# Statistics for Data Analytics: 5 Comprehensive Aspects

The most critical aspect of Data Science is the way information is processed as generated insights augment decision-making. Since dealing with business challenges requires predicting the occurrence of events, Statistics acts as a foundation to solve several organizational challenges.

When you use Statistics for Data Analytics, it helps Data Scientists to build complex models that can generate insights into Big Data and help companies optimize business operations effectively. Although Statistics have been a part of business decisions for decades, the exponential growth of data, computation and advancements in Data Science led to the proliferation of Statistics with Big Data.

This article provides you with a comprehensive overview of the role of Statistics in Data Analytics and Data Science. It also explains about types of Statistics and fundamental concepts that will shed some light on the application of Statistics in the Data Science and Analytics domain.

## What is Statistics for Data Analytics?

Statistics is a branch of mathematics that is concerned with collecting, organizing, and interpreting data to represent specific characteristics. Statistics is presumed as the science of learning from data, which acts as a measure of attributes of a given sample.

The data leveraged here can be qualitative (categorical) or quantitative (continuous or discrete type). By using Statistics for Data Analytics, organizations can find trends and patterns within data, which are then applied to practical use cases for business growth. The main objective is to solve strenuous problems that could not have been possible without data.

## Types of Statistics

The term Statistics has several basic meanings, and when related to mathematics, it is broadly classified into two types:

### 1) Descriptive Statistics

Descriptive Statistics describe basic features of data to provide an overview of Big Data, as it assists in summarizing, reviewing, and communicating in a meaningful way.

When organizations use Descriptive Statistics for Data Analytics, they can describe the measure of central tendency and distribution of data. However, it does not give any idea of future events.

### 2) Inferential Statistics

Inferential Statistics are used to construct predictions, and inferences and make decisions from data. It also assists in drawing business insights into collected data to accomplish organizational goals, which could be hypothetical, having randomness and variations from the desired result.

## Benefits of Statistics for Data Analytics

Using Statistics for Data Analytics and Data Science can provide you with the following benefits:

• Statistics assists in gaining insights into business operations, making it an important aspect of any Data Science and Analytics project life cycle.
• Apart from understanding Statistical measures, it also plays a vital role in data preprocessing and feature engineering.
• It helps in visualizing numbers to understand patterns and trends existing in quantitative data.
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## Fundamental Terms Used in Statistics for Data Analytics

To be more familiar with the power of Statistics, one must know the following essential terms which are often used in Statistics for Data Analytics:

### 1) Probability

Probability, in simple terms, is the chance of occurrence of the desired result. In other words, it is presuming possibilities for a random event.

For instance, in a dice game, getting 6 in a single roll, a player has a 16th (16.67%) chance of winning the jackpot. In the same way, using Statistics for Data Analytics to find the likelihood of an event helps in classifying categories by their probability.

### 2) Population and Sample

The Population is a complete set of data and the sample is a subset of population data. To perform Statistical tests on population data requires more time and cost, which becomes inefficient, therefore it is always performed on samples of data to understand associated measures of population.

When you use Statistics for Data Analytics, these measures obtained as test results are used to gain further insights into the population.

### 3) Distribution of Data

It is highly recommended to understand the spread of data to evaluate the Skewness and Kurtosis, which tells that data is biased. In such cases, one should apply different data transformations.

One of the widely used methods when applying Statistics for Data Analytics is the normalization of data to resemble a bell shape. Normalization often distributes data symmetrically, scaling them between 0 and 1.

### 4) The Measure of Central Tendency

Central Tendency is a value that determines the central value of the given dataset. The Central Tendency is summarized by 3 terms: Mean, Median, and Mode. It becomes crucial to justify when to use a particular measure for a given data.

• Mean is the arithmetic average of a given distribution and is highly affected if data consist of outliers.
• Median is the absolute middle value of a given set and separates data into two halves. It is resistant to skewness and does not get affected by outliers.
• Mode represents the most frequent value of the dataset. Data can be multimodal if there is more than one value with the same frequency.

When using Statistics for Data Analytics, Mean is preferred when data is symmetrically distributed. However, when data possess skewed characteristics or ordinal type, the Median should be the optimal choice, and if the data type is categorical, the Mode is the best choice.

### 5) Variability

In Statistics, the dispersion of data from each other is referred to as variability. It gives an extent to which data can be stretched or squeezed. It can be better understood if we do a univariate analysis of features. A few key terms to be aware of when using Statistics for Data Analytics are:

• Interquartile Range [IQR]: The difference between the largest and smallest value is known as Range. If the data is partitioned into four parts, it is termed a Quartile, and the difference between the third and first Quartile is known as IQR. A box plot is used in such cases to determine Spread, Outliers, and IQR.
• Standard Deviation: A value that shows the amount of variation in population data is termed Population Standard Deviation. In a given distribution, the Standard Deviation is used to find how far one value lies from the other. In the actual case, the sample population is calculated where n is the size of the sample and n-1 is the considered sample size.
• Variance: An average squared deviation obtained on population is termed the Variance. In a given distribution, the Variance value tells us the degree of spread of data. Higher Variance indicates that data points are located away from the Mean.

### 6) Central Limit Theorem

Pierre-Simon Laplace, in 1830 introduced the first standard version of the Central Limit Theorem. It provides insight into population data by using the mean of the samples, and if the mean value of samples is plotted, it approaches a Normal Distribution that holds irrespective of the type of distribution of population.

It also states that the mean of means will be approximately equal to the mean of sample means. This theorem plays a major role when you use Statistics for Data Analytics and Data Science.

### 7) Conditional Probability and P-Value

Conditional Probability differs slightly from probability as here an outcome is expected given a relational event has already occurred. This concept is extended in Bayes theorem by which the naive Bayes algorithm is designed and applied for text classification.

Statistical tests often refer to the P-value where the probability of an event is calculated considering hypothesis conditions. If the p-value is less than the significant value (usually 0.05), the null hypothesis is rejected; else the null hypothesis is accepted.

## Significance of Hypothesis Testing

When you use Statistics for Data Analytics, the interpretation of data is progressed with some initial assumptions that usually have no relation existing among variables. This is called the “Null Hypothesis“. In contrast to the null hypothesis, exists an “Alternative Hypothesis“, which is determined by comparing the P-value with the significance level or alpha.

This p-value tells us whether to take the necessary actions or not. However, there can be Statistical errors introduced while testing data that are called type-1 and type-2 errors that occur if the true null hypothesis is rejected or the false null hypothesis is accepted.

## Application of Statistics for Data Analytics and Data Science

Statistics are no longer anticipated for calculating a measure of a quantity, it is now a necessity in every business domain for performing advanced Analytics.

The most important applications of Statistics for Data Analytics and Data Science are:

• Building ML algorithms: The association of Statistics with Data Science and Analytics has served as a base for several machine learning algorithms like logistic regression, naive Bayes, and many other algorithms that have evolved considering the importance of Statistical summary.
• Business Intelligence: Statistics is widely intended for business processes occurring in the industry, as we come to a conclusive term with a level of confidence. This confidence in results is used for predictions and forecasting of plans to be implemented with possible outcomes.

## Conclusion

This article talks about the vital role and importance of Statistics for Data Analytics and Data Science. It gives a brief overview of Statistical terms and explains their types and benefits. Furthermore, familiarity with the terms Statistics and the significance of hypotheses is described, as it is crucial in Data Science and Analytics.

Statistics play an important role in understanding a given feature’s behavior and the relation they inherit. Data Science and Analytics also involve knowledge of advanced Mathematics and Programming, and thereby Statistics serves as the first step towards understanding the Data Science and Analytics process. The results obtained by using Statistics for Data Analytics have helped in generating insights from data that drive business growth in the industry to stay ahead in the competitive world.

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