Advanced Business Analytics with R

Advanced Business Analytics with R-etechguys />

Advanced Business Analytics with R

Business analytics (BA)

Overview; Business analytics (BA) is the practice of iterative, methodical exploration of an organization’s data with emphasis on statistical analysis. 
 Business analytics is used by companies committed to data-driven decision making. 

Course Duration: 50 Hrs.(3months)                       Timings:Weekends/Custom/Flexible

Mode of Training:Regular/Fast Track           
Class Duration: 4hrs weekly

  • What is business analytics?
  •     Describe business analytics
  •     Describe the evolution of analytics beginning with "scientific management" to its present form
  •     Describe the differences between analytics and analysis and explain the concept of insights
  •     Case for business analytics
  •     Describe how organizations benefit from using analytics
  • Understanding data
  •     Describe the importance of data in business analytics
  •     Describe the differences between data, information and knowledge
  •     Describe the various stages that an organization goes through in terms of data maturity
  •     Explain what an organization can do in the absence of good quality data
  • Business analytics, business intelligence and data mining
  •     Explain the differences between business analytics and business intelligence
  •     Describe the two major components within business analytics and business intelligence
  •     Understand how Data Mining as a technique helps both business intelligence and business analytics
  •     Analytical decision-making
  •     Describe the analytical decision-making process
  •     Describe the characteristics of the analytical decision-making process
  • Analysing business problems using key questions
  •     Describe how a business problem can be broken down repeatedly into key questions and then answered through analytics
  •     Describe the characteristics of a good key question    
  • Skills of a good business analyst
  •     Identify the skills of a good business analyst
  • Future of business analytics
  •     Describe the current trends that are likely to shape the future of business analytics
  • Big data analytics in the enterprise
  •     Describe the characteristics of big data
  •     Describe how hardware and software technologies are helping analytics handle extremely large volumes of data
  • Social media analytics
  •     Define social media analytics
  •     Describe the capabilities and common goals of social media analytics
  • Basic statistical concepts and types of data
  •     Define statistics and its use in business
  •     Describe the types of data
  •     Describe the basic statistical concepts
  • Sampling techniques
  •     Explain the concept of sampling and why it is necessary
  •     Describe the various techniques for sampling
  •     Describe a good sample
  • Frequency distributions and measures of central tendency
  •     Describe frequency distributions
  •     Explain the various measures of central tendency
  • Variability and shape
  •     Explain the different measures of dispersion
  •     Explain the different measures of shape
  • One-way analysis of variance
  •     Explain the concept of ANOVA
  •     Calculate ANOVA using MS Excel
  •     Test a hypothesis using ANOVA
  • Correlation
  •     Evaluate the statistical relationships between two random variables and understand the measure of correlation
  •     Identify and quantify correlation between two datasets using MS Excel
  •     Explain the concepts of correlation versus causation
  • Linear regression
  •     Explain how to model statistical relationships between two data series using linear regression
  •     Create a linear regression model to forecast values using linear regression in MS Excel
  • Linear programing
  •     Explain the concept of linearity
  •     Describe linear programing
  •     Formulate a linear programing problem
  • Linear programing – allocation models
  •     Describe allocation models in linear programing
  •     Solve allocation model problems in linear programing using MS Excel
  • Linear programing – covering models
  •     Describe covering models in linear programing
  •     Solve covering model problems in linear programing using MS Excel
  • What is R
  •     Recognize why learn R
  •     Identify the R interface - console
  • k-Means clustering
  •     Identify what is clustering?
  •     Understand the concept of k-Means clustering
  •     Cluster a dataset in MS Excel using k-Means
  •     Cluster a dataset in R using k-Means
  • Statistical modelling
  •     Define a statistical model
  •     Understand and build linear models
  •     Compute regression statistics
  •     Determine ANOVA
  • Regressions
  •     Understand the concept of multiple regression
  •     Create a multiple regression model in MS Excel
  •     Create a multiple regression model in R
  •     Understand the concept of logistic regression
  •     Create a logistic regression Model in MS Excel    

  • Course Package:0
  • Course Duration: hours
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