Introduction to Data Analysis, Germany

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Organizations need to make business decisions more quickly and accurately than ever before. Basing these decisions on data and best practice analysis techniques and less on gut feel or "the way we have always done things" is how today's corporate management is demanding information. A solid foundation of data analysis for business decision making is a critical skill you should have regardless of whether your motive is to obtain or sustain a competitive advantage or simply better steward your resources to serve customers. In this course, you will learn to use data analytics to create actionable recommendations, as well as identify and manage opportunities where data-based decisions can be used to change the way you do business.

This course provides many of the common data analysis tools used to gather, analyze, and adapt your data to feed business decisions. You do not need heavy Excel or data analysis experience. This course includes introductory exercises on Excel add-ins, standard deviation, random sampling, and an introduction to pivot tables and charts. These exercises will show you how to effectively demonstrate basic data analysis functions and reporting in Excel or Google Spreadsheets. We will simplify math jargon and complex symbols and equations to concentrate on what your data can tell you and your organization. In addition, you will learn how to present to those executives, managers and subject matter experts who need to quickly make decisions that drive your organization.

There are no prerequisites for this course.

Anyone involved in operations, project management, business analysis, or management, who needs an introduction to data analysis.

Terms, jargon, and impact of business intelligence and data analytics
Scope and application of data analysis
Impact of analytics on gaining competitive advantage and decision support
Measure the performance of and improvement opportunities for business processes
Need for tracking and identifying the root causes of deviation or failure
Basic principles, properties, and application of probability theory and the normal distribution
Introduction to different methods for summarizing information and presenting results including charts
Statistical inference and drawing conclusions about the population
Sample sizes and confidence intervals, and how they influence the accuracy of your analysis
Forecasting and an introduction to simple linear regression analysis
Interpret your results and draw sound and relevant conclusions on business
Methods and algorithms for forecasting future results and to reduce current and future risk
Process improvement and analysis skills
Where powerful reference material exists and how to leverage to enhance your decision-making

1. Introduction to Data Analysis and Analytics

Definition and history
Current technology environment and the growing availability of data
Role of the business analyst and data analyst
Applications for gaining competitive advantages
Fact-based decision making
Process tracking and control

2. Application of Probability and Probability Distributions

Key concepts and essentials
Decision making under uncertainty
Random variables
Population and samples
The normal distribution
Many business distributions are nowhere near normal. constraints!
Establishing confidence intervals

3. Introduction to Data Mining and Data Warehousing

Data mining concepts and application
Introduction to application benefits of data warehousing

4. Describing Information Needs

Identify operational and executive information classes
Modeling key decisions and the needs for information
Describing key business transactions and documents
Map information needs to underlying data
Executive information needs and the balanced scorecard
Pivot tables in Excel
Tracking and managing business process performance
Selecting measures and targets
Measuring performance and finding performance gaps
Root cause analysis

5. Data Exploration Concepts and Formulas

Basic concepts
Types of variables
Selecting dependent and independent variables
Sample vs. population
Descriptive measures of a sample
Key sample parameters
Variability
Sampling distributions
Sample size
Histograms
Establishing and analyzing correlation among different variables
Explanation of variance

6. Introduction to Risk Management

Uncertainty and risk analysis
Assessing your organization risk culture and level of risk tolerance
Identifying, describing, ranking, prioritizing, and controlling risks
When to use quantitative risk analysis
Important risk management best practices

7. Forecasting

Forecasting methods and models
History of forecasting
Long and short term forecasts
Heuristics
Time series analysis
Establishing trends and business cycles (i.e., seasonality) and confidence limits
Selecting independent variables for predictive models including regression techniques

8. Review

Data analysis and analytics
Probability and distributions
Data mining, data warehousing, and the need for information
Statistic inference, forecasting, and decision support
Next steps options

9. Additional Resources and Exercises

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Introduction to Data Analysis Training Course in Germany

The Federal Republic of Germany is the second most populous country in Europe and is located in Central Europe. The official language of the country is German. Germany is one of the richest countries in the world. The main exports of the country include motor vehicles and iron and steel products.

Here are some fun facts about Germany:
The fairy tale writer, the Brothers Grimm, came from Germany and wrote many famous stories such as Cinderella, Snow White, and Sleeping Beauty.
Germany is home to the largest theme park in Europe, the Europa-Park.
The famous composer Ludwig van Beethoven was born in Germany.
The Autobahn, the German highway system, is known for having no general speed limit.

Berlin was divided by the Berlin Wall from 1961 to 1989. Known for its street art, Berlin has many colorful murals and graffiti throughout the city. Also, Berlin is home to many famous museums, such as the Pergamon Museum and the Museum Island. Many clubs and bars stay open until the early hours of the morning in this big city.

Another popular city is Munich, which is famous for its Oktoberfest beer festival that attracts millions of visitors every year. Munich is also home to many historic buildings, including Nymphenburg Palace and the Marienplatz town square.

The country's capital and largest city is Berlin, however Frankfurt is considered to be the business and financial center of Germany. It is home to the Frankfurt Stock Exchange, the European Central Bank, and many other financial institutions. Because of its central location within Europe and its status as a major financial hub, Frankfurt is often referred to as the "Mainhattan," a play on the city's name and its association with the Manhattan financial district in New York City.

Frankfurt is also a major transportation hub, with the largest airport in Germany and one of the largest in Europe, Frankfurt Airport. Additionally, it is a popular destination for tourists, with its historic city center, beautiful parks, and vibrant cultural scene.

Some of the top German technology companies like Siemens AG, Bosch, SAP SE, Deutsche Telekom, Daimler AG and Volkswagen has business centers in Frankfurt. The country has a strong tradition of engineering and innovation, and is home to many other world-class technology companies and research institutions.

Tailored to meet the specific needs of Germany, Bilginç IT Academy combines cutting-edge training methodologies with our comprehensive range of Certification Exam preparation courses and accredited corporate training programs. Experience a transformative approach to IT training that will redefine your expectations.

Introduction to Data Analysis Training in Germany

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Introduction to Data Analysis Training in Germany

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What You Will Learn

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ESSENTIAL TIPS FOR NEW DATA ANALYSTS

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