Introduction to Data Analysis, Norway

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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

Introduction to Data Analysis Training Course in Norway

The Nordic country Norway, is in Northern Europe. Known for its stunning natural beauty, including fjords, mountains, and forests, Norway is also famous for its high standard of living and strong social welfare system. Norway's capital and largest city is Oslo. Tromsø, Bergen, Trondheim and Stavanger are the other tourist attracting cities of Norway.

Norway is a constitutional monarchy with King Harald V as the head of state. The country has a population of 5,425,270 as of January 2022. Norway is a relatively small country and has a relatively low population density, with much of its land area covered by forests, mountains, and fjords. Despite its small size, Norway is known for its rich cultural heritage, strong economy, and stunning natural beauty, which attracts millions of visitors every year. This Nordic country is also known for its winter sports, such as skiing and snowboarding, and is a popular destination for outdoor enthusiasts.

Norway has a long history of invention and is home to numerous more top-tier tech firms and research facilities, such as; Kongsberg Gruppen, Telenor, Atea, Evry and Gjensidige Forsikring.

Due to the country's high latitude, there are large seasonal variations in daylight. From late May to late July, the sun never completely descends beneath the horizon. Which attracts many tourists around the world to see the "Land of the Midnight Sun". Tourists mainly visit Sognefjord, Norway's Largest Fjord, Pulpit Rock, one of the most photographed sites in Norway and of course the capital; Oslo.

Oslo is considered the business center of Norway. It is the country's largest city and the capital of Norway. The city is home to many of Norway's largest and most important companies, as well as several international organizations and research institutions. Additionally, the city is a popular tourist destination, known for its scenic location on the Oslo Fjord, its many museums and cultural attractions, and its vibrant nightlife and dining scene. Some of the most popular museums in Oslo are The Norwegian Museum of Cultural History, The Nobel Peace Center, The National Museum of Art, Architecture, and Design, The Munch Museum and The Vigeland Museum.

Introduction to Data Analysis Training in Norway

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Introduction to Data Analysis Training in Norway

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ESSENTIAL TIPS FOR NEW DATA ANALYSTS

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