Introduction
While statistical theorems form the basis of data analysis, these theorems can be extended beyond basic applications to advanced levels that enable data analysts and scientists to fully leverage the potential of data science technologies. For instance, an entry level Data Science Course in Chennai might cover the basic theorems of statistics as applicable in data analysis while an advanced-level course will include teaching learners some advanced theorems of statistics as well as how those theorems are applied in data science.
Advanced Statistics in Data Science
Following are some of the advanced statistical techniques used in data science:
- Bayesian Inference: Unlike traditional frequentist statistics, Bayesian inference allows for the incorporation of prior knowledge or beliefs about a parameter of interest to make probabilistic inferences. This approach is particularly useful when dealing with small sample sizes or when prior information is available.
- Time Series Analysis: Time series data, which consists of observations collected over time, requires specialised techniques for analysis. This includes methods for trend analysis, seasonal decomposition, forecasting, and detecting anomalies or patterns within the data.
- Survival Analysis: Survival analysis is used to analyse time-to-event data, such as the time until a patient experiences a particular outcome or the time until failure of a mechanical component. Techniques like Kaplan-Meier estimation and Cox proportional hazards regression are commonly used in this context.
- Spatial Statistics: Spatial statistics deals with data that have a spatial component, such as geographic locations. Techniques like spatial autocorrelation, spatial interpolation, and point pattern analysis are employed to analyse and model spatial relationships and patterns.
- Machine Learning: While machine learning is often associated with predictive modelling, it also involves advanced statistical techniques—such as ensemble methods (for example, random forests, gradient boosting), dimensionality reduction (for example, principal component analysis, t-distributed stochastic neighbour embedding), and deep learning (for example, neural networks, convolutional neural networks)—that go beyond the fundamental concepts of statistics typically covered in an introductory Data Science Course.
- Causal Inference: Causal inference aims to identify causal relationships between variables based on observational data. Techniques like propensity score matching, instrumental variables, and structural equation modelling are used to mitigate confounding and estimate causal effects.
- Multilevel Modelling: Multilevel modelling, also known as hierarchical or mixed-effects modelling, is used to analyse data with a nested structure, such as individuals within groups or repeated measures over time. This approach allows for the estimation of both within-group and between-group effects.
- Nonparametric Methods: Nonparametric methods do not rely on specific assumptions about the underlying distribution of the data. Techniques like kernel density estimation, rank-based tests, and bootstrapping are topics covered in a Data Science Course that focuses on advanced statistical techniques in view of the usefulness of these techniques in dealing with data that may not meet the assumptions of parametric models.
- Text Mining and Natural Language Processing (NLP): Text mining and NLP techniques are used to analyse and extract information from unstructured text data. This includes methods for sentiment analysis, topic modelling, named entity recognition, and document classification. NLP techniques demystify complex data analytics methods and render them simple enough to be comprehensible for non-technical persons. In commercial applications of data science, data analysts need to collaborate with business strategists and decision makers who might not be technical experts. Many organisations in commercialised cities where data science is employed for realising business objectives encourage their workforce to gain expertise in NLP. Thus, a professional Data Science Course in Chennai would have many learners, professionals in both technical and non-technical roles, seeking to acquire expertise in NLP.
- Network Analysis: Network analysis involves the study of relationships or interactions between entities represented as nodes and edges in a network. Techniques such as centrality measures, community detection, and network visualisation are used to analyse and interpret complex networks.
Conclusion
By mastering these advanced statistical techniques, data scientists can tackle a broader range of analytical challenges and extract deeper insights from complex and diverse datasets. This article presented an overview of some advanced statistical techniques covered in a Data Science Course that goes beyond the general application of statistics in data sciences. However, depending on specific areas and domains of application, there are several other statistical techniques that reinforce the efficacy of data science technologies and enhance their usefulness.
BUSINESS DETAILS:
NAME: ExcelR- Data Science, Data Analyst, Business Analyst Course Training Chennai
ADDRESS: 857, Poonamallee High Rd, Kilpauk, Chennai, Tamil Nadu 600010
Phone: 8591364838
Email- enquiry@excelr.com
WORKING HOURS: MON-SAT [10AM-7PM]
