HARTMANN software group

Data Analytics Training in New Haven

Enroll in or hire us to teach our Data Analytics class in New Haven, Connecticut by calling us @303.377.6176. Like all HSG classes, Data Analytics may be offered either onsite or via instructor led virtual training. Consider looking at our public training schedule to see if it is scheduled: Public Training Classes
Provided there are enough attendees, Data Analytics may be taught at one of our local training facilities.
We offer private customized training for groups of 3 or more attendees.
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Course Description

  
Data Analytics is the practice of using data for gaining insights, for forecasting and for decision-making. This training workshop will introduce techniques and tools that knowledge workers can use for analyzing data using popular software tools. At the end of the workshop, the participant will be able to (a) manage and prepare data for analysis, (ii) summarize data across multiple dimensions, (iii) interpret distributions of the data, (iv) identify and interpret relationships among data elements, (v) compare data summaries and (vi) forecast based on historical data.
Course Length: 3 Days
Course Tuition: $1200 (US)

Prerequisites
Basic math and some Excel skills.

Course Outline

  
Session 1: Introduction to data
Data can be categorical or numeric.
Scales of measurement
Transforming data
 
Session 2: Summarizing the data
Pivot table, pivot chart, and dashboards
Distributions, box and whisker plot, and interpretations
 
Session 3: Discovering relationships among data
Scatter-plots and interpretations of relationships
Create linear relationships between variables
Predictions and determine confidence intervals.
 
Session 4: Comparing sets of data for similarity.
Use of t-tests and ANOVA
Interpretation of results
 
Session 5: Time-series data
Analysis of time-series to forecast future values
Comparing forecasting models.
 
Session 6: Data mining
Identifying data mining projects
Data mining techniques
Interpretation of results
 
Each session will include (a) presentation, summary of literature with references for further
information, (ii) hands-on lab exercises with real data sets and (iii) quizzes to assess
comprehension.
 
Tools used: Excel and Rapidminer

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