Overview of Analytics Course in Hamburg

Hamburg, a bustling port city in northern Germany, is known for its vibrant cultural scene, rich history, and stunning waterfront. However, in recent years, Hamburg has also emerged as an important hub for business and technology, attracting a growing number of students and professionals from around the world. One area where Hamburg is particularly strong is analytics, thanks in part to its advanced technological infrastructure and impressive array of academic and professional institutions. For those interested in pursuing a career in this exciting field, Hamburg offers a wide range of analytics courses to choose from. One of the most popular options is the Master of Science in Business Analytics program at the Hamburg University of Technology (TUHH), a highly respected institution that consistently ranks among the top universities in Germany for engineering and technology. This program is aimed at students who want to gain a deep understanding of statistical analysis, data mining, machine learning, and other key topics in the field of analytics. The program is taught entirely in English and includes a wide range of coursework as well as practical projects and case studies. Another strong option for studying analytics in Hamburg is the Executive MBA in Business & Data Analytics program offered by the University of Hamburg's Business School (BWL). This program is designed specifically for working professionals who want to enhance their skills and knowledge in the areas of data analytics, business strategy, and project management. Students in this program attend seminars and workshops with highly experienced practitioners, and also have the opportunity to work on real-world projects to gain hands-on experience. For those interested in a shorter-term program, the Hamburg School of Business Administration (HSBA) offers a variety of certificates and workshops focused on analytics and related topics. These courses are designed to be highly practical and hands-on, allowing students to develop real-world skills they can apply immediately in their professional lives. In addition, the HSBA has strong connections with a variety of local businesses and organizations, giving students the opportunity to network and find job opportunities. Finally, the Hamburg Institute of International Economics (HWWI) offers a number of courses and workshops focused on applied research in the areas of economics, business, and social science. Although these programs are not specifically focused on analytics, they offer valuable training in research methods and analysis that can be applied to a wide range of fields, including analytics. Overall, Hamburg is a fantastic place to study analytics, thanks to its strong academic and professional institutions, thriving business ecosystem, and vibrant cultural scene. Whether you're interested in a full-time master's program, a part-time executive MBA, or a shorter-term certificate program, Hamburg has something to offer for anyone interested in this exciting and rapidly growing field.

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Course Duration

2

Internship

Yes

Live Training

Yes

Career Assistance

Yes
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Skillfloor Course Training Process
Skillfloor Course Training Process

Internship Certificate

internship certificate

Completing this Analytics Course certifies your proficiency in data analysis, statistical methods, and essential analytics tools. This certification shows you can transform raw data into actionable insights, improving decision-making in various business contexts. It indicates your understanding of data visualization, predictive modelling, and data-driven strategy development, providing you with the skills to excel in analytics. This credential reflects your dedication to continuous learning and commitment to leveraging data for strategic advantage.

Analytics Tools Covered

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Why Choose SKILLFLOOR for Analytics in Hamburg?

Why Course Training in Skillfloor

Syllabus

- Overview of data analysis and its importance in business

- Types of analytics: Descriptive, Predictive, Prescriptive

- Role of data in decision-making processes

- Introduction to common tools: Tableau, PowerBI, Excel

- Ethical considerations in data collection and analysis

- Data sources: Primary and secondary data

- Data collection methods (surveys, web scraping, databases)

- Data cleaning techniques (handling missing values, outliers)

- Data transformation and feature engineering

- Data storage concepts (structured vs. unstructured data)

- Descriptive statistics: Mean, median, mode

- Data visualization basics (histograms, scatter plots)

- Identifying data patterns and trends

- Outlier detection and handling methods

- Correlation and causation analysis

- Inferential statistics and probability theory

- Hypothesis testing (t-tests, chi-square tests, ANOVA)

- Measures of central tendency and variability

- Confidence intervals and margin of error

- Regression analysis: Linear and logistic regression

- Principles of effective data visualization

- Types of charts and their uses (bar, line, pie, heatmaps)

- Designing dashboards for different audiences

- Interactive visualization techniques

- Data storytelling for impactful presentations

- Time series analysis and forecasting methods

- Clustering and segmentation analysis

- Decision trees and classification techniques

- Introduction to machine learning in business analytics

- Model evaluation and selection

- Basics of SQL for data manipulation

- Creating databases and relationships

- Aggregating data with SQL (GROUP BY, JOIN)

- Data modeling for business intelligence (star and snowflake schemas)

- Case study: Building a business model with SQL

- Connecting and preparing data in Tableau

- Creating basic visualizations (charts, maps)

- Advanced Tableau functions (LOD calculations, table calculations)

- Building interactive dashboards and stories

- Publishing and sharing visualizations on Tableau Server/Online

- Introduction to PowerBI workspace and components

- Data import and transformation with Power Query

- Data modeling and relationships in PowerBI

- Creating and customizing visualizations

- Publishing and collaborating on PowerBI Service

- Selecting a real-world dataset for analysis

- Defining business questions and objectives

- Conducting data analysis and visualization

- Presenting findings in a comprehensive dashboard

- Peer review and feedback on project

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