Key Concepts

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Data Analysis Definition

Data analysis is the process of mathematically summarizing data and evaluating patterns in data with the goals of discovering useful information, informing conclusions, and supporting decision making.

Data Analyses and Conclusions
Lesson 1 of 1
  1. 1
    When working with data, our ultimate goal is to draw conclusions. The point of data analysis is to discover useful information, inform conclusions, and support decision-making. In general, data…
  2. 2
    Let’s imagine we’re looking at a field of flowers like the one in the learning environment. There are flowers with different shapes, colors, and heights. If you had to describe these flowers, how w…
  3. 3
    Exploratory analysis is the next step after descriptive analysis. With exploratory analysis, we look for relationships between variables in our dataset. While our exploratory analyses might …
  4. 4
    Unsupervised machine learning techniques, such as clustering algorithms, are useful tools for exploratory analysis. These techniques “learn” patterns from untagged data, or data that do not…
  5. 5
    A/B tests are a popular business tool that data scientists use to optimize websites and other online platforms. A/B tests are a type of inferential analysis. Inferential analysis lets us test a…
  6. 6
    We know that correlation does not mean causation. This is an important limitation in data analysis. We should be cautious to believe any studies or headlines claiming that one thing caused another …
  7. 7
    Sometimes we need to establish causation when actual experimentation is impossible. This could be due to a variety of reasons. For example, we might want to know why something happened that we real…
  8. 8
    We interact with predictive analysis in everyday life when we text a friend using text completion or watch a suggested TV show on Netflix. Predictive analysis also underlies computer vision, wh…
  9. 9
    Predictive analyses have tremendous power. As a result, we need to be careful about when we use them and when we trust them. Recommendation algorithms are excellent for making low-risk prediction…
  10. 10
    Congratulations on finishing the lesson! We covered a lot of ground and introduced quite a few ideas in this lesson. Here is a recap of what we have learned: 1. Data analysis is the process of ma…

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