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Eli Broad College of Business

Applying Business Analytics (Online)

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Description

Big data and business analytics have the capability to help you operate from a new vantage point. With accessibility and the competency to leverage relevant business information from both inside and outside your corporation, you can advance your position in the increasingly competitive marketplace. In this course you will explore external data and its sources that, when integrated into your business analytics, can help you increase efficiencies, develop innovative strategies, optimize processes and more.

Explore how external data and internal data spread across departmental silos and systems can be mined and modeled to solve complex business issues. In this 100% online eight-week course you’ll develop techniques for analyzing social media, geospatial, mobile, location-based, and video and imagery data. With enhanced analysis capabilities you can realize new opportunities and develop innovative solutions you can apply across various functional areas, such as customer service, human resources, marketing, IT and procurement, as well as multiple industries and fields like healthcare, financial services and retail.

What You’ll Learn

Statistics – Data Driven Science

  • Distinguish between definite and statistical statements
  • Define data, variable, and element
  • Identify quantitative and qualitative data
  • Visualize data using graphical tools

Describing the Data

  • Calculate variance and standard deviation of numerical variables
  • Find Z-scores and corresponding percentiles of various dataset entries
  • Utilize statistical package R to perform calculations

Data Distribution

  • Identify normally distributed data using a histogram
  • Calculate percentiles and Z-scores for normally distributed data
  • Utilize binomial distribution in business

Statistical Inference

  • Identify and define the target parameter of a population
  • Distinguish between point and interval estimates
  • Construct confidence intervals for mean of a population

Simple Linear Regression

  • Identify the nature of the relationship between two variables leveraging a scatter diagram
  • Define linear regression and estimate model parameters
  • Construct predictions for response variables using recession and model
  • Utilize R to obtain a linear model

Data Mining and Inferential Statistics

  • Identify critical issues associated with preparing, checking and transforming data for data mining
  • Explain the use of inferential statistics in data mining
  • Master to how apply specific inferential techniques

Decision Trees, Machine Learning and Optimization

  • Learn how to apply different machine learning approaches
  • Compare and contrast supervised and unsupervised machine learning
  • Describe the challenges with successfully deploying recommendations from machine learning projects

Analyzing Text, Networks, Location and Imagery Data

  • Identify text analytics methods, focusing on “words” and, separately, “documents”
  • Compare and contrast when and what types of problems network analytics could be used to address
  • Understand how and why spatial/temporal analyses, mobile-location based analyses, and image analyses might be conducted
  • Describe specific visual techniques available in spreadsheets to meaningfully explore data

Curriculum

Statistics – Data Driven Science

  • Statistics – Data Driven Science – Introduction
  • Data and Variables
  • Data Visualization
  • Statistical Thinking

Describing the Data

  • Describing the Data – Introduction
  • Mean – Measure of Central Tendency
  • Measures of Variability
  • Application and Mean of Variance
  • Application Using R
  • Percentile and Z–score

Data Distribution

  • Probability Distributions
  • Normal Distribution
  • Use of Normal Distribution
  • Binomial Distributions

Statistical Inference

  • Defining the Target
  • Point and Interval Estimators
  • Confidence Intervals
  • Inference on Two Populations

Simple Linear Regression

  • Probabilistic Relation
  • Linear Regression – Introduction
  • Case Study One: Building Maintenance
  • Case Study One: Building Maintenance (Continued)
  • Use of R
  • Case Study Two: Predicting Facebook Likes From Twitter Data

Data Mining and Inferential Statistics

  • Data Mining – Introduction
  • Business Understanding
  • Framing a Research Question
  • Data Understanding and Preparation
  • Checking and Transforming Data
  • Inferential Data Mining Techniques – Part One
  • Inferential Data Mining Techniques – Part Two

Decision Trees, Machine Learning and Optimization

  • Machine Learning
  • Introduction to Non–Inferential Techniques
  • Clustering Techniques
  • Decision Trees
  • Neural Networks
  • Optimization
  • Detecting Anomalies
  • Machine Learning Closure and Challenges

Analyzing Text, Networks, Location and Imagery Data

  • Text Analytics – Part One
  • Text Analytics – Part Two
  • Network Analysis
  • Spatial – Temporal Analysis
  • Mobile – Location Based Analysis – Part One
  • Mobile – Location Based Analysis – Part Two
  • Imagery Analytics
  • Redux – Data Visualization

Who should attend

This course is designed for professionals who want to enhance their analytical competencies. With its exploration into social media and other types of data external to the enterprise, the course helps managers understand how this type of information can be leveraged to develop and enhance long-term business strategies.

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