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How to become a data scientist – Key concepts to master data science

Data Science Dojo

Algorithms: Decision trees, random forests, logistic regression, and more are like different techniques a detective might use to solve a case. Data Visualization Think of data visualization as creating a visual map of the data.

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How to become a data scientist – Key concepts to master data science

Data Science Dojo

Algorithms: Decision trees, random forests, logistic regression, and more are like different techniques a detective might use to solve a case. Data Visualization Think of data visualization as creating a visual map of the data.

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Introduction to applied data science 101: Key concepts and methodologies 

Data Science Dojo

It leverages algorithms to parse data, learn from it, and make predictions or decisions without being explicitly programmed. From decision trees and neural networks to regression models and clustering algorithms, a variety of techniques come under the umbrella of machine learning.

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How to become a data scientist

Dataconomy

It involves developing algorithms that can learn from and make predictions or decisions based on data. Familiarity with regression techniques, decision trees, clustering, neural networks, and other data-driven problem-solving methods is vital. This is where data visualization comes in.

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Big Data Syllabus: A Comprehensive Overview

Pickl AI

Big Data Technologies and Tools A comprehensive syllabus should introduce students to the key technologies and tools used in Big Data analytics. 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.

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8 Best Programming Language for Data Science

Pickl AI

It is popular for its powerful data visualization and analysis capabilities. Hence, Data Scientists rely on R to perform complex statistical operations. With a wide array of packages like ggplot2 and dplyr, R allows for sophisticated data visualization and efficient data manipulation. Wrapping it up !!!

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

IBM Journey to AI blog

The fields have evolved such that to work as a data analyst who views, manages and accesses data, you need to know Structured Query Language (SQL) as well as math, statistics, data visualization (to present the results to stakeholders) and data mining.