Written Report Preliminary Information Research Assignment

Written Report: (10 Marks)

Use this as a guide for your written report. Make sure you include all the information below.
Part 1: Preliminary information
1. What is the research question you have explored, and which data set did you use?
Part 2: Report (500 words)
We suggest you structure your report using the following sections. How many words you use
in each section is up to you, but the total word count (not including reference list) cannot
exceed 500 words (plus or minus 10%).
1. What is the title of your report:
2. Introduction – outline the research question and summarise your insights
3. Body
i) Describe the data and its key features.
ii) Briefly describe cleaning and wrangling of data.
iii) Explain the insights - this will involve showing your visualisations and explaining
them (label each visualisation clearly, using APA style). You will use extra research to
support your insights.
4. Conclusions – sum up and suggest ideas for further research
5. Reference list (APA)

THE FOLLOWING LIST ARE THE Data Formatting and Cleaning Tasks that have to be done on the provided excel list: The highlighted areas are the most important steps

1. Format Headings: Make the headings of your columns bold, change
the font size, or modify the background colour to ensure they are
distinguishable from the data. Properly capitalise headings, except for
conjunctions like "is" and "and".
2. Row/Column and Table Formatting: Adjust the row heights and column
widths for better visibility. Include gridlines in tables for ease of reading
and understanding the data layout.
3. Correct Spelling Mistakes: Use an automated spellcheck tool to identify
and correct any spelling mistakes throughout your dataset, rather than
doing it manually.
4. Remove and Replace Data: Identify unnecessary or incorrect data
entries and replace them with appropriate values.
5. Identify and Replace Null Values: For records with missing values,
either delete the record if the data is crucial or replace all missing
values in the relevant columns with "NA" to indicate that the data is not
available.
6. Change Capitalisation: Ensure that all relevant text is correctly
capitalised, while excluding specific conjunctions as mentioned.
7. Remove Duplicates: Check for duplicate entries in your dataset, such
as films being entered more than once, and remove them to maintain
data integrity.
8. Sort Data: Arrange the data based on IMDB scores from highest to
lowest, enabling you to quickly identify the top-rated films.
9. Change Date Format: Standardise the date formats within your dataset
(e.g., converting from DD/MM/YYYY to MM/DD/YYYY) as per your
requirement for consistency.
10.Split Data in Columns: If data is combined in a single column (for
example, "First Last" names), you can split it into separate columns for
first names and last names to enhance readability.
11. Use Basic Calculation Formulas: Apply basic formulas to calculate
averages or totals, such as determining the average score for each
movie genre.
12.Format Cells According to Appropriate Category: Ensure that numerical
data is formatted as numbers, financial figures are formatted as
currency, and text fields are formatted correctly for clarity.
13.Merge Sheets Using VLOOKUP: If your data is spread across multiple
sheets, use the VLOOKUP function to merge related information based
on a common identifier (e.g., movie titles).
14.Create Pivot Tables for Closer Analysis: Utilize pivot tables to
summarise large datasets, allowing you to analyse trends and conduct
more detailed examination of your data.
15.Extract and Copy Values from a Column: If you need specific data from
a column, you can easily extract and copy it to another location or
worksheet.
16.Convert Data Using IF and THEN Statements: Implement conditional
logic by using IF statements to classify or transform data based on
specific criteria (e.g., if the score is above 8, classify it as "Highly
Rated").

Question :

1. As you know, this subject is about using data to tell interesting stories – whether the stories are news reports, podcasts, social content, or reports for your boss. This assignment is designed to familiarise you with the basic ingredients for a good story. What are these ingredients? Two things mainly: (1) Interesting things, called insights, and your summary of these insights, called conclusions. For example, we’ve talked a lot in this subject so far about insights and stories, and you’ve practised gathering insights, suggesting ideas for stories, and even writing short stories. Now, in this assignment, we’ll go back a step and make sure we can gather insights from data and form conclusions. This will prepare you for your storytelling in the rest of the subject.

2. As you know, this subject is about using data to tell interesting stories – whether the stories are news reports, podcasts, social content, or reports for your boss. This assignment is designed to familiarise you with the basic ingredients for a good story. What are these ingredients? Two things mainly: (1) Interesting things, called insights, and your summary of these insights, called conclusions. For example, we’ve talked a lot in this subject so far about insights and stories, and you’ve practiced gathering insights, suggesting ideas for stories, and even writing short stories. Now, in this assignment, we’ll go back a step and make sure we can gather insights from data and form conclusions.

3. This is the RESEARCH QUESTION: Imagine you are part of a digital content team for a streaming platform like Netflix or Stan. Create a presentation for producers or executives to advise on the ‘key ingredients’ of a critically acclaimed, high-engagement TV series. Look at the data for insights to provide recommendations for acquiring and developing new content. Your presentation could include extra research to look at current production trends, audience engagement and popular genres of TV shows.

4. Using Excel, import, clean and wrangle the data sets (“wrangling” may involve analysing a certain number of rows/columns and deleting others, and/or creating pivot tables).

5. From the cleaned and wrangled data, create one stacked bar chart AND one visual presentation to help you arrive at 5 insights and explain them.

6. Write a script to explain your 5 insights to the class.

7. APA 7th edition referencing must be used

Steps to Create a Stacked Bar Chart

1. Define Your Key Metrics

• Identify the key ingredients you want to analyze. For example, you might consider:

• Genres (e.g., Drama, Comedy, Thriller)

• Audience Ratings (e.g., IMDb scores)

• Engagement Metrics (e.g., social media mentions, viewership numbers)

• Awards Won (e.g., Logies, AACTA Awards)

2. Gather Data

• Collect data from reliable sources such as:

• Streaming platform analytics (e.g., Netflix, Stan)

• Industry reports (e.g., OzTAM ratings, Box Office Mojo)

• Audience feedback platforms (e.g., Rotten Tomatoes, IMDb)

• Structure your data in a way that allows for easy comparison. For instance, you could create a table with genres as rows and metrics as columns.

3. Example Data Structure:

• | Genre | Average IMDb Rating | Social Media Mentions | Awards Won |

• |-----------|---------------------|-----------------------|------------|

• | Drama | 8.5 | 150,000 | 10 |

• | Comedy | 7.8 | 100,000 | 5 |

• | Thriller | 8.2 | 120,000 | 8 |

• | Sci-Fi | 8.0 | 90,000 | 6 |

4. Choose a Visualization Tool

• Use any available data visualization tool that supports stacked bar charts.

5. Create the Stacked Bar Chart

• Input Data: Enter your data into the chosen tool.

• Select Chart Type: Choose the stacked bar chart option.

• Configure Axes:

• X-Axis: Represent the genres of TV shows.

• Y-Axis: Represent the metrics you are analyzing (e.g., ratings, mentions, awards).

• Stacked Segments: Each bar will be divided into segments representing different metrics (e.g., one segment for ratings, another for social media mentions, and another for awards).

6. Design the Chart

• Color Coding: Use distinct colors for each metric segment to enhance clarity. For example, use blue for ratings, green for social media mentions, and orange for awards.

• Labels: Ensure each segment is labeled with the corresponding metric value or percentage for clarity.

• Title and Legend: Add a descriptive title (e.g., "Key Ingredients of Critically Acclaimed TV Series") and a legend to explain the color coding.

7. Interpret the Data

• In your presentation, explain what the stacked bar chart reveals about the relationship between genres and their respective metrics. Highlight any trends, such as which genre has the highest ratings or the most awards, and discuss potential reasons for these trends.

8. Connect to Recommendations

• Use insights from the chart to inform your recommendations for content acquisition and development. For example, if dramas have the highest ratings and awards, suggest focusing on acquiring more drama series or developing original content in that genre.

Assessment Requirements

The assessment was divided into two main parts: a Written Report and a Presentation .

• Written Report : This 10-mark component required a 500-word report following a specific structure. It needed to include a research question, an introduction, a body, a conclusion, and an APA-style reference list.

  • Body : The body of the report was the most crucial part. It required students to describe the data, explain the data cleaning and wrangling process, and present their insights using visualisations.

• Data Handling : A separate list of 16 specific data formatting and cleaning tasks was provided. These included tasks like formatting headings, correcting spelling, removing duplicates, sorting data, and using advanced functions like VLOOKUP and Pivot Tables . This part was the foundation for the entire assessment.

• Presentation : The presentation required students to use Excel to import, clean, and wrangle the data, create a stacked bar chart and another visual presentation, and write a script to explain five key insights to the class. The overall goal was to provide recommendations for content acquisition and development based on the data.

  • Deliverable : The key deliverables were the cleaned dataset, the visualisations, and the script explaining the insights.

The Academic Mentor's Approach

The academic mentor guided the student through the assessment in a step-by-step, process-oriented manner, focusing on each section's purpose and how it contributed to the final outcome. The mentor's guidance can be broken down into the following stages:

1. Understanding the Core Concept

The mentor began by helping the student understand the central theme of the subject: using data to tell stories. The mentor explained that the assessment was designed to teach the "ingredients" of a good story—specifically, insights (interesting facts from data) and conclusions (summaries of those insights). This context helped the student see the purpose behind each task, rather than just completing a checklist.

2. Defining the Research Question

The mentor first helped the student articulate the research question: "What are the key ingredients of a critically acclaimed, high-engagement TV series?" This step was critical as it provided a clear focus for all subsequent data analysis and interpretation.

3. Step-by-Step Data Wrangling

This was the most intensive part of the guidance. The mentor broke down the 16 data wrangling tasks into manageable steps, explaining the importance of each one.

• Initial Setup : The mentor first guided the student on how to import the data into Excel and perform basic tasks like formatting headings and adjusting row/column widths.

• Data Cleaning : Next, the mentor focused on the core cleaning tasks: correcting spelling, removing duplicates, and handling null values. The mentor emphasised that a clean dataset is the only way to get reliable insights.

• Data Manipulation : The mentor then introduced more advanced techniques, explaining how and why to use them.

  • Sorting : The student was guided to sort the data by IMDb scores to easily identify top-rated films.

  • Splitting and Merging : The mentor explained how splitting columns (e.g., names) and merging sheets using VLOOKUP enhances data usability and analysis.

  • Calculations & Conditional Logic : The student was shown how to use basic formulas for calculations and IF statements to categorise data (e.g., "Highly Rated"). This turned raw numbers into meaningful classifications.

  • Pivot Tables : The mentor explained that pivot tables are essential for summarising large datasets and identifying trends, which directly helps in finding insights.

4. Creating Visualisations & Scripting

After the data was clean and prepared, the mentor guided the student on how to create the visualisations.

• Stacked Bar Chart : The mentor explained the purpose of the stacked bar chart—to show the composition of different metrics (e.g., ratings, awards) for different genres. The student was guided to select appropriate metrics and configure the axes correctly to make the chart clear and easy to interpret. .

• Insight Generation : With the visualisations created, the mentor helped the student to interpret the data. They worked together to identify five key insights from the charts and the cleaned data.

• Script Writing : Finally, the mentor guided the student in writing a concise and clear script. The focus was on explaining the insights in a logical flow, starting with the data's key features, then presenting the insights from the visualisations, and concluding with clear recommendations for the executives.

Final Outcome and Learning Objectives

The final outcome was a well-structured and data-driven assessment submission that fulfilled all requirements. The student successfully produced a clean and wrangled dataset, a written report, a stacked bar chart, and a presentation script.

The learning objectives covered during this process were:

• Data Literacy : The student learned how to interpret and work with raw data.

• Data Wrangling & Cleaning : The student gained practical skills in using Excel to clean and format data, which is a fundamental skill in data analysis.

• Data Visualisation : The student learned how to create meaningful visualisations to represent complex data in an understandable way.

• Critical Thinking & Analysis : The student developed the ability to find "insights" by identifying patterns and trends in the data.

• Communication Skills : The student practiced articulating these insights into clear and compelling "stories" through both a written report and a presentation script, demonstrating their ability to communicate data-driven recommendations effectively.

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