Dataconomy

JUNE 13, 2023

At its core, decision intelligence involves collecting and integrating relevant data from various sources, such as databases, text documents, and APIs. This data is then analyzed using statistical methods, machine learning algorithms, and data mining techniques to uncover meaningful patterns and relationships.

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Pickl AI

SEPTEMBER 12, 2024

Summary : This article equips Data Analysts with a solid foundation of key Data Science terms, from A to Z. Introduction In the rapidly evolving field of Data Science, understanding key terminology is crucial for Data Analysts to communicate effectively, collaborate effectively, and drive data-driven projects.

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IBM Journey to AI blog

JULY 6, 2023

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.

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Pickl AI

JULY 12, 2024

Pandas: A powerful library for data manipulation and analysis, offering data structures and operations for manipulating numerical tables and time series data. Scikit-learn: A simple and efficient tool for data mining and data analysis, particularly for building and evaluating machine learning models.

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Pickl AI

JULY 31, 2023

It constructs a hyperplane to separate different classes during training and uses it to make predictions on new data. Decision Trees : Decision Trees are another example of Eager Learning algorithms that recursively split the data based on feature values during training to create a tree-like structure for prediction.

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Pickl AI

OCTOBER 3, 2024

Predictive analytics refers to the use of statistical algorithms and Machine Learning techniques to analyse historical data and predict future events or outcomes. It involves various processes, including data mining, predictive modelling, and statistical analysis.

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Mlearning.ai

MAY 23, 2023

Once the data is acquired, it is maintained by performing data cleaning, data warehousing, data staging, and data architecture. Data processing does the task of exploring the data, mining it, and analyzing it which can be finally used to generate the summary of the insights extracted from the data.

Data Science 52

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Pickl AI

SEPTEMBER 23, 2024

Synergy Between Artificial Intelligence and Data Science AI and Data Science complement each other through their unique but interconnected roles in data processing and analysis. Data Science involves extracting insights from structured and unstructured data using statistical methods, data mining, and visualisation techniques.

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Pickl AI

MAY 24, 2023

Uses: The primary use for the Scikit-Learn emphasises on the implementation of standard machine learning tasks and data mining tasks that contains high number of algorithms. Its modified feature includes the cross-validation that allowing it to use more than one metric.

Data Science 52

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Pickl AI

JULY 18, 2023

It includes statistical analysis, predictive modeling, Machine Learning, and data mining techniques. It is helpful in descriptive and inferential statistics, regression analysis, clustering, decision trees, neural networks, and more. Advanced Analytics: SAS offers a comprehensive set of advanced analytics capabilities.

Data Science 52

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Dataconomy

SEPTEMBER 1, 2023

Several data mining and neural network techniques have been employed to gauge the severity of heart disease but the prediction of it is a different subject. Hybrid machine learning techniques enhance model interpretability by combining methodologies that shed light on the model’s decision-making process.

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DagsHub

APRIL 21, 2024

Scikit-learn Scikit-learn is a machine learning library in Python that is majorly used for data mining and data analysis. It offers implementations of various machine learning algorithms, including linear and logistic regression , decision trees , random forests , support vector machines , clustering algorithms , and more.

Machine Learning 59

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Data Science Dojo

AUGUST 30, 2023

Machine learning algorithms Machine learning forms the core of Applied Data Science. It leverages algorithms to parse data, learn from it, and make predictions or decisions without being explicitly programmed.

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