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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. Suppose you are a data scientist working for an e-commerce company.

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

Pickl AI

Proficient in programming languages like Python or R, data manipulation libraries like Pandas, and machine learning frameworks like TensorFlow and Scikit-learn, data scientists uncover patterns and trends through statistical analysis and data visualization. Data Visualization: Matplotlib, Seaborn, Tableau, etc.

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Data Scientist Salary in India’s Top Tech Cities

Pickl AI

Here is the tabular representation of the same: Technical Skills Non-technical Skills Programming Languages: Python, SQL, R Good written and oral communication Data Analysis: Pandas, Matplotlib, Numpy, Seaborn Ability to work in a team ML Algorithms: Regression Classification, Decision Trees, Regression Analysis Problem-solving capability Big Data: (..)

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Top 50+ Data Analyst Interview Questions & Answers

Pickl AI

This comprehensive blog outlines vital aspects of Data Analyst interviews, offering insights into technical, behavioural, and industry-specific questions. It covers essential topics such as SQL queries, data visualization, statistical analysis, machine learning concepts, and data manipulation techniques.

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7-Steps to Perform Data Visualization Guide for Success

Pickl AI

Steps to Perform Data Visualization: Data visualization is the presentation of information and statistics using visual tools that include charts, graphs, and maps. Its goal is to create patterns in data, trends, and anomalies comprehensible to both data professionals and people without technical knowledge.

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How to Build a Data Analyst Portfolio?

Pickl AI

Data Cleaning is a crucial step in any data analysis process, and it’s important to showcase your ability to handle messy data effectively. Data Visualization: Create compelling and informative Data Visualizations. Visual Appeal: Use clean and visually appealing Data Visualizations.