Interactive Data Analysis

IN A DIGITIZED WORLD, DATA IS THE KEY TO SUCCESS. BUT IS YOUR COMPANY STILL ABLE TO PROCESS EFFECTIVELY THE GROWING AMOUNT OF DATA? WITHOUT A RELIABLE AND FAST ANALYSIS AND INTERPRETATION OF DATA, ITS VALUE GO TO RACK AND BIG DATA BECOMES A MERE BUZZWORD.

Digital transactions, sensor data, web interactions – as a result of digitization global data volumes are growing with 2.5 trillion bytes per day almost four times faster than the global economy – with upward tendency. This opens up undreamt-of opportunities for companies and institutions, for example in the real-time control of processes or the development of new business fields. However, by the enormous amount of data new challenges arise as well - not only in terms of computing and storage capacity, but also in the area of evaluation: Without reliable and fast analysis and interpretation of the data, its value decays.

Conventional analyses are usually created in dedicated departments and are based on complex programming methods and downstream reporting. This is a time-consuming process that is reaching its limits with the exponential increase in data volume: As long as the data is analyzed and the corresponding report is in progress, the respective users have to attend and inevitably fall behind their schedule. Or the analyses are created in proprietary software that no longer meets the requirements of the time and is sometimes connected with high license fees. In order to counter these problems with up-to-date answers and scalable solutions, KÖNIGSWEG has developed a workshop and training program that anchors the corresponding expertise for agile and valid data analysis in the relevant departments.

Within the course, our experts initially impart basic technical knowledge in the context of Big Data (NoSQL, Hadoop, Spark ...) and design the best individual solution for your company based on Jupyter and Python - a highly intuitive and adaptable programming language - and pandas or Spark for high-performance processing of all common data formats (CSV, Excel, HDF, SQL, JSON ...). Based on the KÖNIGSGWEG workshop, companies not only receive the know-how to analyze high-volume data sets intra-departmentally. Through direct access to visualization and statistical functions, they also gain the ability to create their own reports and derive decisions directly from them.

This methodology is used to optimize departmental workflows. At the same time, it promotes the knowledge exchange and cooperation between the various units in a company, since the report history is completely available in order to be transferred between different departments. As an open source platform, Python not only offers the advantage of a royalty-free license. Due to permanent product optimization, it also provides greater adaptability and a higher security standard than proprietary applications.

Typical workshop process:

Part 1 – Basic principles

  • Basics Jupyter Notebooks and pandas (information)
  • Data input and output in various formats (CSV, Excel, JSON, SQL, HTML ...)
  • Exercise part
  • Access and selection of data series
  • Exercise part
  • Boolean indexing
  • Summary and Q&A
Part 2 – Visualization of data sets

  • Introduction of data visualization with pandas
  • Exercise part
  • Customize & extend data visualization
  • Exercise part
  • Prospect: Visualization beyond matplotlib: Bokeh
  • Summary and Q&A
Part 3 - Data analysis and aggregation

  • Overview of potential data aggregation
  • Exercise part
  • Extended analysis options with indexing:
  • TimeSeries and Resampling
  • Exercise Part
  • Summary and Q&A
Part 4 – Jupyter and Integration

  • Extended functions of Jupyter
  • Integration with SciKit-Learn
  • Stream processing
  • Integration PySpark
  • Summary and Q&A

How your company benefits from our workshop:

  • Streamlining of all analysis and reporting processes
  • Flexibility due to agile and versatile integration of numerous data formats
  • Productivity through real-time visualization
  • Time savings and less redundancy by collaborative work of different business units
  • Independence from external service providers through direct integration in the respective departments and sustainable know-how transfer
  • Improved IT security and diverse customization options using open source technology
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