E-commerce, statistics and data analytics

The course provides knowledge of legal principles, store systems, online marketing strategies, logistics and customer service. It is aimed at people who want to work in digital commerce and provides a sound basis for entering the world of e-commerce. You will also be familiar with empirical relationships, be able to verify observations and classify measurement data correctly. Finally, the course teaches data analysis and data visualization. You will learn how to use Python, SQL and NoSQL databases in a targeted manner and how to apply artificial intelligence (AI) in your job. You will also learn how to use dashboards and text mining.
  • Certificates: E-Commerce" certificate
    Statistics" certificate
    Data Analytics" certificate
  • Examination: Practical project work with final presentations
  • Teaching Times: Full-time
    Monday 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.)
  • Language of Instruction: German
  • Duration: 12 Weeks

E-Commerce

Basics of e-commerce (approx. 2 days)

Introduction to e-commerce and digital commerce

Business models and framework conditions

Players and enablers in e-commerce

Online sales channels and multi-channel strategies


Legal aspects (approx. 2 days)

Legal principles in online trading

Imprint, general terms and conditions and right of withdrawal

Data protection and DSGVO

International B2C online trade


Artificial intelligence (AI) in the work process

Presentation of specific AI technologies

and possible applications in the professional environment


Online store systems and design (approx. 3 days)

Store systems and comparison of providers

Functionalities and design

Usability and user experience (UX)

Product presentations and descriptions

Checkout process optimization


Online marketing and customer acquisition (approx. 3 days)

Search engine marketing (SEA) and optimization (SEO)

Social media marketing and content strategies

Email marketing and newsletter optimization

Affiliate marketing and influencer cooperations

Display advertising and retargeting


Payment systems and finance (approx. 2 days)

Payment methods and providers in e-commerce

Risk management and fraud prevention

Debt collection and receivables management

Controlling and key figures in online retail


Logistics and fulfillment (approx. 2 days)

E-commerce logistics and shipping options

Warehouse and merchandise management systems

Returns management and optimization

International logistics and customs clearance


Customer service and CRM (approx. 2 days)

Customer service strategies in e-commerce

CRM systems and customer loyalty measures

Complaint management and conflict resolution

After-sales service and customer feedback


Web controlling and data analysis (approx. 2 days)

Web analytics tools and their use

Conversion optimization and A/B testing

Data analysis and interpretation

KPI tracking and reporting


Project work (approx. 2 days)

Conception and implementation of an e-commerce project

Presentation of the project result

Statistics

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

Data analytics

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



Changes are possible. The course content is updated regularly.

Programming skills (ideally Python) and experience with databases (SQL) are required.

After completing this course, you will have acquired a sound understanding of all important aspects of online retail. You will be familiar with the legal principles, have mastered the selection and design of store systems and be able to develop effective online marketing strategies. You will also be familiar with payment systems, logistics, customer service and data analysis. With this knowledge, you will be able to successfully set up, 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.

Furthermore, you can analyse, visualize and manage data. You also understand the use of dashboards and text mining.

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.

As companies also have to manage and structure ever-increasing volumes of data in order to evaluate and target their business processes, data analysis skills are in demand in all sectors.

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).

We will gladly advise you free of charge. 0800 3456-500 Mon. - Fri. from 8 am to 5 pm
free of charge from all German networks.
Contact
We will gladly advise you free of charge. 0800 3456-500 Mon. - Fri. from 8 am to 5 pm free of charge from all German networks.