#KeyNote #BusinessAnalytics #DataScience

Cross Industry Standard Process for Data Mining (CRISP-DM)
"A data mining process model that describes commonly used approaches that data mining experts use to tackle problems... it was the leading methodology used by industry data miners." -Wikipedia

CRISP-DM Steps

1. Business Issue Understanding
2. Data Understanding
3. Data Preparation
4. Analysis/Modeling
5. Validation
6. Presentation/Visualization

#KeyNote #DataAnanlysisMethodology #BusinessAnalytics

Type of data analysis methodology

Predictive

Predictive analytics uses existing data to predict a future outcome. For example, a company may use predictive analytics to forecast demand or whether a customer will respond to an advertising campaign.

Geospatial

This type of analysis uses location based data to help drive your conclusions. Some examples are:

Identifying customers by a geographic dimension such as zip code, state, or county, or
Calculating the distance between addresses and your stores, or
Creating a trade area based upon your customer locations for further analysis

Some types of Geospatial analysis require the use of special software - such as software that can convert an address to Latitude & Longitude, or can calculate the drive time between two geographic points on a map.

Segmentation

Segmentation is the process of grouping data together. Groups can be simple, such as customers who have purchased different items, to more complex segmentation techniques where you identify stores that are similar based upon the demographics of their customers.

Aggregation

This methodology simply means calculating a value across a group or dimension and is commonly used in data analysis. For example, you may want to aggregate sales data for a salesperson by month - adding all of the sales closed for each month. Then, you may want to aggregate across dimensions, such as sales by month per sales territory. In this scenario, you could calculate the sales per month for each salesperson, and then add the sales per month for all salespeople in each region.

Aggregation is often done in reporting to be able to “ slice and dice” information to help managers make decisions and view performance.

Descriptive

Descriptive statistics provides simple summaries of a data sample. Examples could be calculating average GPA for applicants to a school, or calculating the batting average of a professional baseball player. In our electricity supply scenario, we could use descriptive statistics to calculate the average temperature per hour, per day, or per date.

Some of the commonly used descriptive statistics are Mean, Median, Mode, Standard Deviation, and Interquartile range.

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