Choosing Data Sources for Movie Viewing Statistics by Country

1. The Landscape of Data Sources

Before we can find data specific to our project, it's helpful to understand the common types of places where data is shared. There are vast repositories online, each with its own strengths and weaknesses.

To get a quick tour of this landscape, please watch the first few minutes of the following video. It provides an excellent overview of popular data source hubs.

As you saw, sources range from community platforms like Kaggle, which are great for a wide variety of projects, to highly specialized government or institutional portals like data.gov or data.nasa.gov. For our project—tracking movie popularity by country—we need something quite specific. A static dataset from Kaggle might be outdated, and government portals are unlikely to track international streaming habits. This means we need to be methodical in our search.

2. Defining Our Evaluation Criteria

To choose the best source, we can't just pick the first one we find. We need a systematic way to evaluate our options. Given your background in applied math and engineering, you'll appreciate that defining clear requirements is the foundation of a successful project.

For our goal, an ideal data source should meet the following criteria:

• Relevance: The data must measure movie popularity or viewership. Metrics like box office revenue are less relevant for at-home Christmas viewing than streaming charts or user ratings.
• Granularity: The data must be broken down by country. A global "top 10" list won't help us see what's specifically popular in France versus Japan.
• Timeliness: The data needs to be recent and ideally cover the specific pre-Christmas period (e.g., November-December). Daily or weekly updates are a huge plus.
• Accessibility: We need a practical way to get the data. An Application Programming Interface (API) is the gold standard for this, as it allows for programmatic, repeatable data extraction. We also need to consider cost and terms of use.
• Quality & Coverage: The source should cover a wide range of countries and movies. We also need to understand how "popularity" is measured—is it based on a large user base? Is the methodology transparent?

With these criteria in mind, let's evaluate some candidates.

3. Evaluating Potential Data Sources

Now let's apply our criteria to real-world sources relevant to the movie industry.

Candidate Type 1: Movie Metadata Databases (TMDb, OMDb)

When you search for "movie data API," you'll quickly find sources like TMDb (The Movie Database) and OMDb (Open Movie Database). These are excellent resources, but are they right for our primary goal?

The following article gives a great comparison of these APIs and explains why the most famous source, IMDb, is often not a practical choice for developers.

After reading the article, a crucial distinction should be clear:

• TMDb and OMDb are primarily metadata databases. They are fantastic for finding a movie's genre, cast, release year, or poster.
• However, they do not provide viewing statistics or popularity charts showing what people are actively watching in a specific country at a specific time.

Therefore, while these APIs are valuable, they don't provide the core data we need. We may use them later to enrich our data (e.g., to get the genre of a popular movie), but they can't be our primary source.

Candidate Type 2: Streaming Aggregators (JustWatch)

So, where can we find data on what people are actually watching? This leads us to services that track streaming availability and popularity, like JustWatch.

Let's examine their "Streaming Charts" page to see how it aligns with our criteria.

Let's evaluate JustWatch against our criteria:

• Relevance: Perfect. It directly measures movie popularity based on user activity on streaming services.
• Granularity: Excellent. The data is available for 140 countries.
• Timeliness: Excellent. The charts are updated daily.
• Accessibility: Ideal. They explicitly offer an API, which is exactly what we want for programmatic access.
• Quality & Coverage: Very good. The data is sourced from over 50 million monthly users, providing a solid sample size.

4. Conclusion and Next Steps

Based on our evaluation, JustWatch is the clear choice for our primary data source. It directly answers our core question—"What are people watching?"—with the country-level granularity and timeliness we need, and it provides an API for easy access.

In this lesson, we established a structured approach to data sourcing for our project.

Key Takeaways:

• Always start a data project by defining clear criteria for your data source, including relevance, granularity, timeliness, and accessibility.
• It's important to distinguish between primary data sources that contain the core information you need (like viewing statistics) and secondary sources that provide supplementary metadata (like movie genres).
• For our project, JustWatch has been selected as the primary data source because its Streaming Charts API meets all our key requirements.

Now that we've identified what data to use and where to get it, the next logical step is to figure out how to get it.

In our next lesson, we will dive into the practical side of this process: Extracting movie viewing data from sources with public APIs. We'll look at the fundamentals of interacting with an API like the one from JustWatch to start gathering the raw data for our analysis.