Data Analytics Training in Duluth
Enroll in or hire us to teach our Data Analytics class in Duluth, Minnesota 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.
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We offer private customized training for groups of 3 or more attendees.
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Course Description |
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| 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) |
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Prerequisites |
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| Basic math and some Excel skills. | |
Course Outline |
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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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