Big Data Analyst with statistics

Statistical basics (approx. 6 days)

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

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

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

Basics of inductive inferential statistics (probability distributions, normal distribution, sampling distribution of the mean, significance test, null hypothesis test, significance level, effect size, parameter estimation, confidence intervals, error bar charts, power analysis, sample size)

Data preparation and data cleansing with suitable software

Descriptive analysis

Visualization of statistical results

AI-supported analysis and interpretation of statistical results

Methods for comparing two groups (approx. 5 days)

z-test, t-test for one sample

t-test for independent and related samples

Pretest-posttest designs with two groups

Supporting significance tests (Anderson-Darling test, Ryan-Joiner test, Levene test, Bonett 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, measures of association)

Interpretation of test results

AI-supported interpretation of results

Basics of regression analysis (approx. 2 days)

Linear regression

Model interpretation

AI-supported model interpretation

Correlation analysis

Methods for comparing the means of several groups (approx. 3 days)

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

Post-hoc analyses

Interpretation of group differences

Multi-factorial analysis of variance (general linear model)

Fixed, random, crossed and nested factors

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

Interaction analysis

Power analysis for variance analyses

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

Full factorial and partial factorial experimental designs

Project work (approx. 3 days)

To consolidate the content learned

Presentation of the project results

Fundamentals of Data Engineering and Data Architectures (approx. 2 days)

Tasks and Application Areas in Data Engineering

Fundamentals of Modern Data Architectures and Data Platforms

Fundamentals and Comparison of Cloud-Based Data Architectures

OLTP and OLAP

Processing Structured, Semi-Structured, and Unstructured Data

Requirements Management and Data Modeling (approx. 1 day)

Requirements for Data and Data Sources

Conceptual Data Modeling

Modeling Data Structures and Data Flows

Data Contracts, Data Governance, and Data Lineage

Databases and Data Processing (approx. 1 day)

Database System Architecture

SQL vs. NoSQL vs. NewSQL: A Comparison

Relational Data Modeling

SQL for Querying and Processing Data

Data Warehousing and Modern Data Architectures (approx. 2 days)

Fundamentals and Architecture of Data Warehouse Systems

Dimensional Data Modeling and Schema Design

Facts, Dimensions, and Historical Data Storage

Comparing Data Warehouses, Data Lakes, and Lakehouses

Introduction to dbt

Practical Implementation of Data Models

Data Integration and Data Pipelines (approx. 5 days)

Data Integration and Data Provisioning

ETL and ELT Processes

Design and Orchestration of Data Pipelines

Data Cleaning, Validation, and Data Quality

Batch and Streaming Processing

Practical Application of Airflow and Spark

Programming and Automated Data Processing (approx. 4 days)

Python Environments and Package Management

Data Processing and Transformation with Python

Automation of data processing workflows

Connecting to Different Data Sources and Interfaces

Development of data engineering workflows

Containerization with Docker

AI-powered support in the development and data processing process

Project work (approx. 5 days)

To consolidate the content learned

Presentation of the project results

Introduction to Data Analytics (approx. 1 day)

Tasks, Areas of Application, and Typical Use Cases of Data Analytics

Data Analytics Processes and CRISP-DM

The Role of the Data Analyst and How It Differs from Data Science, Data Engineering, and Business Intelligence

Classification of Artificial Intelligence and Machine Learning

Python for Data Analytics (approx. 1 day)

Working with Jupyter Notebooks

NumPy and Pandas for Data Analysis

Importing and Exporting Data from Various Sources, e.g., Excel and CSV

Selecting and transforming data

Data Preparation and Exploratory Data Analysis (approx. 3 days)

Data cleaning and transformation with Pandas

Handling missing, incorrect, and outlier data

Merging, grouping, and aggregating data

Exploratory data analysis and deriving initial insights

Fundamentals of statistical analysis

SQL and Data Management (approx. 2 days)

Relational Databases and Data Models

Data Queries and Processing with SQL

Joins, aggregations, and analytical queries

Interaction Between SQL and Python in Data Analysis

Data Quality and Data Protection

Connecting to and Using Different Data Sources and Database Systems

Data Visualization and Data Storytelling (approx. 2 days)

Visualization with Matplotlib, Seaborn, and Plotly

Selecting Appropriate Charts and Visualization Methods

Preparing and interpreting analysis results

Data Storytelling and Communication Tailored to the Audience

Power BI and Interactive Dashboards (approx. 2 days)

Data Import and Preparation with Power Query

Data modeling and relationships

Basics of DAX and metrics

Creating interactive reports and dashboards

Visualization and Communication of Business KPIs

Overview of Alternative Business Intelligence and Visualization Solutions

AI-powered data analysis (approx. 2 days)

Use of generative AI in the data analytics workflow

AI-powered data preparation, analysis, and visualization

Support with Python and SQL queries

Prompting for data analysis and interpretation of results

Critical evaluation of data sources and detection of synthetic and implausible data

Quality assurance, data protection, and responsible use of AI

Business Analytics and Statistical Fundamentals (approx. 2 days)

Key Metrics and KPIs in a Business Context

Descriptive Statistics and Statistical Relationships

Correlation, Causality, and Simple Regression Analyses

Fundamentals of confidence intervals and hypothesis testing

Interpreting Analysis Results and Deriving Data-Driven Recommendations

Project work (approx. 5 days)

To consolidate the content learned

Presentation of the project results

What is Big Data? (approx. 1 day)

Volume, Velocity, Variety, Value, Veracity

Opportunities and risks of large amounts of data

Differentiation: business intelligence, data analytics, data science

Introduction to data mining

Role of AI and data-driven systems in the big data environment

Introduction to big data frameworks (approx. 2 days)

Big data solutions in the cloud (overview of AWS, Azure, GCP)

Data access patterns

Data storage

Introduction to data lakes and data warehouses

Overview of Apache Hadoop and Spark

Distributed data processing with Spark (approx. 3 days)

Basics of distributed systems

Apache Spark (Core and SQL)

Comparison of different approaches to data processing

Processing large amounts of data

Introduction to simple ML workflows with Spark

Data pipelines and data integration (approx. 2 days)

ETL and ELT processes

Batch vs. streaming processing

Basics of data pipelines

Introduction to orchestration (e.g. Airflow overview)

Data quality and preparation

Components (approx. 2 days)

Brief presentation of various tools

Data transfer

Overview of resource management in big data systems

Hadoop ecosystem

Apache Spark deepening

Introduction to streaming technologies

NoSQL and data storage (approx. 2 days)

CAP theorem

ACID and BASE

Types of databases

HBase

Introduction to document-oriented databases

Introduction to storage formats

Overview of data lakehouse approaches

Big Data Visualization (approx. 2 days)

Theories of visualization

Diagram selection

New types of diagrams

Tools for data visualization

Introduction to BI tools (e.g. Power BI, Tableau)

Basics of data-driven decision making

Data governance and data protection (approx. 1 day)

Basics of the GDPR in the data context

Data ethics and responsible handling of data

Data quality and governance concepts

Access controls and security

Fundamentals of responsible AI use

Project work (approx. 5 days)

To consolidate the content learned

Presentation of the project results