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

Pickl AI

APRIL 3, 2025

Modeling & Algorithms: Applying statistical models (like regression, classification, clustering) or Machine Learning algorithms to identify deeper patterns, make predictions, or classify data points. Clean Data: Handle missing addresses, standardize purchase dates, remove test accounts.

Data Analysis 52

Data Analysis Data Analysis Data Visualization EDA 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 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

MAY 29, 2024

Knowledge of supervised and unsupervised learning and techniques like clustering, classification, and regression is essential. This skill allows the creation of predictive models and insights from data. Data Manipulation and Cleaning Raw data is often messy and unstructured.

Data Scientist 52

Data Scientist Data Science Machine Learning Machine Learning 52

Pickl AI

SEPTEMBER 12, 2024

Data cleaning identifies and addresses these issues to ensure data quality and integrity. Data Analysis: This step involves applying statistical and Machine Learning techniques to analyse the cleaned data and uncover patterns, trends, and relationships.

Data Analyst 52

Data Analyst 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