Data Science Research Methods: R Edition Training

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Data Science Research Methods: R Edition focuses on applying research and statistical techniques using R for data analysis. It covers data exploration, modeling, experimentation, and interpreting results to generate insights.

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What is Data Science Research Methods: R Edition Training about?

This course introduces learners to the core research methods used in data science, with hands-on practice in R. Participants will explore experimental design, sampling strategies, hypothesis testing, and causal inference, applying these methods through R packages such as dplyr, ggplot2, and stats. The course emphasizes practical implementation, reproducibility, and ethical considerations, enabling learners to carry out rigorous, evidence-based data analysis for academic and industry projects.

What are the objectives of Data Science Research Methods: R Edition Training ?

Understand and apply research design principles in data science.
Perform hypothesis testing, sampling, and statistical inference using R.
Apply causal inference techniques and validate analytical models.
Conduct reproducible research with R Markdown and best practices.
Translate research findings into actionable business or scientific insights.

Who is Data Science Research Methods: R Edition Training for?

Data Scientists and Analysts working with R.
Researchers and Academics conducting data-driven studies.
Machine Learning practitioners validating model assumptions.
Business Analysts using R for statistical analysis.
Students starting a career in data science research.

What are the prerequisites for Data Science Research Methods: R Edition Training?

Prerequisites:

Basic knowledge of R programming.
Familiarity with R packages for data manipulation (dplyr, tidyr).
Understanding of fundamental statistics and probability.
Exposure to regression or statistical modeling concepts.
Interest in applying structured research approaches in data science.

Learning Path:

Introduction to Research Methods in Data Science
Experimental Design and Sampling with R
Hypothesis Testing and Statistical Inference
Causal Inference and Model Validation in R
Reproducible Research with R Markdown and Reporting

Related Courses:

Introduction to Data Science with R
Applied Statistics with R
Machine Learning with R
Data Visualization with R

Available Training Modes

Live Online Training

3 Days

Course Outline Expand All

Expand All

Module 1- The Research Process

The Research Process

The Psychology of Providing Data

Module 2- Planning for Analysis

Planning for Analysis

Power and Sample Size Planning

Research Practices

Module 3- Research Claims

Frequency Claims

Association Claims

Causal Claims

Module 4- Measurement

Survey Design and Measurement

Reliability and Validity

Module 5- Correlational and Experimental Design

Bivariate and Multivariate Designs

Between and Within Groups Experimental Designs

Factorial Designs

Who is the instructor for this training?

The trainer for this Data Science Research Methods: R Edition Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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