Pickl AI

NOVEMBER 2, 2023

A cheat sheet for Data Scientists is a concise reference guide, summarizing key concepts, formulas, and best practices in Data Analysis, statistics, and Machine Learning. It serves as a handy quick-reference tool to assist data professionals in their work, aiding in data interpretation, modeling , and decision-making processes.

Data Scientist 52

Data Scientist Data Science Data Visualization Machine Learning 52

Pickl AI

DECEMBER 16, 2024

Step 3: Data Preprocessing and Exploration Before modeling, it’s essential to preprocess and explore the data thoroughly.This step ensures that you have a clean and well-understood dataset before moving on to modeling. Cleaning Data: Address any missing values or outliers that could skew results.

AI 52

AI AI Machine Learning Machine Learning 52

Pickl AI

SEPTEMBER 12, 2024

Data Cleaning: Raw data often contains errors, inconsistencies, and missing values. Data cleaning identifies and addresses these issues to ensure data quality and integrity. Data Visualisation: Effective communication of insights is crucial in Data Science.

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. What is Cross-Validation?

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

Pickl AI

DECEMBER 10, 2024

This process often involves cleaning data, handling missing values, and scaling features. Feature extraction automatically derives meaningful features from raw data using algorithms and mathematical techniques. Cross-validation ensures these evaluations generalise across different subsets of the data.

Machine Learning 52

Machine Learning Machine Learning Algorithm Deep Learning 52

Heartbeat

MAY 29, 2023

This step involves several tasks, including data cleaning, feature selection, feature engineering, and data normalization. It is therefore important to carefully plan and execute data preparation tasks to ensure the best possible performance of the machine learning model.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78