Introduction to Data Journalism and Digital Storytelling
Are you newly shifting, or launching, to a new digital platform and looking for laying a solid background for your team to catch up with the most successful digital practices? We offer you the best opportunity for a workshop introduces your team to the highly efficient digital storytelling techniques and the latest trends of digital journalism around the globe, with a special focus on data-driven content. No previous background of any technical skills is required for this workshop. Throughout this workshop you’ll learn: - Expanding the horizon by viewing the most successful journalistic digital content from around the world, with special focus on data-driven stories. - How to think digitally. - Finding data sources and essential scraping techniques. - Fundamentals of data cleaning and analysis, using Microsoft Excel and Google Sheets. - Fundamentals of exploratory data visualization using Microsoft Excel and Google Sheets. - Data visualization for storytelling, using Datawrapper, Flourish and The Atlas.
Static and Interactive Data Visualization
In this course, your team will learn how to efficiently produce advanced static and interactive data visualizations professionally, using Adobe Illustrator and Tableau Public software. This workshop is right for you, if your have a team of information designers, graphic designers, researchers or data-focused journalists, bloggers and activists. Basic knowledge about Illustrator and Tableau Public is preferred. Throughout this workshop, your team will learn: - Fundamentals and introduction. - Illustrator charts: anatomy and options. - Illustrator symbols, color themes and text options. - Illustrator export options for digital and print. - Essential graph options, and how to play with colors, marks, tooltip, annotations, filters and highlighters. - How to build dashboards and stories in Tableau, and how to spark interactivity. - How to publish your Tableau work and embed it into your website.
Data Cleaning and Analysis with MS Excel
Get a way ahead of your counterpart teams of journalists, activists and researchers with learning the skills we provide through this workshop. The workshop doesn’t require any previous skills, but basic knowledge of MS Excel is highly preferred. By the end of this workshop, your team will have learned: - Inserting different data file types into excel. - Splitting columns, sorting, filtering, pivot tables and adding subtotals. - Managing and removing duplicates. - Basic and intermediate formulas and functions for data cleaning and analysis. - Using Excel charts for exploratory data visualization.
Static and Animated Mapping
This workshop is suitable for journalists, researchers, bloggers, graphic designers or those who are just interested to be a way ahead of their counterparts regarding dealing with geospatial data and using it for their storytelling. Basic knowledge about data types is essential. Solid background of basic data visualization tools is highly prefered. By the end of this workshop, your team will have learned: - Finding sources and understanding geospatial data, and converting addresses to coordinates. - Connecting data to QGIS and CARTO. - Shape files, data layers and how to join multiple datasets. - How to play with colors, markers, annotations and themes in QGIS and CARTO. - How to export your QGIS maps for print and digital publication. - How to create interactive maps with filters and highlighters in CARTO. - How to create animated maps through time in CARTO. - How to publish and embed your CARTO maps into your website.
Data Visualization with D3.js
Data Cleaning and Analysis with Pandas
If your team work with data regularly, you already know how much data cleaning absorbs of your work time. So, we give you the ultimate opportunity to master Python’s Pandas for data cleaning; one of the quickest, most efficient and enjoyable data cleaning tools. Basic programming skills using Python is preferred. Throughout this workshop, your team will learn: - How to work with notebooks, Jupyter Notebook in particular. - Loading and viewing your data on Python. - Flawlessly merging different data sets, and data concatenating techniques. - Exploratory visual and non-visual analysis, and diagnosing data for cleaning. - Restructuring, tidying, and pivoting data. - Testing your data, and final check-ups.