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

Data Science Dojo

Libraries and Tools: Libraries like Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, and Tableau are like specialized tools for data analysis, visualization, and machine learning. Algorithms: Decision trees, random forests, logistic regression, and more are like different techniques a detective might use to solve a case.

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

Data Science Dojo

Libraries and Tools: Libraries like Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, and Tableau are like specialized tools for data analysis, visualization, and machine learning. Algorithms: Decision trees, random forests, logistic regression, and more are like different techniques a detective might use to solve a case.

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Big Data Syllabus: A Comprehensive Overview

Pickl AI

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. It is built on the Hadoop Distributed File System (HDFS) and utilises MapReduce for data processing. Once data is collected, it needs to be stored efficiently.

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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. Tools like Tableau, Matplotlib, Seaborn, or Power BI can be incredibly helpful.

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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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Best Resources for Kids to learn Data Science with Python

Pickl AI

Begin by employing algorithms for supervised learning such as linear regression , logistic regression, decision trees, and support vector machines. It includes regression, classification, clustering, decision trees, and more. To obtain practical expertise, run the algorithms on datasets.

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Introduction to R Programming For Data Science

Pickl AI

Packages like dplyr, data.table, and sparklyr enable efficient data processing on big data platforms such as Apache Hadoop and Apache Spark. Esquisse: One of the most essential tableau features that has been introduced within the R libraries is Esquisse. You can simply drag and drop to complete your visualisation in minutes.