In addition to the much-discussed lack of data scientists, it is becoming clear that there is a shortage of business users adept at working with data. However, experienced business users, in addition to data scientists, are an important pillar for embedding analytics in the organization over the long term and for promoting data-based decision-making. Formulating questions or hypotheses and examining them based on data requires specific skills. In addition, business users increasingly need to engage in dialog with data scientists to successfully complete projects: teaming up with data scientists is particularly useful when it comes to identifying use cases, explaining data, and understanding and verifying the results of mathematical analyses. An increasing number of business intelligence tools provide advanced data analysis methods, as well as data-processing capabilities. These make it easier for business users to analyze data in relation to specific questions. A basic understanding of methods in analytical projects, the most important analysis concepts, and the ability to correctly interpret the output of the advanced analysis methods is nevertheless required. Only with this knowledge is it possible in the interaction between data scientists and business users to clearly define use cases, exchange information about data smoothly, and make operational use of results from data labs. What’s more, knowing the potential benefits of the data increases the likelihood that attention will be paid to data quality.
BARC supports this process with its new seminar “Data Science for Business Analysts”. The objective of this BARC seminar is to introduce users from the line of business to the procedures and methods of data discovery and data science. Participants will work on the essential steps of data preparation, data analysis, and presentation of results using a concrete data set and specific analytical problems. It will qualify course participants to formulate analytical problems independently, prepare data, identify patterns in data, engage in dialog with data scientists, and interpret results from data labs. The goal of this one-day seminar is to understand how data quality, data preparation, visual analysis, and machine learning interlink to extract information from data. You can find more information on the registration page.