Business Analytics With R

Objective
“Business Analytics using R from 1.21GWS” is a 60 hours instructor led practical course designed and delivered by 1.21GWS to equip professionals from various backgrounds such as statistics, marketing, finance, economics, IT, analytics, marketing research, etc. with the fundamentals of analytics. The course aims to enhance the skills of participants in understanding, interpretation and analysis of data using statistical, analytical and probabilistic techniques. The course covers some of the common machine learning techniques. Scenarios and hands-on analysis of data from various industries including Marketing, Finance, Human Resources, Sales and Software Services are part of this course. The course “Business Analytics using R” covers techniques for performing process performance models, baselines, simulation, variability analysis, statistical analysis required by models such as CMMI®, PCMM®, Six Sigma, etc.
Who should attend:
Professionals and students from various backgrounds such as statistics, marketing, finance, economics, IT, analytics, marketing research, etc. looking to make a career in analytics and data sciences.
REGISTER
GROUP OF 3 OR MORE [USD 800 PER PARTICIPANT]
INDIVIDUAL STANDARD PRICE [USD 1000 PER PARTICIPANT]
Course Features
- Lectures 86
- Quizzes 0
- Duration 50 hours
- Skill level All levels
- Language English
- Students 77
- Certificate No
- Assessments Yes
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Introduction – Analytics and R / RStudio
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Basic RStudio commands
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R Data Types
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R Variables
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R Data Types
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R Operators
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R Decision Making
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R Loops
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R Functions
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R Data Import and Export
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Data Manipulation in R
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Data Visualization with R
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Introduction to Statistics and Probability
- Introduction to Statistics
- Descriptive Statistics
- Measure of Central Tendency
- Measure of Dispersion
- Measure of Shapes
- Probability
- Sampling methods
- Probability Sampling
- Simple Random Sampling
- Semantic Sampling
- Stratified Sampling
- Cluster Sampling
- Non- Probability Sampling
- Convinced Sampling
- Quota Sampling
- Judgement Sampling
- Snow Ball Sampling
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Introduction to Hypothetical Testing
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Introduction to Machine Learning
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Introduction to Supervised & Unsupervised Learning
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Introduction to Time Series Analysis