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Does Data Science require Statistics?

Statistics is collecting, organising, analysing, interpreting, and presenting numerical data in large quantities to infer proportions about the population or targeted groups from the representative sample population.

For example:

As we know, Census helps collect the population’s demographic statistics to get deeper insights into how well government schemes are performing (like family planning, Beti Bachao Beti Padhao etc.). Knowing how much the population is below the poverty line is also helpful. This data helps in planning and implementing the requisite policy action, or how much proportion of people practices a certain religion which can help during elections etc.

Thus, this collection, organisation, analysis, and interpretation of data are termed statistics that can help get deeper insights about the population or target groups etc.

What is data science?

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, continuous and discrete data, to apply data insights in various application areas.

Relationship between Statistics and Data Science

Statistics in data science seek structure and relationships among various unflustered data. Data structuring aids in revealing various valuable insights hidden within your collected data. The insights you will gain from the data are basically digging out the possibilities of certain events; these possibilities in data science are known as Statistical Analysis.

For example: Let’s understand this through the recent spread of Monkeypox disease. This new disease has become a global emergency. After thorough analysis, World Health Organisation observes that in 95% of the cases, monkeypox spreads through gay or bisexual men as a Sexually Transmitted Infection. This information has helped World Health Organisation release necessary guidelines or advisory to control the spread of monkeypox.

However, you can not discover these truths or facts with the help of gathering isolated, unorganised or unstructured data. Statistics help present data in structured forms using tools such as pie charts and bar graphs that can help individuals (without technical background) understand the crux of the analysed data.

Application of Statistics in Data Science

The following are the applications of statistics in Data Science:

  1. Classification

Classification is an umbrella term used for data mining techniques. During the data mining process, you have to divide the data into subsets based on various criteria. The criteria for division could be:

  • research, or
  • objectives of the researcher/ individual, or
  • patterns discovered through data visualisation and sampling.

Data classification plays a prevalent role in data science as it helps transform the unstructured data from a liability into an asset. An accurate classification can help devise crucial strategies to find a solution on the problem areas. The classification of data also helps make data visualisation easier, which helps in grasping the necessary information easily. Along with this, the classification of data can help spot trends like when there is a structural break in the data or trends like jobless growth, which India experienced during 2008 crisis or why there is a downward trend in housing prices in the U.S. during the 2008 recession etc.

  1. Regression

Regression is a way of determining the relationship between the independent and dependent variables. It can be a Linear regression with one independent variable or regressor or Multi Linear regression with more than one independent variable or regressor.

For example, You can regress data of family expenditure on income and the number of family members to find whether there exists a relationship or not.

The Regression also helps predict the qualitative responses of the unknown variables through the observed pattern in the data. For example, One can predict whether the unemployment rate will rise or fall based on the inflation or GDP growth pattern. When GDP rises, the unemployment rate falls, and the unemployment rate rises in the economy.

Data Science uses the regression technique to find the relationship between the variables. This helps to estimate values and probability distributions. It also helps reduce assumptions, improving the model’s predictive powers. It also can help predict outcomes based on precise inferences rather than assumptions.

In other words, the term ‘regression,’ in statistics and machine learning, refers to how data can be fit into a straight line and how each point on the straight line provides some insight. You can learn machine learning as tasks that can be solved without being explicitly programmed. They talk about how a line can fit a given set of data points and then extrapolate to make predictions.

  1. Re-sampling Methods

Re-sampling is a common method for analysing large data samples unbiased and precisely. The technique eliminates the uncertainty of population parameters when analysing massive amounts of data. The re-sampling method draws samples from large amounts of data to obtain a small and unique sampling distribution representing the original data. The technique encompasses all possible research outcomes, improving accuracy and decreasing bias. Also, the re-sampling method draws samples from large amounts of data to obtain a small and unique sampling distribution representing the original data. The technique encompasses all possible research outcomes, improving accuracy and decreasing bias.

This data science technique helps increase the account for variability present in data, thereby increasing the efficiency of the estimates. This further helps make the probability distributions that will help make predictions through precise inferences from the data.

Conclusion

The above points rightly represent the significance of statistics for data science like-

  • Identifying the relationship between features to rule out the possibility of duplicate features.
  • Converting the features to the necessary format.
  • Data normalisation and scaling also include determining the distribution of data and the type of data.
  • Taking the data for further processing by making the necessary changes to the data.
  • Determining the best mathematical approach/model after processing the data. Once you obtain the results, they will be validated using the various accuracy measurement scales.

Hence, Statistics is significant for every stage of data processing, from the beginning to the end of the entire cycle. That is why a good statistician can also be a good Data Scientist. Though statistics plays a pivotal role in data science, you also need to learn requisite and necessary skills like programming to become a data scientist. You can enrol yourself in a statistics course to get a foundational knowledge of the concepts involved in both data science and statistics.

You may even like: 8 Best Data Analysis Tools for Researchers for 2022

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