News / Blog: #data

Statistics in Online Marketing: How Data Helps to Reach Target Groups Better

04/21/2023 | By: FDS

Today, online marketing is an important part of any successful marketing strategy. The use of data and statistics is essential to better understand target groups and develop successful marketing campaigns. In this article, we will show how statistics are used in online marketing and what advantages this offers.

The collection of data is a crucial factor in online marketing. By analyzing data, target groups can be better understood. For example, information such as age, gender, place of residence and interests can be collected to perform an accurate target group analysis. This helps to develop appropriate marketing messages and to optimize the approach to the target group.

Statistics is an important tool for gaining valuable insights from this data. Statistical methods such as correlation analysis and regression analysis can be used to identify relationships between different data points. For example, correlations between demographic data and buying behavior can be uncovered to develop more targeted marketing messages.

Another important statistical tool in online marketing is A/B testing. Here, a campaign is designed in two variants and presented to the target groups. By comparing the results from both groups, it can be determined which variant was more successful. As a result, adjustments can be made to further optimize the campaign.

The evaluation of web analytics data is also an important part of online marketing. Analyzing metrics such as click-through rate, dwell time and conversion rate helps to better understand user behavior on the website. By using this information, adjustments can be made to improve the user experience and increase conversion rates.


Statistics play an important role in online marketing. Collecting and analyzing data makes it possible to better understand target groups and develop successful marketing campaigns. Statistical methods such as A/B testing and web analytics can be used to make targeted adjustments to improve the user experience and increase conversion rates. Anyone who wants to be successful in online marketing therefore cannot avoid using statistics and data analysis.

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How in demand are Data Scientists on the job market?

04/19/2023 | By: FDS

Data scientists are currently in high demand on the job market. According to various reports and forecasts, the demand for data science professionals will continue to rise in the coming years.

A study conducted by the job portal Indeed in 2021 found that Data Scientist are currently among the most in-demand jobs. Similar findings have been published in other studies and reports by McKinsey, LinkedIn and others.

The increasing demand for Data Scientists is due to the growing importance of data and data analytics in many industries and businesses. Companies need professionals who can analyze large amounts of data and derive insights from the data to make better decisions and optimize their business processes.

In particular, the demand for Data Scientists is high in industries such as finance, technology, healthcare, e-commerce, and research and development. In these industries, Data Scientists are used to analyze customer behavior, develop predictive models, optimize business processes, and identify patterns and trends.

Overall, it is certain that Data Scientists will continue to be in high demand in the future, as the importance of data and analysis will continue to increase in many industries and companies.

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Data Science Jobs - How and where to get started?

04/07/2023 | By: FDS

Starting a career in Data Science can vary depending on your background and experience. Here are some ways to get started in the field:

Degree in a relevant field: A bachelor's or master's degree in computer science, statistics, mathematics, physics, or another related field can be a good starting point for a career in Data Science.

Data Analysis and Programming Skills: Experience in data analysis, programming, and working with statistical methods are essential for a career in Data Science. It is advisable to gain experience working with Python, R, SQL, and other relevant tools and technologies.

Internships and Volunteering: Internships and volunteering in Data Science projects or with companies can help gain practical experience and skills.

Online courses and certifications: Online courses and certifications in data science and related fields, such as data mining, machine learning, and artificial intelligence, can help gain knowledge and skills.

Networking: connecting with professionals and others in the industry can help identify potential job opportunities and gather information about the industry.

Some of the most common entry-level positions in data science include data analyst, data scientist, business analyst, and machine learning engineer. Most companies offering data science positions are looking for applicants with a combination of technical skills and an ability to interpret data and turn it into business results.

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Data Science - Is the Future in Data?

04/03/2023 | By: FDS

Data Science is a growing and forward-looking industry in which the analysis and processing of data is playing an increasingly important role. Data is generated, collected and stored in almost every industry and business sector. Businesses, governments, healthcare organizations, and other institutions collect and analyze data to make decisions, identify trends, and improve their business processes and services.

Data Science is an interdisciplinary field that combines methods from mathematics, statistics, computer science, and other fields to gain insights from data. It involves processing and analyzing data, building predictive models, and identifying patterns and trends.

Data science is also important for the development of artificial intelligence and machine learning, as these technologies must be trained on large amounts of data. In the future, companies and organizations that are able to use data effectively will have a competitive advantage and drive innovation.

Overall, it is certain that the future is in data and Data Science will play a critical role in processing and analyzing data.

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Python in Data Science - Why it is indispensable

03/15/2023 | By: FDS

Python is an indispensable tool in Data Science today and has established itself as one of the leading programming languages for Data Science and Machine Learning. Here are some reasons why Python is so important for Data Science:

Simplicity and Flexibility: Python is an easy language to learn and provides a clear and consistent syntax that enables rapid development and prototyping of Data Science applications. Python is also flexible and can be used for a variety of applications and problem solving.

Wide range of libraries and frameworks: Python has an extensive library of data analytics and machine learning tools, including NumPy, Pandas, Scikit-learn, TensorFlow, and Keras, which allow developers to quickly and easily build and train sophisticated models.

Community support: Python has a vibrant developer community that provides a variety of resources, tutorials, forums, and libraries. This makes it easy to get started with data science development and allows developers to quickly respond to questions and problems.

Integration capability: Python can be easily integrated with other tools and platforms, including SQL databases, Hadoop, and Spark, making it a versatile tool for data analysis.

Growing demand: Python is one of the fastest growing programming languages and is being used by a growing number of companies and organizations to develop data science applications and machine learning models.

In summary, Python is an indispensable tool in Data Science due to its simplicity, flexibility, large libraries, community support, integration capabilities, and growing demand.

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