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Computer Assisted Reporting (Car)

Digital Journalism · Level 6  ·  5 Chapters

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What Computer Assisted Reporting (Car) covers

Computer Assisted Reporting (CAR) sits in Digital Journalism Level 6 as the unit where data becomes a story. The first outcome covers CAR tools, word processors and spreadsheets, importing data, mapping, reviewing data in Excel, data cleaning and building your own database. Software comes next: quantitative and qualitative data, statistical analysis software, data visualisation and categorisation. News sources include public domain databases and private sources, with security features and an assessment of data value. Data mining covers clustering, classification, association, prediction, classification and regression trees and text mining. Analysis closes the unit with regression, hypothesis testing, content analysis and narrative analysis.

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Careers after Computer Assisted Reporting (Car)

Computer Assisted Reporting (CAR) skills open doors to a wide range of exciting career paths in Kenya’s growing data-driven economy. Whether you dream of working in government, media, business, or starting your own venture, mastering these skills will empower you to turn raw data into powerful stories and informed decisions.

  • In the media industry, especially in newsrooms like Nation Media Group or Standard Group, CAR skills help journalists analyze and present data-driven stories that influence public opinion and policy.
  • In county governments and agencies such as the Kenya National Bureau of Statistics (KNBS), these skills are essential for managing and interpreting data used in planning and resource allocation.
  • The agribusiness sector, including companies like KTDA and smallholder cooperatives, relies on accurate data reporting for monitoring crop yields, market trends, and improving farmer incomes.
  • In the construction and infrastructure sectors, firms and county works departments use CAR tools to track project progress, budgets, and compliance with safety standards.
  • The matatu industry and transport companies use data analysis for route optimization, fleet management, and improving customer service.
  • You can also leverage these skills to become a data consultant or entrepreneur, offering services like data cleaning, visualization, and reporting to small businesses and NGOs, helping them make smart decisions.
  • Employment opportunities abound in organizations like KenGen, Safaricom, and hospitality chains where data-driven reporting supports operational efficiency and customer insights.

By mastering Computer Assisted Reporting, you position yourself at the heart of Kenya’s digital transformation, becoming a valuable asset in any sector. Your ability to turn complex data into clear, actionable information will not only boost your career but also contribute to Kenya’s growth and innovation. Keep learning, stay curious, and watch your future unfold!

Chapters

1
Determine CAR tools
10 topics
Open chapter →
2
Determine software in CAR
6 topics
Open chapter →
3
Determine sources of news
6 topics
Open chapter →
4
Apply data mining techniques
9 topics
Open chapter →
5
Analyze mined data
7 topics
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What Computer Assisted Reporting (Car) covers: 38 main topics in 5 chapters

1. Determine CAR tools

  1. 1.1 Introduction to CAR
  2. 1.2 Meaning of data
  3. 1.3 Telling stories using data
  4. 1.4 CAR tools
  5. 1.5 Use of word processors and spreadsheets in CAR
  6. 1.6 Data importation
  7. 1.7 Data mapping
  8. 1.8 Data review on Excel
  9. 1.9 Data cleaning methods
  10. 1.10 Creating your own database

2. Determine software in CAR

  1. 2.1 Types of computer data
  2. 2.2 Meaning of Quantitative data
  3. 2.3 Meaning of Qualitative data
  4. 2.4 Statistical data analysis software
  5. 2.5 Data visualization software
  6. 2.6 Data categorization

3. Determine sources of news

  1. 3.1 Sources of data
  2. 3.2 Security features for data sources
  3. 3.3 Public domain databases
  4. 3.4 Getting data from private sources
  5. 3.5 Assessing data value
  6. 3.6 Importance of assessing data mining sources

4. Apply data mining techniques

  1. 4.1 What is data mining
  2. 4.2 Techniques of data mining
  3. 4.3 Reasons for data mining
  4. 4.4 Clustering data
  5. 4.5 Data classification
  6. 4.6 Data association
  7. 4.7 Data prediction
  8. 4.8 Classification and regression data trees (CART)
  9. 4.9 Text mining

5. Analyze mined data

  1. 5.1 Meaning of data analysis
  2. 5.2 Reasons for analysing data
  3. 5.3 Techniques of analysing data
  4. 5.4 Regression analysis
  5. 5.5 Hypothesis testing
  6. 5.6 Content analysis
  7. 5.7 Narrative analysis

Revise this unit

Exam-style questions 70 Written to CDACC standard, with answers Test yourself 117 Multiple-choice, one at a time Practise questions 96 Quick recall — check you know the facts
3 more for this unit
Oral exam preparation 29 What the assessor asks, and how to answer out loud Worked calculations 14 Step-by-step maths with the common mistakes Quick cards 582 Myth busters, skill tips and scenarios

Beyond the syllabus

Decision simulator 3 On site, what would you do? Workplace scenarios 60 Real site situations — what would you do? Turn this into income 36 Where this skill earns — what to charge, how to find clients

Other units in Module V