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    <title>Data Science Homework Portfolio</title>

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    <h1> Data Science Homework Portfolio</h1>
    <h3 style="text-align: left"> <a href="/Regional_Inequality/"> ⧉ Final Project</a> </h3>


    <section>
        <div class="graphDesc">
            <h2> Embedding Two Charts </h2>
            <h3> Week 1 </h3>

            <summary> The first chart shows how Korea's population has rapidly aged, and how it is expected to continue doing
                so. The second chart shows how, simultaneously, Korea's population is expected to decline.
                </br>
            </summary>
        </div>

        <div class="weekFigures">
            <figure id="week1fig1"> </figure>
        </br>
            <figure id="week1fig2"> </figure>
        </div>
    </section>

    <section>
        <div class="graphDesc">
            <h2> Financial Times Replication </h2>
            <h3> Week 2 </h3>

            <summary> The first chart is a recreation of a chart from <i>'IMF bailouts hit record high as global economic outlook worsens. </i> The values in the first have been converted from IMF SDRs to USD. In the second, total borrowing is shown relative to total holdings.
                <p> <b>Riskiest Year:</b> Perhaps counterintuitively, I think the most risky years were in the early 2000s (e.g. 2003) when lending was greatest relative to holdings.</p>
                <p class='dataSource'>Analysis Worksheet: <a href="/week2/IMF_converted.xlsx">IMF_converted.xlsx</a></p>
            </summary>
        </div>

        <div class="weekFigures">
            <figure id="week2fig1"> </figure>
            <figure id="week2fig2"> </figure>
        </div>
    </section>

    <section>
        <div class="graphDesc">
            <h2> Inline Data Objects and Visual Encodings </h2>
            <h3> Week 3 </h3>

            <summary>
                In these two figures, I visualise related emissions data. The first is cross-sectional, showing the relationship between GDP Per Capita and emissions. The second shows multiple timeseries, illustrating trends in consumption emissions.
                <p> <b>Differences in Encodings: </b> The second uses a temporal versus quantitive encoding, with a line marker. I chose this as trends in emmisions are important in understanding the climate-crisis.  </p>
                <p class='dataSource'>Google Colab: <a href="https://colab.research.google.com/drive/13UnfzSsVEC5qj8X8f8VeFEzE89mBKsJB?usp=sharing">week3_proc.ipynb</a> | </p>
            </summary>
        </div>

        <div class="weekFigures">
            <figure id="week3fig1"> </figure>
            <figure id="week3fig2"> </figure>
        </div>

    </section>

    <section>
        <div class="graphDesc">
            <h2> Data Scraping </h2>
            <h3> Week 4 </h3>

            <summary>
                This figure shows the edit history of selected wikipedia pages.
                <p> <b>Why Wikipedia?: </b> Wikipedia acts for many as a gateway to concepts and information. Seeing edit
                    frequency can help us understand changes in popular understanding. </p>
                <p class='dataSource'>Google Colab: <a href="https://colab.research.google.com/drive/1ltXv0F3IQkk4_oKolZZxXHSpwdbilQbC?usp=sharing
                        ">WikiEditScraping.ipynb</a> </br> Data: <a href="https://raw.githubusercontent.com/FM-ds/FM-ds.github.io/main/week4/wiki_edits.csv
                        ">wiki_edits.csv</a></p>
            </summary>
        </div>

        <div class="weekFigures">
            <figure id="week4fig1"> </figure>
        </div>
    </section>

    <section>
        <div class="graphDesc">
            <h2> ONS Dashboard </h2>
            <h3> Week 5 </h3>

            <summary>
                Nine time series showing api fetched data with programmatically generated vega-lite json files.

                <p class='dataSource'>Google Colab: <a href="https://colab.research.google.com/drive/1Qf76i8m3Y3Mnj3D5XcbIrNpLZwtzI4Ho?usp=sharing
                        ">ons_dashboard.ipynb</a></p>
            </summary>
        </div>

        <div class="weekFigures">
            <figure>
                <div class="grid">
                    <div id="onsFig1"></div>
                    <div id="onsFig2"></div>
                    <div id="onsFig3"></div>
                    <div id="onsFig4"></div>
                    <div id="onsFig5"></div>
                    <div id="onsFig6"></div>
                    <div id="onsFig7"></div>
                    <div id="onsFig8"></div>
                    <div id="onsFig9"></div>
                  </div>
            </div>
            </figure>
    </section>

    <section>
        <div class="graphDesc">
            <h2> Maps </h2>
            <h3> Week 7 </h3>

            <summary>
                Two maps of Bristol's wards.
                <p> <b>What My Map Shows: </b> My map shows free school meal rates for schools in each of Bristol's wards.
                    This is often used as an indicator of socio-economic status. </p>
                <p class='dataSource'>Data: <a href="https://opendata.bristol.gov.uk/explore/dataset/wards/information/
                            ">Wards</a> </br> <a href="https://opendata.bristol.gov.uk/explore/dataset/free-school-meals-in-bristol/information/?disjunctive.ward_name&disjunctive.year
                            ">free-school-meals-in-bristol</a> </br>
               </p>
            </summary>
        </div>

        <div class="weekFigures">
            <figure id="week7fig1"> </figure>
            <figure id="week7fig2"> </figure>
        </div>
    </section>

    <section>
        <div class="graphDesc">
            <h2> Festival of Economics: Audio Charts </h2>
            <h3> Week 8 </h3>

            <summary>
                <b> Argument: </b>At the Big Data Event, <a href="https://www.tizianaalocci.com">Tiziana Alocci</a> briefly spoke about the 'exciting' potential of combining audio and visualization. This inspired me to try and represent some data through audio.
                <p> <b> Finding: </b> Positively, on the audio chart, time is mapped to time: an advantage. However, interaction takes longer. I believe the potential is exciting however. </p>
                <p> I chose to use trade war data, representing rounds of tarrifs through tones of different volumes presented on a prototype created with d3 and then conventionally with Vega.</p>
                <p class='dataSource'>
                     Audio Chart Source:<b> <a href="week8/audioChart.js">audiochart.js</a> </b> </br>
                    Data (Adapted From): <a href="https://www.scmp.com/economy/china-economy/article/3146489/us-china-trade-war-timeline-key-dates-and-events-july-2018/
                            ">US-China trade war timeline</a> </br> <b> Data: </b> <a href="week8/data.csv
                            ">data.csv</a> </br>
               </p>
            </summary>
        </div>

        <div class="weekFigures">
            <figure id="week8fig1"> </figure>
            <figure id="audioChart"> </figure>
        </div>
    </section>

    <section>
        <div class="graphDesc">
            <h2> Advanced Charts </h2>
            <h3> Week 9 </h3>

            <summary>
                For this task, I combined UN life expectency data with WHO marternal mortality and OECD healthcare spending data to produce a bubble chart and a connected scatter chart. Both are interactive.

                <p class='dataSource'>colab: <a href="https://colab.research.google.com/drive/1o7yoQksnk-gBvrAi0vbQ7wzNFkX5CyRi?usp=sharing
                            ">week9_proc.ipynb</a>
               </p>
            </summary>
        </div>

        <div class="weekFigures">
            <figure id="week9fig1"> </figure>
            <figure id="week9fig2"> </figure>
        </div>
    </section>

    <section>
        <div class="graphDesc">
            <h2> Machine learning - Supervised </h2>
            <h3> Weeks 10-11 </h3>

            <summary>
                <b>Hypothesis 1: </b> There is a negative relationship between first differenced (one year changes) healthcare expenditure and maternal mortality. It may not be linear.
                <p class='dataSource'> <b>Conclusion 1: </b> The first figure shows a very weak negative relationship between changes in expenditure and mortality - likely showing effects of spending are not instantaneous.</p>

                <p class='dataSource'>colab: <a href="https://colab.research.google.com/drive/1zPcLTCLdNr6QPb8B3agyHFIEHYRhgyPY?usp=sharing
                            ">ml.ipynb</a>
               </p>
            </summary>
        </div>

        <div class="weekFigures">
            <figure> <div>
                <img id='mlFirst' src='week10/first.png'></img>
            </figure></div>
            <div>
        </div>
        </div>
    </section>

    <section>
        <div class="graphDesc">
            <h2> Machine learning - Unsupervised </h2>
            <h3> Weeks 10-11 </h3>

            <summary>
                <p class='dataSource'> <b>Hypothesis 2: </b> The US healthcare system is an outlier among developed countries when considering spending, coverage, maternal mortality and life expectency.</p>
                <p class='dataSource'> <b>Conclusion 2: </b> The US does appear to be an outlier. When countries are grouped into four clusters, the US appears alone.</p>

                <p class='dataSource'>colab: <a href="https://colab.research.google.com/drive/1zPcLTCLdNr6QPb8B3agyHFIEHYRhgyPY?usp=sharing
                            ">ml.ipynb</a>
               </p>
            </summary>
        </div>

        <div class="weekFigures">
            <figure> <div>
                <img id='mlSecond' src='week10/second.png'></img>
            </figure></div>
            <div>
        </div>
        </div>
    </section>


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