Importance of Statistics for Data Science

In 8th class, Statistics used to be one of the easiest chapters of all in the mathematics section and that was actually the real purpose of it to combine different types of data and to present it in an adequate and neat way.

 

Nobody at that age and IQ level would understand the use of it but now in today’s world, it has become the norm to process data through statistics so that it becomes trouble-free for others to understand it and pick something valuable and informative out of it.  

 

To put it in simple words, statistics is the basic use of mathematics in formulating a technical analysis of data. It is used to process complex problems in the real world so that data scientists and analysts can look for meaningful trends and changes in Data.

 

Different statistical techniques and functions, principles and algorithms work together to provide us with an ideal Statistical model.

 

If the data taken is a sample from a larger population, then the data scientist or the analyst is supposed to assume patterns and interpret them as data from the large population solely based on the results of the sample size that was taken earlier. This may seem like a scary yet bold step but you would be surprised by its accuracy.

 

Statistical analysis has proven to be an elite way to analyse and interpret data in various different fields such as the psychology, business, physical and social sciences, production and manufacturing, government, etc.

 

On the other side, Data Science is the perfect blend of business, mathematics, computer science and communication.

 

As Wikipedia search for Data Science reads, ”Data Science is a concept to unify statistics, data analysis, and their related methods in order to understand and analyse the actual phenomena with data.” It is using different algorithms, patterns from structured or unstructured data to form insights and gain knowledge about any field of play.

 

It is primarily used to make decisions and predictions making use of predictive casual analytics, prescriptive analytics (predictive plus decision science) and machine learning. However, all these analyses are described in another blog, i.e. types of statistical analysis. 

 

Data Science is just like any other science requiring firstly to define a problem. Then collect and leverage data to counteract with solutions and test the solution if it's applicable on the given problem. 

 

Importance of Statistics in Data Science

 

As we know that Data Science is the study of data in different forms to make healthy assumptions about behaviours and tendencies and to make these assumptions the information needs to be organised according to the concepts of statistics so that the study becomes easy and hence the findings become more accurate.

 

When the data is big and unorganised, statistics plays a powerful role in that situation. When a company uses statistics to find insights, it makes the tedious task look minimalist and easy in front of the big and buffer information that was provided earlier.

 

Statistics eradicate the unwanted information and catalogues the useful data in an effortless way making the humongous task of organising inputs seem so futile and serene.

 

Some ways in which Statistics helps in Data Science are:

 

1. Prediction and Classification: Statistics help in prediction and classification of data whether it would be right for the clients viewing by their previous usage of data.

 

2. Helps to create Probability Distribution and Estimation: Probability Distribution and Estimation are crucial in understanding the basics of machine learning and algorithms like logistic regressions. 

 

Cross-validation and LOOCV techniques are also inherently statistical tools that have been brought into the Machine Learning and Data Analytics world for inference-based research, A/B and hypothesis testing.

 

3. Pattern Detection and Grouping:  Statistics help in picking out the optimal data and weeding out the unnecessary dump of data for companies who like their work organised. It also helps spot out anomalies which further helps in processing the right data.

 

4. Powerful Insights: Dashboards, charts, reports and other data visualizations types in the form of interactive and effective representations give much more powerful insights than plain data and it also makes the data more readable and interesting. 

 

5. Segmentation and Optimization: It also segments the data according to different kinds of demographic or psychographic factors that affect its processing. It also optimizes data in accordance with minimizing risk and maximizing outputs.

 

Apart from that, some of the statistical methods are also imperative approaches while analyzing complex data, some are discussed below. 

 

 

Descriptive and Inferential Statistics for Data Analysis

 

There are 2 main categories in the statistics department-

 

1. Descriptive Statistics

 

Descriptive Statistics churns the data to provide a description of the population by relying on the characteristics of data providing parameters.

 

For eg- In a class, if we need to find average marks of a student in a test, in the descriptive analysis we would note the marks of every student in the class and then would note the highest marks obtained by a student, the lowest marks and the average of the class.

 

(Related read: Descriptive Statistics in R)

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Types of statistics: Descriptive and inferential

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2. Inferential Statistics

 

Inferential Statistics makes predictions and assumptions regarding a large population by the trends prevalent in a sample taken from the same.

 

For eg. - In the recent past, many clinical trials have been done for the CoronaVirus vaccine and for the people are being chosen at random as a sample size from the immense population of different geographical locations.

 

 

Decoding the Descriptive Analysis

 

Whenever Descriptive Analysis is practised, it is always done around a central measurement which actually plays a huge role in determining the results. These central parameters are the Mean, Median and Mode.

 

Let’s throw some light on these measurements:

 

Measures of the Center

 

1. MEAN-  Measure of an average of all the values in a sample is called Mean. 

 

Eg.-  If we need to find the mean of the marks obtained by the students of a class we will take the sum of all the marks and divide it by the total number of students.

 

 

2. MEDIUM- Measure of the central value of the sample set is called Median.

 

Eg.- if we need to find the medium of the marks obtained by the students of a class we will arrange them in ascending or descending order of marks and the value in the exact middle of the size taken will be considered its medium.

 

 

3. MODE- The value most recurrent in the sample set is known as Mode.

 

Eg.- if we need to find the model of the marks obtained by the students of a class we will see the most recurring marks that most of the students have received, and that will become the model of the class marks obtained.

 

 

Decoding the Inferential Statistics

 

Inferential Statistics is more prevalent in studying human nature and understanding the characteristics of the living. To analyse the trends of a general population, we take a random sample and study the properties of it. Then we test the findings, whether they comply with the general population accurately or not and then finally provide results with conclusive evidence.

 

Statisticians use hypothesis testing to formally check whether the hypothesis is accepted or rejected. Hypothesis testing is an Inferential Statistical technique used to determine whether the evidence stands tall in a data sample to infer that a certain condition complies with the entire population.

 

 

Statistical Data Analysis

 

Finding structures and making assumptions on it is the most predominant step of Statistical Data Analysis. Some useful Statistical Data Analysis Methods are:

 

 

Hypothesis Testing

 

As discussed above, Hypothesis Testing is one of the most important methods of analysis. Also, hypotheses are the natural links between underlying theory and statistics. Recurring usage of specific data in different tests allows the hypothesis to be more accurate.

 

(Most related: What is p-value in statistics?) 

 

 

Classification

 

Classification is the most common method to define sub-populations from data. Now in the age of Big Data, it has become a necessity to look upon traditional methods such as Classification because the number of observations or the number of features tends to increase which makes the calculations too difficult.

 

 

Regression

 

Regression methods are the main tool to find global and local relationships between features when the target variable is measured. Simple Linear Regression is the most commonly used method for working within exponents. For more big data functional regression and quantile regression are used.

 

 

Time Series Analysis

 

Time series analysis is used to comprehend data and predict time intervals or temporal structure. Time series are very common in studies of observational data, and prediction is the most important challenge for such data. Its expertise is most commonly used in sectors of engineering, behavioural sciences, economics and natural sciences.

 

Conclusion

 

Nowadays it has become very hectic to even waste a minute on something not worth it and our lifestyles also reflect so. Everybody loves if their task is cut to the chase and is made viewer-friendly. 

 

Statistics have been up to the task since it was discovered and now the people have actually understood how wonderful it is. It has made the life of many sectors easy and Data Science is one of these.

 

On the other side, data science is the rage of the new and without it, many supreme decisions could not have been possible. So, it would be safe to say we would not be where we are without Data Science and hence without Statistics.