DATA SCIENCE PREDICTING the future
•Predictive analytics in data science rest on the shoulders of explanatory data analysis.
•In fact, everything is connected. Once the BI reports and dashboards
have been prepared and insights – extracted from them this information becomes the basis for predicting
future values. And the accuracy of these predictions
lies in the methods used.
have been prepared and insights – extracted from them this information becomes the basis for predicting
future values. And the accuracy of these predictions
lies in the methods used.
•Recall the distinction between traditional data and big data in data science. A similar distinction can be made regarding predictive
analytics and their methods: traditional data science methods vs. Machine Learning. One deals primarily with traditional data, and the other – with big
data.
analytics and their methods: traditional data science methods vs. Machine Learning. One deals primarily with traditional data, and the other – with big
data.
TRADITIONAL METHODS
•Traditional forecasting methods in this comprise the classical statistical methods for forecasting – linear regression analysis, logistic regression
analysis, clustering, factor analysis, and time series.
analysis, clustering, factor analysis, and time series.
•The output of each of these feeds into the more sophisticated machine learning analytics.
•Some in the data science industry refer to several of these methods as machine learning too, but in this machine learning refers to newer, smarter, better methods, such as deep
learning.
learning.
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