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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Pickl AI

APRIL 3, 2025

Exploratory Data Analysis (EDA): Using statistical summaries and initial visualisations (yes, visualisation plays a role within analysis!) to understand the data’s main characteristics, distributions, and relationships. Clean Data: Handle missing addresses, standardize purchase dates, remove test accounts.

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Data Analysis Data Analysis Data Visualization EDA 52

Pickl AI

JUNE 7, 2023

Analysing Netflix Movies and TV Shows One of the most enticing real-world Data Science projects Github can include the project focusing to analyse Netflix movies and TV shows. Using Netflix user data, you need to undertake Data Analysis for running workflows like EDA, Data Visualisation and interpretation.

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Pickl AI

JULY 25, 2023

Unfolding the difference between data engineer, data scientist, and data analyst. Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. These models may include regression, classification, clustering, and more.

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Pickl AI

OCTOBER 14, 2024

Exploratory Data Analysis (EDA) Exploratory Data Analysis (EDA) is an approach to analyse datasets to uncover patterns, anomalies, or relationships. The primary purpose of EDA is to explore the data without any preconceived notions or hypotheses.

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Becoming Human

MAY 15, 2023

Data Analysis also helps you to prepare your data for predictive modeling, and it is also a specific field in Data Science. There is a position called Data Analyst whose work is to analyze the historical data, and from that, they will derive some KPI s (Key Performance Indicators) for making any further calls.

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Pickl AI

SEPTEMBER 23, 2024

PCA is the go-to method when your primary goal is data compression without losing much information, especially when dealing with high-dimensional datasets. PCA is also commonly used in exploratory Data Analysis (EDA) when the aim is to detect patterns and relationships between variables before building more complex models.

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