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revised learning objectives for data types and formats lesson #63

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revised learning objectives for data types and formats lesson #63

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revised learning objectives for data types and formats lesson
valpasq May 24, 2016
18e7738
group learning objectives into two main levels
valpasq May 27, 2016
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20 changes: 14 additions & 6 deletions 03-data-types-and-format.md
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Expand Up @@ -16,10 +16,18 @@ structure and format of our data.

## Learning Objectives

* Learn about character and numeric data types.
* Learn how to explore the structure of your data.
* Understand NaN values and different ways to deal with them.

*Lower-level objectives (Remember/Understand)*
* List the Pandas Type associated with each of the following Native Python Types: (1) string, (2) int, (3) float
* Explain what an ‘NaN’ value is, and list at least two challenges of dealing with NaN data.
* Describe the difference between floating point and integer numeric data types.
* Identify a major limitation of storing numbers as strings, e.g. ‘5678’ or ’3.16’.

*Higher-level objectives (Apply/Analyze/Evaluate/Synthesize)*
* Practice checking the data type of an object, e.g. a data frame, as well as a column within a data frame
* Demonstrate how you would convert a float to an integer, then demonstrate how you would convert an integer to a float.
* Demonstrate how you would replace NaN values with another value, e.g. 0 or -9999
* Investigate how leaving NaN values as-is versus replacing with 0 impacts analysis results, such as calculation of the mean value.
* Given what you’ve learned in this lesson, recommend an approach for dealing with NaN values in all attributes of the example dataset (surveys.csv) and explain your rationale

# Types of Data

Expand Down Expand Up @@ -68,7 +76,7 @@ is in the table below:
## Checking the format of our data

Now that we're armed with a basic understanding of numeric and character data
types, let's explore the format of our survey data. We'll be working with the
types, let's explore the format of our survey data. We'll be working with the
same `surveys.csv` dataset that we've used in previous lessons.

```python
Expand Down Expand Up @@ -150,7 +158,7 @@ print(24-4)

If we divide one integer by another, we get a float.
The result on python 3 is different than in python 2, where the result is an
integer (integer division).
integer (integer division).

```python
print(5/9)
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