Business Intelligence Analyst

Statistical basics (approx. 6 days)

Measurement theory basics (population and sample, sample types, measurement and scale levels)

Univariate descriptive statistics (frequency distributions, central measures, measures of dispersion, standard value, histograms, bar charts, pie charts, line charts and box plots)

Bivariate descriptive statistics (measures of correlation, correlation coefficients, crosstabs, scatter plots and grouped bar charts)

Basics of inductive inferential statistics (probability distribution, normal distribution, mean value distribution, significance test, Fisher's null hypothesis test, effect size, parameter estimation, confidence intervals, error bar charts, power analyses and determining the optimum sample size)

Artificial intelligence (AI) in the work process

Presentation of specific AI technologies

and possible applications in the professional environment

Methods for comparing two groups (approx. 5 days)

z- and t-test for a sample (deviation from a specified value)

t-test for the mean difference between two independent/connected samples

Testing the effectiveness of actions, measures, interventions and other changes with t-tests (pretest-posttest designs with two groups)

Supporting significance tests (Anderson-Darling test, Ryan-Joiner test, Levene test, Bonnet test, significance test for correlations)

Nonparametric methods (Wilcoxon test, sign test, Mann-Whitney test)

Contingency analyses (binomial test, Fisher's exact test, chi-square test, cross-tabulations with measures of association)

Methods for comparing the means of several groups (approx. 5 days)

One- and two-factorial analysis of variance (simple and balanced ANOVA)

Multi-factorial analysis of variance (general linear model)

Fixed, random, crossed and nested factors

Multiple comparison methods (Tukey-HSD, Dunnett, Hsu-MCB, Games-Howell)

Interaction analysis (analysis of interaction effects)

Selectivity and power analysis for variance analyses

Introduction to Design of Experiments (DoE) (approx. 1 day)

Full and partial factorial experimental designs

Project work (approx. 3 days)

To consolidate the content learned

Presentation of the project results

MATLAB basics (approx. 2 days)

MATLAB user interface

Reading data from a file

Variables, arrays, operators, basic functions

Graphical representation of data

Customizing diagrams

Exporting graphics

Variables and commands (approx. 2 days)

Relational and logical operators

Sets, sets with 2D solids (polyshape)

Performing mathematical and statistical calculations with vectors

Graphics in statistics

Analysis and visualization (approx. 1 day)

Creating and modifying matrices

Mathematical operations with matrices

Graphical representation of matrix data

Matrix applications: Mappings, rotation, systems of linear equations, least square method

Artificial intelligence (AI) in the work process

Presentation of specific AI technologies

and possible applications in the professional environment

Data processing (approx. 1 day)

Data types: Structure arrays, cell arrays, string vs. char, categorical, datetime and much more.

Creating and organizing tabular data

Conditional data selection

Importing/exporting with Matlab: folder structures, .mat data, table data, continuous texts

MATLAB programming (approx. 3 days)

Control structures: loops, if-else, exceptions

Functions

Object-oriented programming

App design

Simulation in MATLAB (approx. 5 days)

Numerical integration and differentiation

Basics of simulation of ordinary differential equations, Matlab ODE and solver options

Simulation technology in Matlab: input parameters, data interpolation, simulation studies

Simulation control: event functions (zero crossing), output functions

Application examples, e.g. simulation of an electric motor, simulation of a rocket

Simulink (approx. 4 days)

Basics of Simulink: Diagrams, functions, signals and differential equations

Functions, subsystems and libraries

Import/export, lookup tables, control

Zero-crossing, automation of simulation tasks (Matlab access)

Application examples, e.g. simulation of an aircraft drive train

Project work (approx. 2 days)

To consolidate the content learned

Presentation of the project results

Python basics (approx. 1 day)

History, concepts

Usage and areas of application

syntax

First steps with Python (approx. 5 days)

Numbers

Strings

Date and time

Standard input and output

list, tuple dict, set

Branches and loops (if, for, while)

Artificial intelligence (AI) in the work process

Presentation of specific AI technologies

and possible applications in the professional environment

Functions (approx. 5 days)

Define your own functions

Variables

Parameters, recursion

Functional programming

Troubleshooting (approx. 0.5 days)

try, except

Intercept program interruptions

Object-oriented programming (approx. 4.5 days)

Python classes

Methods

Immutable objects

Data class

Inheritance

Graphical user interface (approx. 1 day)

Buttons and text fields

Grid layout

File selection

Project work (approx. 3 days)

To consolidate the content learned

Presentation of the project results

Basics of Business Intelligence (approx. 2 days)

Fields of application, dimensions of a BI architecture

Basics of business intelligence, OLAP, OLTP, tasks of data engineers

Data Warehousing (DWH): handling and processing of structured, semi-structured and unstructured data

Requirements management (approx. 2 days)

Tasks, objectives and procedures in requirements analysis

Data modeling, introduction/modeling with ERM

Introduction/modeling in UML

- Class diagrams

- Use case analysis

- Activity diagrams

Artificial intelligence (AI) in the work process

Presentation of specific AI technologies

and possible applications in the professional environment

Databases (approx. 3 days)

Basics of database systems

Architecture of database management systems

Application of RDBMS

Implementation of data model in RDBMS, normal forms

Practical and theoretical introduction to SQL

Limits of relational databases, csv, json

Data Warehouse (approx. 4 days)

Star Schema

Data modeling

Creation of Star Schema in RDBMS

Snowflake Schema, basics, data modeling

Creation of Snowflake Schema in RDBMS

Galaxy Schema: Basics, data modeling

Slowly Changing Dimension Tables Type 1 to 5 - Restating, Stacking, Reorganizing, mini Dimension and Type 5

Introduction to normal, causal, mini and monster, heterogeneous and sub dimensions

Comparison of state and transaction oriented

Fact tables, density and storage from DWH

ETL (approx. 4 days)

Data Cleansing

- Null Values

- Preparation of data

- Harmonization of data

- Application of regular expressions

Data Understanding

- Data validation

- Statistical data analysis

Data protection, data security

Practical structure of ETL routes

Data Vault 2.0, basics, hubs, links, satellites, hash key, hash diff.

Data Vault data modeling

Practical structure of a Data Vault model - Raw Vault, practical implementation of hash procedures

Project work (approx. 5 days)

To consolidate the content learned

Presentation of the project results

Introduction to data analysis (approx. 1 day)

CRISP-DM reference model

Data analytics workflows

Definition of artificial intelligence, machine learning, deep learning

Requirements and role in the company of data engineers, data scientists and data analysts

Review of Python basics (approx. 1 day)

data types

Functions

Data analysis (approx. 3 days)

Central Python modules in the context of data analytics (NumPy, Pandas)

Process of data preparation

Data mining algorithms in Python

Artificial intelligence (AI) in the work process

Presentation of specific AI technologies

and possible applications in the professional environment

Data visualization (approx. 3 days)

Explorative data analysis

insights

Data quality

Benefit analysis

Visualization with Python: Matplotlib, Seaborn, Plotly Express

Data storytelling

Data management (approx. 2 days)

Big data architectures

Relational databases with SQL

Comparison of SQL and NoSQL databases

Business Intelligence

Data protection in the context of data analysis

Data analysis in a big data context (approx. 1 day)

MapReduce approach

Spark

NoSQL

Dashboards (approx. 3 days)

Library: Dash

Structure of dashboards - Dash components

Customizing dashboards

Callbacks

Text Mining (approx. 1 day)

Data preprocessing

Visualization

Library: SpaCy

Project work (approx. 5 days)

To consolidate the content learned

Presentation of the project results