly and time-consuming, specific samples form the basis for statistical testing. The process of selecting a sample from a population is called sampling.

### 3. Distribution of data

The distribution of data is the shape occurring on the graph when one plots every value in the data set on a frequency graph. It can be of three types, which include normal distribution, positive skewness and negative skewness.

Normal distribution occurs when the distribution of data is absolutely symmetric and has a bell-shaped curve. For a positively skewed distribution, the graph has a tail on the right and a higher number of data points on the left side. For a negatively skewed distribution, the graph has a tail on the left and a higher number of data points on the right side.

### 4. Measure of central tendency

The measure of central tendency is a statistical method that determines a single value for the entire data set or distribution. With the help of central tendency, one can deduce an accurate understanding of the entire distribution. There are three important measures to determine the central tendency of a distribution:

#### Mean

The mean is the average value occurring in the data set. Summing up all the values and dividing this number by the count of values gives you the mean of a distribution. When a distribution is symmetric, the mean value is at the centre of the graph. While for positive and negative skewed distributions, the mean value drifts away from the centre. Because of this, one can more accurately determine the mean of symmetrical distributions.

#### Median

Arranging the data set in the ascending or descending order and picking out the middle value in this list will give you the median in a data set. The median can help one determine the actual middle or median value in a particular data set. Determination of median is common in qualitative analysis of data. In business scenarios, this measure of central tendency is common in problems that pertain to expenses, revenues and investments.

#### Mode

The mode in a data set is the element or data value that occurs most frequently. By calculating the mode, one can determine the element in the data set with the highest frequency. In the real-world business scenario, mode finds use in sales or pricing strategies. Determining the mode price can help shopkeepers determine the price which generated maximum sales.

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### 5. Variability

Variability in statistics is a measure of the extent to which data points in a data set diverge and differ. It is a good measure of how values in a data set can vary with respect to each other. The four common ways to determine variability inside a data set are:

#### Range

The range covers the amount from the smallest to the largest value in your data set. Subtract the smallest value from the largest to find the range in a data set. It is the simplest and most basic measure of variability.

#### Interquartile range

This value is almost the same as that of the range, but it does not cover the entire data set as with the range. With interquartile range, only the middle fifty values account for the calculation of the range. Also called the midspread, this range occurs between the 25th and 75th percentiles of data.

#### Variance

The variance value helps you determine the spread of your data. Obtaining a small variance value means that your data values are tightly clustered. Obtaining a large variance indicates that the values occur widely apart. Finding the variance is the first step to calculating the standard deviation of a data set.

#### Standard deviation

Standard deviation indicates the nature of clustering of data values around the mean of a distribution. A small value of standard deviation shows tightly clustered data values around the mean. A large value of standard deviation shows less clustering around the mean and values in your data differ from each other significantly.

### 6. Central limit theorem

According to the central limit theorem, plotting a distribution comprising sample means gives you a normal distribution or its approximate, irrespective of the distribution of the original population. The central limit theorem helps data scientists make statistical inferences regarding the data available. This theorem finds applications in real-world scenarios like census and election polling.

### 7. Conditional probability

Conditional probability differs slightly from probability. In conditional probability, the outcome expected relies on the occurrence of a relational event. Conditional probability finds the best use in real-world situations where some additional information is available. This concept relates closely to Bayes' theorem, one of the most important theories in statistics.

Related: What Does A Data Scientist Do? And How To Become One

## Benefits of statistics in data science

Statistical methods enable data scientists to find structure and make predictions regarding data sets. The following are some important benefits of statistical methods used in data science:

### Enables the organisation and classification of data

Organisation and classification of data is a key statistical method that is employed in data science. Proper organisation and classification make it easier to find, retrieve, manipulate and analyse data. This step is key for organisations that rely on resulting data-driven insights to make business plans and predictions.

### Helps calculate probability distribution/estimation

Probability distribution in statistics helps assess the possibilities of occurrences of different outcomes in a particular scenario. Predictions and estimates are essential tools while making inferences regarding real-time data. These statistical methods form the basis of Bayesian analysis, machine learning and logistic regression algorithms.

### Helps to find structure in data

Organisations often deal with large and complex data dumps from several sources. This can include a combination of structured, unstructured and semi-structured data. Statistical methods can help assess common trends and patterns and spot anomalies in data, saving the company valuable time, resources and effort.

### Aids in the visualisation of data

Data visualisation is an important aspect of data science. It is helpful for cleaning data, understanding data structures, recognising trends in data, detecting anomalies and outliers in data and presenting the information. Statistics help plot the data in graphs and other structures to allow easy interpretation of data.

### Supports mathematical analysis

Complex data require mathematics for analysis and insights. Mathematical analysis of data can include statistical tools and also other simpler formulae and calculations. Applying statistical models to data sets helps one draw conclusions and infer scientific predictions.

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