Data Science Dojo

JANUARY 22, 2023

The data analysis process enables analysts to gain insights into the data that can inform further analysis, modeling, and hypothesis testing. EDA is an iterative process of conglomerative activities which include data cleaning, manipulation and visualization.

Exploratory Data Analysis 370

Exploratory Data Analysis EDA Data Analysis Data Analysis 370

Dataconomy

MAY 16, 2023

Data preprocessing and feature engineering: They are responsible for preparing and cleaning data, performing feature extraction and selection, and transforming data into a format suitable for model training and evaluation. They use data visualization techniques to effectively communicate patterns and insights.

Data Scientist 156

Data Scientist ML ML Machine Learning 156

learn data science

DECEMBER 30, 2020

Prediction Configuration for Base Level for Statisical Learning Models Visualization of Probability Distribution for Hypothesis Tests Test Mode for Cox Regression and Surivival Forest But, the most important one is the new Prediction capability. Text Data Wrangling UI When cleaning data, the text data is the most notorious.

Data Wrangling 52

Data Wrangling Hypothesis Testing Analytics Analytics 52

Pickl AI

MAY 30, 2024

Overview of Typical Tasks and Responsibilities in Data Science As a Data Scientist, your daily tasks and responsibilities will encompass many activities. You will collect and clean data from multiple sources, ensuring it is suitable for analysis. Data Cleaning Data cleaning is crucial for data integrity.

Data Analysis 52

Data Analysis Data Analysis Data Science Exploratory Data Analysis 52

Pickl AI

AUGUST 27, 2024

Statsmodels Allows users to explore data, estimate statistical models, and perform statistical tests. It is particularly useful for regression analysis and hypothesis testing. Pingouin A library designed for statistical analysis, providing a comprehensive collection of statistical tests.

Data Analysis 52

Data Analysis Data Analysis Python Data Analyst 52

Pickl AI

MAY 29, 2024

Skills in data manipulation and cleaning are necessary to prepare data for analysis. Data Scientists frequently use tools like pandas in Python and dplyr in R to transform and clean data sets, ensuring accuracy in subsequent analyses. Data Visualisation Visualisation of data is a critical skill.

Data Scientist 52

Data Scientist Data Science Machine Learning Machine Learning 52

Mlearning.ai

MAY 23, 2023

The following figure represents the life cycle of data science. It starts with gathering the business requirements and relevant data. Once the data is acquired, it is maintained by performing data cleaning, data warehousing, data staging, and data architecture. Why is data cleaning crucial?

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52