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9 important plots in data science

Data Science Dojo

KS Plot (Kolmogorov-Smirnov Plot): The KS Plot is a powerful tool for comparing two probability distributions. This plot is particularly useful for tasks like hypothesis testing, anomaly detection, and model evaluation. Explore, analyze, and visualize data using Power BI Desktop to make data-driven business decisions.

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The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering

Pickl AI

These models may include regression, classification, clustering, and more. Statistical Analysis: Hypothesis testing, probability, regression analysis, etc. Excel, Tableau, Power BI, SQL Server, MySQL, Google Analytics, etc. Machine Learning: Supervised and unsupervised learning techniques, deep learning, etc.

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Top 50+ Data Analyst Interview Questions & Answers

Pickl AI

Then, I would use clustering techniques such as k-means or hierarchical clustering to group customers based on similarities in their purchasing behaviour. Data Analyst Master Program by Simplilearn Comprehensive Learning Master descriptive and inferential statistics, hypothesis testing, regression analysis, and more.

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How to Build a Data Analyst Portfolio?

Pickl AI

Data analysts build interactive dashboards, charts, graphs, and infographics using a variety of programmes and libraries like Tableau , Power BI , or Python’s Matplotlib and Seaborn. For Data Analysts to conduct statistical analyses on data, a strong foundation in statistics and mathematical ideas is essential.

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7-Steps to Perform Data Visualization Guide for Success

Pickl AI

By visualizing data distributions, scatter plots, or heatmaps, data scientists can quickly identify outliers, clusters, or trends that might go unnoticed in raw data. By enabling users to interact with visual representations, Data Scientists can encourage deeper analysis, hypothesis testing, and knowledge discovery.