This course provides a thorough understanding of the key aspects of e-commerce, including legal fundamentals, e-commerce platforms, online marketing strategies, payment and logistics processes, and customer service. Furthermore, the course provides relevant knowledge of statistics for verifying observations and correctly interpreting measurement data. Finally, the course covers data analysis and data visualization. You will learn how to use Python, SQL, and NoSQL databases effectively, as well as how to work with dashboards and text mining.
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Certificates: E-Commerce" certificate
Certificate "Statistics and data analysis"
Data Analytics" certificate -
Examination: Praxisbezogene Projektarbeiten mit Abschlusspräsentationen
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Teaching Times: Full-timeMonday to Friday from 8:30 a.m. to 3:35 p.m. (in weeks with public holidays from 8:30 a.m. to 5:10 p.m.)
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Language of Instruction: German
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Duration: 12 Weeks
E-Commerce
Fundamentals of E-Commerce (approx. 1.5 days)
Introduction to E-Commerce and Digital Commerce
Business Models and Framework Conditions
Key Players and Enablers in E-Commerce
Online Sales Channels: Multi-Channel Strategies, Marketplaces, and an Outlook on Agentic Commerce
Legal Aspects (approx. 2.5 days)
Legal Foundations of Online Commerce: Legal Notice, Terms and Conditions, and Right of Withdrawal
Data Protection and the GDPR
International B2C Online Commerce
EU Product Safety Regulation: General Product Safety Regulation (GPSR)
Platform and AI Regulation: DSA/DMA and the EU AI Act (Obligations, Labeling, High-Risk Classification)
Accessibility (BFSG) and Product Liability
Online Store Systems and Design (approx. 3 days)
Comparison of E-commerce Platforms, Headless and Composable Commerce
Usability, User Experience (UX), and Accessible Web Design
AI-powered creation of product presentations and descriptions
Checkout Process Optimization
Online Marketing and Customer Acquisition (approx. 3 days)
SEO/SEA and Content Strategies
Social Media Marketing and Social Commerce
Email, Affiliate, and Influencer Marketing
Marketplace Marketing and Retail Media
AI-powered personalization, recommendation systems, and visibility in AI search systems (GEO/AEO)
Payment Systems and Finance (approx. 2 days)
Payment Methods and Providers, Instant Payments, and Digital Wallets
Buy Now, Pay Later (BNPL)
Risk Management, Fraud Prevention, and Accounts Receivable Management
Controlling and Key Performance Indicators in E-commerce
Logistics and Fulfillment (approx. 2 days)
E-commerce Logistics and Shipping Options
Warehouse and Inventory Management Systems
Returns Management and Optimization
International Logistics and Customs Clearance
Sustainable Logistics and Digital Product Passport (DPP)
Customer Service and CRM (approx. 2 days)
Customer Service Strategies in E-Commerce
CRM Systems and Customer Loyalty Initiatives
AI-powered customer service: chatbots, voicebots, omnichannel support
Complaint Management and Conflict Resolution
After-Sales Service and Customer Feedback
Web Analytics and Data Analysis (approx. 2 days)
Web Analytics Tools and Their Use
Conversion Optimization and A/B Testing
Data Analysis, KPI Tracking, and Reporting
Cookie-less tracking and consent management
Project Work (approx. 2 days)
To reinforce what has been learned
Presentation of project results
Statistics and data analysis
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
Data analytics
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
Changes are possible, the course content is updated regularly.
Upon completing this course, you will have a solid understanding of all key aspects of e-commerce. You will be familiar with the legal fundamentals, know how to select and configure e-commerce platforms, and be able to develop effective online marketing strategies. In addition, you’ll be well-versed in payment systems, logistics, customer service, and data analysis, and you’ll be able to use AI tools effectively and in compliance with the law throughout the entire e-commerce operation. With this knowledge, you’ll be able to successfully build, operate, and continuously optimize an online store.
You will also understand the basics of statistics, be able to process and evaluate data and present, explain and interpret statistical data analyses and results using graphics.
In addition, you can process and analyze data using Python and SQL, create meaningful visualizations and interactive dashboards with Power BI, and interpret and communicate analysis results in a way that is tailored to your audience. You can use generative AI specifically to support data analysis.
This course is aimed at people who are responsible for the conception, design and practical implementation of websites with e-commerce functions and who wish to acquire the necessary knowledge and skills.
After completing the course, you will have acquired a broad basic knowledge that will enable you to enter various fields of activity in online retail. The course is aimed at people who want to actively participate in the conception, design and implementation of e-commerce projects.
In addition, data-driven decisions are playing an increasingly important role in companies and organizations. Accordingly, there is a need for professionals who can process, analyze, and clearly visualize data from various sources. Knowledge of Python, SQL, and business intelligence opens up career opportunities in the field of data analytics as well as in data-driven functional and business areas.
Your meaningful certificate provides a detailed insight into the qualifications you have acquired and improves your career prospects.
Didactic concept
Your lecturers are highly qualified both professionally and didactically and will teach you from the first to the last day (no self-study system).
You will learn in effective small groups. The courses usually consist of 6 to 25 participants. The general lessons are supplemented by numerous practical exercises in all course modules. The practice phase is an important part of the course, as it is during this time that you process what you have just learned and gain confidence and routine in its application. The final section of the course involves a project, a case study or a final exam.
Virtual classroom alfaview®
Lessons take place using modern alfaview® video technology - either from the comfort of your own home or at our premises at Bildungszentrum. The entire course can see each other face-to-face via alfaview®, communicate with each other in lip-sync voice quality and work on joint projects. Of course, you can also see and talk to your connected trainers live at any time and you will be taught by your lecturers in real time for the entire duration of the course. The lessons are not e-learning, but real live face-to-face lessons via video technology.
The courses at alfatraining are funded by Agentur für Arbeit and are certified in accordance with the AZAV approval regulation. When submitting a Bildungsgutscheinor Aktivierungs- und Vermittlungsgutschein, the entire course costs are usually covered by your funding body.
Funding is also possible via Europäischen Sozialfonds (ESF), Deutsche Rentenversicherung (DRV) or regional funding programs. As a regular soldier, you have the option of attending further training courses via Berufsförderungsdienst (BFD). Companies can also have their employees qualified via funding from Agentur für Arbeit (Qualifizierungschancengesetz).