Remove Cross Validation Remove Exploratory Data Analysis Remove Hypothesis Testing
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Top 10 Data Science Interviews Questions and Expert Answers

Pickl AI

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. What is cross-validation, and why is it used in Machine Learning?

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Popular Statistician certifications that will ensure professional success

Pickl AI

Data Science Bootcamp Pickl.AI This bootcamp includes a dedicated Statistics module covering essential topics like types of variables, measures of central tendency, histograms, hypothesis testing, and more. You will learn by practising Data Scientists. Data Science Job Guarantee Course Pickl.AI

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What is Regression Analysis?

Pickl AI

The process of conducting Regression Analysis typically involves several steps: Step 1: Data Collection: Gather relevant data for both dependent and independent variables. This data can come from various sources such as surveys, experiments, or historical records.

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Types of Statistical Models in R for Data Scientists

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Data Collection: Based on the question or problem identified, you need to collect data that represents the problem that you are studying. Exploratory Data Analysis: You need to examine the data for understanding the distribution, patterns, outliers and relationships between variables.

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Top 50+ Data Analyst Interview Questions & Answers

Pickl AI

Overfitting occurs when a model learns the training data too well, including noise and irrelevant patterns, leading to poor performance on unseen data. Techniques such as cross-validation, regularisation , and feature selection can prevent overfitting. In my previous role, we had a project with a tight deadline.

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Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

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.