Aim of Course:
This course will introduce you to the basic concepts in predictive analytics, also called predictive modeling, the most prevalent form of data mining.
Aim of Course:
This course covers key unsupervised learning techniques: association rules, principal components analysis, and clustering. The course will include an integration of supervised and unsupervised learning techniques.
Aim of Course:
Regression, perhaps the most widely used statistical technique, estimates relationships between independent (predictor or explanatory) variables and a dependent (response or outcome) variable.
Aim of Course:
This course continues the work of Predictive Analytics 1, and introduces you to additional techniques in predictive analytics, also called predictive modeling, the most prevalent form of data mining.
Aim of Course:
This course will teach a mix of quantitative and qualitative methods for describing, measuring and analyzing social networks.
Aim of Course:
The course introduces the use of mathematical models for managerial decision making and covers how to formulate linear programming models for decision problems where multiple decisions need to be made in the best possible way while simultaneously satisfying a number of logical conditions (or constraints). You will learn how to use spreadsheet software to implement and solve these linear programming problems.
Aim of Course:
This course will teach you how to choose an appropriate time series
forecasting method, fit the model, evaluate its performance, and
use it for forecasting.
Aim of Course:
In this course, you'll learn everything you need to get you started using Python for data analysis
Aim of Course:
This course will teach users how to implement spatial statistical analysis procedures using R software. Topics covered include point pattern analysis, identifying clusters, measures of spatial association, geographically weighted regression and surface processing.
Aim of Course:
In this course you will work through a customer analytics project from
beginning to end, using R. You will start by gaining an understanding of the
problem and the context, and continue to clean, prepare and explore the
relevant data.