ODSC - Open Data Science

FEBRUARY 24, 2023

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.

Exploratory Data Analysis 126

Exploratory Data Analysis Data Visualization Data Analysis Data Analysis 126

Dataconomy

JULY 28, 2023

Integration also helps avoid duplication and redundancy of data, providing a comprehensive view of the information. Exploratory data analysis (EDA) Before preprocessing data, conducting exploratory data analysis is crucial to understand the dataset’s characteristics, identify patterns, detect outliers, and validate missing values.

Power BI 91

Power BI Data Preparation Exploratory Data Analysis Machine Learning 91

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.

Data Analysis 52

Data Analysis Data Analysis EDA Data Mining 52

Pickl AI

JULY 25, 2023

Their primary responsibilities include: Data Collection and Preparation Data Scientists start by gathering relevant data from various sources, including databases, APIs, and online platforms. They clean and preprocess the data to remove inconsistencies and ensure its quality. Big Data Technologies: Hadoop, Spark, etc.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Mlearning.ai

JULY 11, 2023

For instance, feature engineering and exploratory data analysis (EDA) often require the use of visualization libraries like Matplotlib and Seaborn. Moreover, tools like Power BI and Tableau can produce remarkable results.

Data Science 40

Data Science Exploratory Data Analysis Azure Power BI 40