Statistics in Data Science


Statistics plays a crucial role in data science across various stages of the data analysis process. This article briefly describes how statistics are utilised in data science.

Role of Statistics in Data Science

Data sciences draw heavily from statistics and probability. An advanced Data Science Course will require the learner to have a substantial background in this branch of mathematics while a basic course might cover those principles of statistics that are used in data science. The following sections describe the statistical entities and principles that are commonly used in data science.

Descriptive Statistics: Descriptive statistics are used to summarise and describe the basic features of the data. This includes measures such as mean, median, mode, standard deviation, range, percentiles, and quartiles. These concepts of statistics help in understanding the central tendency, dispersion, and shape of the data distribution. Often, a background in descriptive statistics is a prerequisite for learning data science. Thus, before you enrol for a data science course in Delhi or Bangalore, go through the course syllabus and check whether the coverage it offers on these basic concepts of statistics is adequate for you. If not, it is recommended that you first build the necessary background in descriptive statistics before enrolling for the course. 

Inferential Statistics: Inferential statistics are used to make inferences or predictions about a population based on a sample of data. Techniques like hypothesis testing, confidence intervals, and regression analysis are commonly used in data science to conclude the underlying population from the observed sample data.
Probability Theory: Probability theory is fundamental to understanding uncertainty and randomness in data. Concepts such as probability distributions, conditional probability, Bayes' theorem, and random variables are used extensively in data science for modelling and analysis.
Statistical Modelling: Statistical models are used to describe the relationship between variables in the data and to make predictions or estimates based on the observed data. Linear regression, logistic regression, time series analysis, and multivariate analysis are examples of statistical models commonly used in data science.
Experimental Design: Experimental design involves designing studies and experiments to collect data systematically and efficiently. Statistical techniques such as randomised controlled trials, factorial design, and A/B testing are used to design experiments, analyse results, and draw valid conclusions.
Data Sampling: Sampling techniques are used to select a representative subset of data from a larger population for analysis. Methods such as simple random sampling, stratified sampling, and cluster sampling are employed to ensure that the sample accurately reflects the characteristics of the population. Sampling techniques in the context of data analysis is a mandatory topic in any data science course. Advanced courses include training on advanced sampling techniques, which are required for analysing large volumes of data distributed across multiple datasets.  
Data Visualisation: Visualisation techniques are used to visually explore and analyse data. Statistical graphics such as histograms, box plots, scatter plots, and heat maps help in understanding patterns, trends, and relationships in the data. Visualisation also helps data analysts convey their findings to decision-makers in a format that can be easily comprehended by non-technical persons. A professional Data Science Course in Delhi, Bangalore, or Chennai will have a substantial focus on graphics because businesses require close collaboration between technical and non-technical resources.  


Overall, a solid understanding of statistics is essential for data scientists to effectively analyse data, draw meaningful insights, and make informed decisions based on data-driven evidence. 

While an entry-level data science course will include refresher topics that revisit the fundamental concepts of statistics, advanced-level courses would presume that the learner already has the required background in statistics. Before you enrol for a course in data science, ensure that you have the background in statistics that is prescribed for the course. 

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