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Here are some key areas often assessed: Programming Proficiency Candidates are often tested on their proficiency in languages such as Python, R, and SQL, with a focus on data manipulation, analysis, and visualization. Clustering algorithms such as K-means and hierarchical clustering are examples of unsupervised learning techniques.
This could be linear regression, logistic regression, clustering , time series analysis , etc. Model Evaluation: Assess the quality of the midel by using different evaluation metrics, crossvalidation and techniques that prevent overfitting. This may involve finding values that best represent to observed data.
HypothesisTesting : Statistical Models help test hypotheses by analysing relationships between variables. These models help in hypothesistesting and determining the relationships between variables. Bayesian models and hypothesistests (like t-tests or chi-square tests) are examples of inferential models.
Concepts such as probability distributions, hypothesistesting , and Bayesian inference enable ML engineers to interpret results, quantify uncertainty, and improve model predictions. Unit testing ensures individual components of the model work as expected, while integration testingvalidates how those components function together.
Some of the most notable technologies include: Hadoop An open-source framework that allows for distributed storage and processing of large datasets across clusters of computers. Statistical Analysis Introducing statistical methods and techniques for analysing data, including hypothesistesting, regression analysis, and descriptive statistics.
Clustering: An unsupervised Machine Learning technique that groups similar data points based on their inherent similarities. Cross-Validation: A model evaluation technique that assesses how well a model will generalise to an independent dataset.
Techniques such as cross-validation, regularisation , and feature selection can prevent overfitting. Then, I would use clustering techniques such as k-means or hierarchical clustering to group customers based on similarities in their purchasing behaviour. In my previous role, we had a project with a tight deadline.
There are majorly two categories of sampling techniques based on the usage of statistics, they are: Probability Sampling techniques: Clustered sampling, Simple random sampling, and Stratified sampling. What is the p-value and what does it indicate in the Null Hypothesis? What is Cross-Validation?
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