Code&Data Insights

[ Data Intergrity ] Data Intergrity - Data integrity is the accuracy, completeness, consistency, and trustworthiness of data throughout its lifecycle. - Alignment to business objective + newly discovered variables + constraints = accurate conclusion [ Types of insufficient data ] - Data from onlly one source - Data that keeps updating - Outdated data - Geographically-limited data [ Minimum sampl..

[ Data Collection ] How data is collected? - Interviews - Observations - Forms - Questionnaires - Surveys - Cookies (personal interests, habits) [ Data Formats ] Discrete data - data that is counted and has a limited number of values (ex) room maximum capacity Continuous data - data that is measured and can have most any numeric value (ex) temperature Nominal data - a type of qualitative data th..

[ Common Problem Types ] 1. Making predictions 2. Categorizing things - categorized by specific keyword or score 3. Spotting something unusal 4. Identifying themes - Grouping categorized info into broader concepts 5. Discovering connections 6. Finding patterns - using historical data to understand what happened in the past and is therefore likely to happen again [ SMART questions ] => kinds of q..

[ The six phases of data analysis ] Ask : Business Challenge/Objective/Question Prepare : Data generation, collection, storage, and data management Process : Data cleaning/data integrity - what type of data we have, missing data, wrong data collection? Analyze : Data exploration, visualization, and analysis => should be unbiased! look for the patterns Share : Communicating and interpreting resul..

[ Data & Data Science Field ]  Data - traditional / bigData science - BI(Business Intelligence) / Traditional Methods / Machine Learning * Data Collection in TRADITIONAL DATA after gathering the data, its need to take the data pre-processing raw data -> data pre-processing -> processing -> Information Where?: Basic customer data / historical stock price data - Class labeling : / - Data cleansin..