Predictive Modelling Techniques | Data Science With R Tutorial



This lesson will teach you Predictive analytics and Predictive Modelling Techniques.

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After completing this lesson you will be able to:
1. Understand regression analysis and types of regression models
2. Know and Build a simple linear regression model
3. Understand and develop a logical regression
4. Learn cluster analysis, types and methods to form clusters
5. Know more series and its components
6. Decompose seasonal time series
7. Understand different exponential smoothing methods
8. Know the advantages and disadvantages of exponential smoothing
9. Understand the concepts of white noise and correlogram
10. Apply different time series analysis like Box Jenkins, AR, MA, ARMA etc
11. Understand all the analysis techniques with case studies
Regression Analysis:
• Regression analysis mainly focuses on finding a relationship between a dependent variable and one or more independent variables.
• It predicts the value of a dependent variable based on one or more independent variables
• Coefficient explains the impact of changes in an independent variable on the dependent variable.
• Widely used in prediction and forecasting

Data Science with R Language Certification Training:

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The Data Science with R training course has been designed to impart an in-depth knowledge of the various data analytics techniques which can be performed using R. The course is packed with real-life projects, case studies, and includes R CloudLabs for practice.

Mastering R language: The course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R.

Mastering advanced statistical concepts: The course also includes the various statistical concepts like linear and logistic regression, cluster analysis, and forecasting. You will also learn hypothesis testing.

As a part of the course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and Internet. R CloudLab has been provided to ensure a practical and hands-on experience. Additionally, we have four more projects for further practice.

Who should take this course?
There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals:
1. IT professionals looking for a career switch into data science and analytics
2. Software developers looking for a career switch into data science and analytics
3. Professionals working in data and business analytics
4. Graduates looking to build a career in analytics and data science
5. Anyone with a genuine interest in the data science field
6. Experienced professionals who would like to harness data science in their fields

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