Working with Data

Data Analytics Foundations - A Manager's Overview

Course Number: JC
Recommended Duration: 1 day, virtual or onsite

The Data Analytics Foundations course will prepare executives to follow a 5-step process that guides data analysis and then communicating results to have the biggest possible impact.  It establishes a foundation for data-intensive analytic projects that deliver insight, clarity, confidence and sound decision support.  As part of the 5-step process, participants will be introduced to the current state of data analytics, data visualization tools and the concepts of ‘big data’.

Objectives

• Frame a business question as an analytics question.

• Create an analysis plan that limits the scope to the core question.

• Collect data that is required – and appropriate – for answering the question.

• Apply appropriate analytic techniques and tools to analyze data, create statistical models, and identify insights that can lead to actionable results.

• Make recommendations, based on the analysis, to address the business.

• Select appropriate data visualizations to clearly communicate analytic insights to business sponsors and analytic audiences.

Prerequisites: The structure of the course assumes no prior experience in statistics or data analytics

Topics

Overview

• Big Data

• Data Analytics

• Data Visualization

• Tools and Techniques

Big Data

• Volume, Velocity & Variety

• Challenges & Opportunities

Data Analysis

• Planning a Data Analysis Project

• Establish an analytic objective from the business question.

• Choose an appropriate analytic approach – exploratory, explanatory, confirming, comparative.

• Data Collection

• Identify and evaluate data sources.

• Determine how much historical data is required.

• Establish a data collection methodology.

• Conduct the Analysis

• Multiple data sets, Missing data, Data Quality

• Become familiar with the data - Descriptive statistics

• Find trends and patterns

Data Visualization

• Visual Perception

• Information Processing

• Categories of Visualizations

Making Recommendations

• Use data visualizations to communicate findings

• Develop recommendations supported by data

Share with others

Data Mining 101 - A Practitioner's Course

Course Number: JC
Recommended Duration: 2 days

Intended Audience: 

BI and Analytics Managers, Business & Data Analyst (IT and non-IT), Data Analyst, Database Administrators, Project Leaders, Systems Analyst

Course Overview

Data mining is the process of discovering interesting knowledge from large amounts of data. It is an interdisciplinary field with contributions from many areas, such as statistics, machine learning, information retrieval, pattern recognition and bioinformatics. Data mining is widely used in many domains, such as retail, finance, telecommunication and social media.

This course provides an overview of various data mining techniques with examples of how they are used in various organizations such as retail, finance, biotechnology and social media.  Case studies are used to allow participants to work through several data mining issues using the techniques described and to recognize opportunities to apply data mining within their organization.

Note: This course uses a visually oriented, open source software package to process the data.  The class is not intended to be a programming class.  Instead, the software is used to examine the impact of different data mining decisions.

Objectives

·      Identify the data mining options available to solve the business question.

·      Plan for common data challenges.

·      Apply data mining techniques relevant to the business question.

Prerequisites

This course will be accessible to students without prior training in quantitative research methods. However, students with a background in basic descriptive and inferential statistics will, most likely, get more out of the course. 

Topics

Introduction to Data Mining

• Descriptive and Predictive
• Models and Algorithms
• Regression vs. Classification
• Supervised/Unsupervised Learning

Data Preparation

• Integrating Data Sets
• Data Reduction
• Inconsistencies, Missing Data & Outliers

Data Mining Methods

• Clustering
• Association Rules
• Classification
• Decision Trees
• Regression
• Neural Networks
• Text Mining

Share with others

Data Quality Improvement Techniques

Course Number: JC
Recommended Duration: 2 days

Intended Audience: Managers, designers, developers, and other people needing an introduction to Web technology and trends.

Overview

This course takes participants through several key data quality processes.

Objectives

• Create a business case for improving data quality, cost benefit analysis, impact and root causes
• Master data management techniques for effective data distribution throughout an organization
• Identify ways to secure data and manage performance
• Align business intelligence initiatives with company’s strategic goals
  
  

Prerequisites: The structure of the course assumes no prior experience in statistics or data analytics

Topics

Overview

• Data Quality – The Big Picture
• Data Quality Management
• Profiling, Assessment, Cleansing, Audit Trails
• Risk Analysis & Interventions
• Organizational Issues
• Governance & Stewardship
• Master Data Management

Data Quality Projects

Data Profiling

• Purpose
• Determining Data to Profile
• Techniques (column, subject, attribute dependency, etc)
• Useful General Technique

Data Assessment & Cleaning

• Defining Rules, Constraints and Relationships
• Historical vs. In-process data
• Creating a Plan

Continuous Improvement

• Lean Six Sigma Approach
• Statistical Process Control

Share with others