Data 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

Basics of database systems with Access (approx. 3 days)

Redundant data

Data integrity

Normalization

BCNF

DB design

Relationship 1:n, m:n

data types

tables

Primary and foreign keys

Referential integrity

Relationships between relations

Entity relationship model

Index, default value

Restrictions (check)

Queries

Forms, reports

Circular reference

Introduction to SQL Server Management Studio (SSMS) (approx. 2 days)

Overview of

Physical DB design

Creating tables

Data types in MS SQL

Primary Key

Restrictions, default values, diagram, relationships

Backup and restore

Artificial intelligence (AI) in the work process

Presentation of specific AI technologies

and possible applications in the professional environment

Introduction to DDL (approx. 8 days)

SQL basics

syntax

Commands

Multiple tables

Operators

Flow control

Scalar value functions

Table value functions

System functions

Procedures with and without parameters

Error types

Transactions, locks, DeadLock

DCL - Data Control Language (approx. 1 day)

Logins

User learning

roles

Authorizations

Data types, data import and export (approx. 1 day)

Data type geography

Data export, data import

Project work (approx. 5 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