The DCLI Professional Practicum is funded by the Data Collaborative for Local Impact (DCLI) program, and specifically administered through Data Zetu. The practicum aims to expose selected fellows to the use of data for decision making through individual program work, work with a host institution that is a leader in data analytics and evidence-based decision making, a joint project and structured training. The training is a four session programme in August 2018 designed and delivered by School of Data to provide fellows with foundational technical skills to effectively work with data.
- Training Details
- Learning Model
- Session Outline
- Software Installations
- Reference Books
- Online Resources
- Session Facilitator: David Selassie Opoku
- In-person sessions: 09.30 - 13.00
- Online Help sessions: Wednesdays & Thursdays
The training will expose fellows to relevant data skills needed to continue their personal learning after practicum. This will be achieved through the following:
This will be done through the School of Data Pipeline framework which aims to walk participants through the steps/phases of identifying a question or challenge to developing the needed insight or solution for intended users.
This model is adapted from the African Leadership University Learning Model which aims to take learners through discovery of new concepts, self-, peer- and facilitated-group learning. Below is a breakdown of this approach:
- Discovery stage: this is where a question/problem is introduced. For instance, the class will start out with a question about “How many hospitals in Dar es Salaam provide dialysis treatment?”. The facilitator and the learner will discuss various methods to answer this question including assessing whether this is the right question to ask, what datasets to use, where to find and get this data etc. Discovery stage will involve in-class sessions which could be half day or full-day workshops depending on the specific concept being presented.
- Self-learning stage: learners are given links to relevant resources and guided questions to explore the topic further. The goal is to have learners spend time exploring solutions on their own.
- Peer-learning stage: learners will work in groups of 3 - 5 to share discoveries in tackling specific questions. This creates an opportunity for learners who have been able to tackle the challenge explain their learning, and also create an opportunity for learners still working through challenges to have questions answered and concepts explained.
- Facilitated group-learning stage: this stage involves regrouping with the entire class to have a facilitated discussions on solutions and tools to the challenge. The facilitator will have answers to the questions but will present an opportunity for learners to explain their approach to answer questions. Emphasis will be on individual/group presentations during this stage.
| Date | Session Number | Description |
|---|---|---|
| 08-Aug-18 | Session 1 | Defining, Finding & Getting Data |
| 13-Aug-18 | Session 2 | Data Verification, Cleaning & Analysis |
| 22-Aug-18 | Session 3 | Data Presentation |
| 29-Aug-18 | Session 4 | Team Data Project |
- Microsoft Excel, LibreOffice Calc or Google Sheets
- Google Chrome Browser
- Tabula
- Open Refine
- InkScape Vector Graphics Editor
- Data Fundamentals - Introduction to Computer, Web and Spreadsheet Basics: PDF | Google Doc
- Data Fundamentals - Introduction to Data: PDF | Google Doc
- Data Fundamentals - Defining, Finding and Getting Data: PDF | Google Doc
- Data Fundamentals - Verifying, Cleaning and Analysing Data: PDF | Google Doc
- Data Fundamentals - Presenting Data: PDF | Google Doc
- Data Fundamentals: Scraping Data From PDFs: PDF | Google Doc
- Data Fundamentals Lab: Scraping Data from the Web: PDF | Google Doc
- Data Fundamentals Lab 3: Data Cleaning: PDF | Google Doc
- Data Fundamentals Lab 4: Calculate Sums, Rates and Percentages in Spreadsheets: PDF | Google Doc
- Data Fundamentals Lab 5: Calculating Averages and Percentages Changes: PDF | Google Doc
- Data Fundamentals Lab 6: Exploring Data in Larger Data Sets: PDF | Google Doc
- Data Fundamentals Lab 7: Exploring Data with Pivot Table: PDF | Google Doc