Statistical Methods Every Data Scientist Should Know
Advances in technology have improved the way data is collected, but as information piles up, it becomes increasingly complex to organize, manipulate and communicate it. Several researchers agree that Data Science is crucial to understanding large amounts of data. If you want to make important, high-quality, risk-controlled decisions that are based on conclusions about the world beyond the available data, you will need to add an additional and mandatory skill, which is statistics for data science and the corresponding statistical methods.
The key to mastering Data Science is to acquire advanced skills in Applied Statistics, which, in general, is the science that deals with the collection, organization, presentation, analysis, and interpretation of numerical data in order to make effective and pertinent decisions. Statistical methods for data science are traditionally used to organize and summarize numerical data. Descriptive Statistics, for example, deals with the tabulation of data, its presentation in graphic or illustrative form, and the calculation of descriptive measures.
To learn more about data science, read our blog on – What is data science?
Statistics used in data science and data processing makes sense of the information to extract important patterns and trends, and to understand “what the data says.” The statistical contribution in Data Science includes the descriptive analysis of the same, as well as the analysis and interpretation of statistical tables and graphs, in addition to regression techniques efficiently applied in predictive models.
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Applications of Statistics for Data Science
Overall, we can say that statistics for data science helps to –
- Apply statistical analysis techniques to solve real problems
- Discover the valuable information that the data contains
- Generate predictions based on data
- Communicate your results properly
- Draw conclusions that facilitate decision-making in complex situations
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What Are the Most Popular Statistical Methods in Data Science?
Below are some of the most popular statistical methods in data science, being extensively used by data scientists, data engineers, machine learning experts, etc.
Descriptive Analysis
It is the simplest. Its purpose is to describe a data set, thus obtaining the parameters that distinguish the characteristics of a data set. The reasons for carrying out descriptive analysis are that it allows knowing in detail the information that is possessed and to know the way in which the information is structured. It is limited to making deductions directly from the data and parameters obtained.
Exploratory Analysis
The exploratory analysis consists of a set of statistical techniques whose purpose is to achieve a basic understanding of the data, allowing to detect of outstanding characteristics, such as unexpected and outliers. Exploratory analysis should be the first stage of any data analysis, to prevent erroneous or unexpected data from being processed inappropriately. It is supported by a descriptive approach and is done without accepting preconceptions about the content of the data information.
Application of this statistics for data science technique makes it possible to study the trend, distribution, and shape of each of the indicators, to study normality on a set of indicators and if this criterion is not met, this analysis provides guidance on the type of transformation that must be submitted to the data.
Inferential Analysis
It aims to demonstrate hypotheses raised by providing conclusions with a certain probability or level of confidence, that is, there is no absolute certainty. It is important to note that to perform the inferential analysis, the same dataset should not be used to generate the hypothesis (used in the exploratory analysis) since there would be bias and the conclusions could not be valid.
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Predictive Analysis
Predictive analysis is based on the identification of relationships between variables in past events, to then exploit these relationships and predict possible results in future situations. While inferential analysis is concerned with understanding and demonstrating the relationship, predictive analysis is only concerned with value and does not seek in any case to understand the system or the relationship between elements.
This process uses the data together with analytical, statistical, and machine learning techniques to create a predictive model. A predictive analysis model is developed using a training data set and then tested (with a different data set) and validated for accuracy.
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Causal Analysis
Causal analysis helps to find the root cause of a problem instead of finding the symptoms. This technique helps to uncover the facts that lead to a certain situation. It makes it possible to relate causes to effects and the degree to which they affect each other. The use of causal inference and modeling techniques is the key to effectively investigating and solving problems that can affect the outcomes.
Exploratory Data Analysis
Data scientists use the EDA technique to analyze and investigate data sets and summarize their main characteristics. It also involves using data visualization methods and determining how to manipulate data sources in the most efficient way to get the desired answers. ED technique helps to discover patterns, spot anomalies, test a hypothesis, or check assumptions.
Read More – Exploratory Data Analysis Basics – The Essential Guide
Linear Regression
Linear regression is a statistical modeling technique that helps to describe a continuous response variable as a function of one or more predictor variables. It can help to understand and predict the behavior of complex systems or to analyze experimental, financial, and biological data.
Must Read – Most Popular Regression in Machine Learning Techniques
Linear regression techniques allow you to create a linear model. A linear regression model equation is:
Image Source – Medium
Logistic Regression
Logistic regression is a regression method and among the most popular statistical methods that allow estimating the probability of a binary qualitative variable as a function of a quantitative variable. One of the main applications of logistic regression is binary classification, in which observations are classified into one group or another depending on the value of the variable used as a predictor.
Image Source – Towards Data Science
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The fact of having large volumes of data makes it difficult to extract accurate and useful information for the purposes of understanding complex processes and phenomena. For this reason, statistics used in data science, accompanied by computational algorithms “for learning and obtaining knowledge“, are giving rise to an area that is expected to have great dynamism in the coming years.
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Rashmi is a postgraduate in Biotechnology with a flair for research-oriented work and has an experience of over 13 years in content creation and social media handling. She has a diversified writing portfolio and aim... Read Full Bio
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