Daniels - BIA

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Daniels - BIA by Mind Map: Daniels - BIA

1. INFO2020

1.1. Optimization

1.2. Regression

1.3. Seasonality

1.4. Time Series

2. INFO1020

2.1. Probability

2.2. Statistics

2.2.1. Regression

2.2.2. ANOVA

2.2.3. T-tests

3. INFO1010

3.1. Probability

3.1.1. Rolling Dice

3.2. Statistics

4. INFO3100

4.1. Visual Basic

4.1.1. Coding Excel Functions

4.1.2. Excel Add Ins

4.2. Reports Visualization

5. INFO3140

5.1. ERDs

5.2. Access

5.2.1. Database creation

5.2.2. Reports Visualization

5.2.3. Presentation of Information

5.3. SQL

5.3.1. Select statements

5.3.2. Where clauses

6. INFO3200

6.1. JMP

6.1.1. Regression

6.1.2. Decision Trees

6.1.3. KNN

6.1.4. Naive Bayes

6.1.5. Clustering

6.1.6. Prediction/Estimation

6.1.7. Association

6.2. Creating Model Comparisons

6.3. Tableau

6.3.1. Data Visualization

7. INFO3240

7.1. Enterprise Info Management

7.1.1. Visual Basic

7.1.1.1. Coding

7.1.1.2. Creating Functions

7.1.2. Visual Studio

7.1.2.1. Creating Forms

7.1.2.2. Creating Reports

8. INFO3340

8.1. Project Management

8.1.1. Interviews

8.1.2. Managing People

8.1.3. Managing Resources

8.1.3.1. Time

8.1.3.2. Money

8.1.3.3. Scope

8.1.4. Real World situations

8.1.4.1. Teams leading other teams

8.1.4.2. In Class enactments

9. INFO3300

9.1. Data Warehousing

9.1.1. Visual Studio

9.1.1.1. Creating Packages

9.1.1.1.1. Making the Database and Warehouse uniform

9.1.1.2. Deploying Project

9.1.1.3. Managing data in one cube

9.1.2. SQL

9.1.2.1. Creating Data Warehouse

9.1.2.2. Altering Database

9.1.2.2.1. To include informative Data

9.1.3. PowerBI

9.1.3.1. Data Visualization

9.1.3.2. Creating Reports

9.1.3.3. Creating Dashboards

10. INFO3400

10.1. SPSS Modeler

10.1.1. Descriptive Statistics

10.1.1.1. Used SPSS to run descriptives

10.1.2. Sentiment Analysis

10.1.2.1. Pos/Neg/Overall

10.1.2.2. How to determine from data

10.1.3. Regression Modeling

10.1.3.1. Regression modeling on review data

10.1.4. Analysis of Review Data

10.1.4.1. Determine sentiment of customer

10.1.4.2. Make informed business decisions

10.1.5. Text Link Analysis

10.1.6. Clustering

10.1.7. Categorization

10.1.8. Social Network Analysis

10.1.8.1. Undirected networks

10.1.8.2. Directed Networks

10.1.8.3. NodeXL

10.1.8.4. Centrality

10.1.8.4.1. Betweenness

10.1.8.4.2. Closeness

10.1.8.4.3. Eigenvector

10.1.8.4.4. Degree

11. INFO3440

11.1. Optimization Modeling

11.1.1. Linear Programming

11.1.1.1. Prediction

11.1.1.2. Preference

11.1.1.3. Product Mix

11.1.1.4. Transportation

11.1.2. Non Linear Programming

11.1.2.1. Evolutionary

11.1.2.2. GRG Non Linear

11.1.2.3. TSP

11.1.2.4. Machine Sequencing

11.1.3. Integer Programming

11.1.3.1. Fixed Cost

11.1.3.2. Binary

11.1.4. LINGO

11.1.4.1. Coding optimization into LINGO