Remove Decision Trees Remove SQL Remove Supervised Learning
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Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

Decision Trees: A supervised learning algorithm that creates a tree-like model of decisions and their possible consequences, used for both classification and regression tasks. Inductive Learning: A type of learning where a model generalises from specific examples to broader rules or patterns.

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Data science vs. machine learning: What’s the difference?

IBM Journey to AI blog

Both data science and machine learning are used by data engineers and in almost every industry. It’s unnecessary to know SQL, as programs are written in R, Java, SAS and other programming languages. Python is the most common programming language used in machine learning.

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Top 50+ Data Analyst Interview Questions & Answers

Pickl AI

It covers essential topics such as SQL queries, data visualization, statistical analysis, machine learning concepts, and data manipulation techniques. Key Takeaways SQL Mastery: Understand SQL’s importance, join tables, and distinguish between SELECT and SELECT DISTINCT. How do you join tables in SQL?

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Top 10 Data Science Interviews Questions and Expert Answers

Pickl AI

Technical Proficiency Data Science interviews typically evaluate candidates on a myriad of technical skills spanning programming languages, statistical analysis, Machine Learning algorithms, and data manipulation techniques. Differentiate between supervised and unsupervised learning algorithms.

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Best Resources for Kids to learn Data Science with Python

Pickl AI

Explore Machine Learning with Python: Become familiar with prominent Python artificial intelligence libraries such as sci-kit-learn and TensorFlow. Begin by employing algorithms for supervised learning such as linear regression , logistic regression, decision trees, and support vector machines.

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Big Data Syllabus: A Comprehensive Overview

Pickl AI

Understanding the differences between SQL and NoSQL databases is crucial for students. Big Data and Machine Learning The intersection of Big Data and Machine Learning is a critical area of focus in a Big Data syllabus. Students should learn how to leverage Machine Learning algorithms to extract insights from large datasets.