Introduction to Data Journalism

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<p>DATA JOURNALISM TRAINING Day 1</p> <p>DATA JOURNALISM TRAININGDay 1</p> <p>WHAT IS DATA</p> <p>Asking a question</p> <p>What we mean by data when we do data journalism?Whether you began with a question or not, you should always keep your eyes open for unexpected patterns, unusual results, or anything that surprises you. Often, the most interesting stories arent the ones you were looking for.</p> <p>NameGenderAgeHeightFeelingMandyF21150cmSwampedShaniF23167cmNervousZizoF25167cmCuriousAshleighF22163cmRelaxedDanyalM22156cmOptimisticJasonM36200cmFlusteredHannahF35167cmVery excited PhumlaniM24180cmGrumpyMilenaF29160cmExcitedData typesQUALITATIVE DATA: is everything that refers to the quality of something: A description of colours, texture and feel of an object , a description of experiences, and interview are all qualitative data.QUANTITATIVE DATA: is data that refers to a number. </p> <p>Data typesDISCRETE DATA: is numerical data with values which are distinct and separate, i.e. they can be counted. Examples might include the number of kittens in a litter; the number of patients in a doctors surgery; CONTINUOUS DATA: is numerical data with a continuous range. You can count, order and measure continuous data. For example height, weight, temperature, the amount of sugar in an orange, etc.</p> <p>Discrete data is counted, Continuous data is measuredDiscrete DataDiscrete Data can only take certain values.Example: the number of students in a class (you can't have half a student).Continuous DataContinuous Data can take any value (within a range)Examples:A person's height: could be any value (within the range of human heights), not just certain fixed heights,Time in a race: you could even measure it to fractions of a second,A dog's weight,The length of a leaf</p> <p>CATEGORICAL DATA: puts the item you are describing into a category; Examples can include gender, colour, size, etc.ORDINAL DATA: data which can be ranked (put in order) or have a rating scale attached. You can count and order, but not measure, ordinal data; Example: a scale from 1 to 5 </p> <p>Data typesData types quizRole: DrummerContinuous DataCategorical DataQuantitative Data</p> <p>Year Born: 1963 Qualitative Data Discrete Data Continuous Data Categorical Data</p> <p>Name: Rick Allen Quantitative Data Qualitative Data Discrete Data</p> <p>Size: MOrdered DataCategorical DataContinuous Data</p> <p>Height: 187cm Discrete Data Categorical Data Continuous Data Qualitative Data</p> <p>Date: 5th of March 2014Discrete DataCategorical DataContinuous Data</p> <p>Jargon busting</p> <p>Machine readable - if it is in a format that can be easily processed by a computer. Digital machine readabale. Example: a PDF document containing tables of data (is digital but are not machine-readable because a computer would struggle to access the tabular information even though they are very human readable!). The equivalent tables in a format such as a spreadsheet would be machine readable. In general, HTML and PDF are *not* machine-readable.</p> <p>Data pipeline</p> <p>DATA ETHICS &amp; VERIFICATION</p> <p>[Jason]</p> <p>Good practices and basic ethicsSave original copy of data and do not touch it.Paper trail - Keep a log with every step that you take in the analysis.Do not change original columns. Duplicate them and make the changes here.Have several drafts and look at how your analysis developed.Spend to understand your data. Read the methodology. </p> <p>Good practices and basic ethicsDo not assume what the data is. Run integrity check on each column.Clean the data before interviewing itCount the records. Cross-reference with the methodology. Report any inconsistency and request the missing data or a recount. Keep the total records in mind while analysing the data.If a result looks to good to be true, it probably is.Make a summary of the end results, as if you were writing a press release. Look for mistakes</p> <p>Good practices and basic ethicsHave somebody else verify your work, preferably somebody who knows nothing about your project.Check your biases and look at your data from new anglesLook for context that would explain your results to yourself and to your audiencee.g. Egypt worst country for womens rightsBounce your results against experts</p> <p>FINDING DATA &amp; DATA SOURCES</p> <p>Advanced searchGoogle Advanced SearchWayback Machine for the dead web (1996 onwards)</p> <p>Search operators* (asterix) substitutes a word and will allow your search to cover similar phrasesCache: - allows you to find web pages hidden in Googles cachefiletype: - will get look for the specified file typeLink: - helps you find all the sites that link to a particular page</p> <p>Search operators or (Quotation marks) help you find the exact phrase+ or AND narrows down your search by returning the exact word phrasesOR expands search by including either of two search phrases- or NOT it would tell an engine to exclude a terme.g. Monsanto-agent orange WHAT MAKES A GOOD VISUALISATION?</p> <p>What makes a good visualisationFor each of these visualisations think of:What is the target audienceWhat is the key messageHow successful are they in communicating the messageWhat makes them stand out?How well are they explained?How simple/ complex they are?</p> <p>Source: The EconomistCOMPARE</p> <p>Source: BBC NewsCOMPARE AND PUT IN CONTEXT:</p> <p>put in context the loss of men and women in the Afgan was as compared to Vietnam and the second World War</p> <p>Source: The GuardianShow trendsSource: New York Times, Amanda Cox; </p> <p>SHOW TREND OVER TIMESource: The Functional Art, Alberto Cairo</p> <p>Trend over timeCompares different presidenciesSource: Lower Saxony State Elections</p> <p>Source: Population pyramid Source: Hans Rosling, 200 Countries, 200 Years, 4 Minutes</p> <p>Show trend over timeTell a storyEngage, captivateCompares countries</p> <p>Source: The Wall Street JournalPatterns</p> <p>Source: Where does my money go, UKPersonal angle</p> <p>Show hierarchy</p> <p>Source: Where does my money go, UKPersonal angle - people get where they fit in the bigger pictureCompares, puts things into perspective</p> <p>Source: Spending stories</p> <p>Source: Driven by Data, Gregor AischShow relations</p> <p>Source: The Guardian</p> <p>Source: Transparency International</p> <p>Source: The Guardian</p> <p>Source: Migrations Map</p>