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Various Techniques to Detect and Isolate Time Series Components Using Python

Analytics Vidhya

Decomposing time series components like a trend, seasonality & cyclical component and getting rid of their impacts become explicitly important to ensure adequate data quality of the time-series data we are working on and feeding into the model […] The post Various Techniques to Detect and Isolate Time Series Components Using Python appeared (..)

Python 291
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Nuclei Detection and Fluorescence Quantification in Python: A Step-by-Step Guide (Part 2)

Towards AI

Welcome back to the second tutorial in our series, Nuclei Detection and Fluorescence Quantification in Python. In this tutorial, we will focus on measuring the fluorescence intensity from the GFP channel, extracting relevant data, and performing a detailed analysis to derive meaningful biological insights.

Python 98
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Unraveling Data Anomalies in Machine Learning

Analytics Vidhya

Introduction In the realm of machine learning, the veracity of data holds utmost significance in the triumph of models. Inadequate data quality can give rise to erroneous predictions, unreliable insights, and overall performance.

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Understanding Data Science and Data Analysis Life Cycle

Pickl AI

Summary: The Data Science and Data Analysis life cycles are systematic processes crucial for uncovering insights from raw data. Quality data is foundational for accurate analysis, ensuring businesses stay competitive in the digital landscape. Data Cleaning Data cleaning is crucial for data integrity.

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What is The Difference Between Data Analysis and Interpretation?

Pickl AI

Summary: Data Analysis and interpretation work together to extract insights from raw data. Analysis finds patterns, while interpretation explains their meaning in real life. Overcoming challenges like data quality and bias improves accuracy, helping businesses and researchers make data-driven choices with confidence.

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Exploring Different Types of Data Analysis: Methods and Applications

Pickl AI

Summary: This article explores different types of Data Analysis, including descriptive, exploratory, inferential, predictive, diagnostic, and prescriptive analysis. Introduction Data Analysis transforms raw data into valuable insights that drive informed decisions. What is Data Analysis?

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11 Open Source Data Exploration Tools You Need to Know in 2023

ODSC - Open Data Science

There are many well-known libraries and platforms for data analysis such as Pandas and Tableau, in addition to analytical databases like ClickHouse, MariaDB, Apache Druid, Apache Pinot, Google BigQuery, Amazon RedShift, etc. These tools will help make your initial data exploration process easy.