8. Synopsis
**Synopsis :**

**Modul_1.: Visual analytics**

Introduction to Predictive Analytics and Visualization, visual analytics

Analytical reasoning techniques,

Data representations and transformations

Visual representations and interaction techniques.

Generalized multidimensional scaling

Perceptual mapping

Business Decision Mapping (BDM)

*Practice in Laboratory 1*:* Visualization*

**Modul_2: Forecasting**

a.)
Approaching a forecasting problem

Components of a time series; Judging the quality of data; Understanding data;
Looking at residuals; How to start making a forecast; Forecasting models.

Defining parameters, Analysis of data sources; Choosing alternative
projection techniques Preliminary
selection criteria

b.)
Forecasting with exponential smoothing models

Smoothing with
moving averages; Single exponential smoothing; Compare exponential smoothing
with moving averages; Exponential smoothing for trending data

*Practice in
laboratory 2. Exponential smoothing;
Software programs and visualization.*

c.)
Trend and seasonality modeling and analysis;

ANOVA model;
Contribution of trend/seasonal effects;
Analysis of residuals.

*Practice in
laboratory 3: Trend and seasonality; Software programs and visualization*

d.)
Preparing the data for modeling;

Achieving
linearity; Achieving normality; Dealing with outliers

*Practice in laboratory 4: Outliers; Software
programs and visualization.*

e.)
Regression modeling and analysis

Building
regression models: The regression curve; A simple linear model; The method of least-squares; Normal
regression assumptions; Comparing estimation techniques; Interpreting regression output: The R-squared
statistic; The t-statistic; The F-Statistic; The D-W Statistic; Assessing
forecast precision, Looking at regression residuals.

*Practice in
laboratory 5: Regression example; Software programs and visualization.*

f.)
Insuring against unusual values

The need for
robustness in correlation and regression analysis Seasonal adjustment;
Ratio-to-moving-average-method. Seasonal adjustment with resistant smoothers

*Practice in
laboratory 6: with seasonality analysis;
Software programs and visualization*

**Modul_3: Foresight**

a)
Differences of foresight and forecasting

Non measurable trend analysis: qualitative description, success factors
Topic definition, starting position Ongoing projects, expected development
Visualization of trends through drawing, pictures (like cicles, hype)

b)
Visioning a usage area

Topic
definition, summary of the situation Driver analysis estimation of effects,
uncertainty Scenario making, alternative scenarios, illustrations Visualization
and illustration of the visions

c)
Technology radar for foresight Flow of news, scanning news, practice for
selections Professional blogging, technology radar Virtual community to build up Games for
knowledge integration

d)
Strategy making based on backward scenarios

Choosing objectives, freedom of choices, views Influencing drivers, costs
and risks Strategy forming through
backward scenario analysis

*Practice in
laboratory 7: Foresight presentations in of the students on a preliminary given
topic*

**Summary:
Usability of predictive analysis, foresight and
visualization.**