User experience
•When companies launch a new product, they often design surveys that measure the attitudes of customers towards that product.
•Analyzing the results after the BI team has generated their dashboards includes grouping the observations by segments (e.g., regions), and then analyzing each segment separately to extract meaningful predictive coefficients.
•The results of these operations often corroborate the conclusion that the product needs slight but significantly different adjustments in each segment in order to maximize customer satisfaction.
Forecasting
sales
sales
•Forecasting sales volume is the type of analysis where time series comes into play.
•
•Sales data has been gathered until a certain date, and the data scientist wants to know what is likely to happen
in the next
sales period, or
a year ahead. How does
it work?
in the next
sales period, or
a year ahead. How does
it work?
•
•They apply mathematical and statistical models and run multiple simulations; these simulations
provide the analyst with future scenarios.
provide the analyst with future scenarios.
•
•This is at the core of data science, because, based on these scenarios, the company can make better predictions and implement adequate strategies.
WHO USES TRADITIONAL
FORECASTING
FORECASTING
•The data scientist. But bear in mind that this title also applies to the person who employs machine learning techniques for analytics too. A lot of the work spills from one methodology to the other.
•The data analyst, on the other hand, is the person who prepares advanced types of analyses that explain the patterns in the data that have already emerged and overlooks the basic part of the predictive
analytics.
analytics.
Class Sessions
1- Introduction to Data Analytics
2- What is Data Science
3- Overview
4- Data preprocessing
5- Data Quality Assessment and Use Case
6- Where does Data come from?
7- Who handles th Data
8- BUSINESS INTELLIGENCE
9- Traditional Methods
10- Traditional Forecasting uses
11- Content
12- Machine Learning Uses
13- Introduction to Large Language Model and Generative AI
14- Data protection
15- Data Management practises
16- Tools and Frameworks