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Coding
Module 1
Python Foundations in a Notebook
1
Running and Editing Python Code in Google Colab
Run and edit Python code cells in Google Colab and identify their displayed outputs.
Run and edit Python code cells in Google Colab and identify their displayed outputs.
2
Variables and Data Types: Integers, Floats, Strings, and Booleans
Assign values to variables and distinguish integers, floating-point numbers, strings, and Boolean values.
Assign values to variables and distinguish integers, floating-point numbers, strings, and Boolean values.
3
Combining Operators and Type Conversions for Desired Results
Use arithmetic operators, string operations, and type conversions to produce a specified result.
Use arithmetic operators, string operations, and type conversions to produce a specified result.
4
Making Decisions with Comparisons and Conditions
Use comparisons, Boolean operators, and conditional statements to choose between actions.
Use comparisons, Boolean operators, and conditional statements to choose between actions.
Module 2
Working with Collections and Reusable Code
5
Python Lists: Creating, Indexing, Slicing, and Updating
Create, index, slice, and update Python lists.
Create, index, slice, and update Python lists.
6
Transforming and Filtering Lists with For Loops
Use a for loop to transform or filter values in a list.
Use a for loop to transform or filter values in a list.
7
Defining and Calling Functions with Parameters and Return Values
Define and call a simple function with parameters and a return value.
Define and call a simple function with parameters and a return value.
8
Using Error Messages to Fix Common Python Errors
Use an error message to correct a NameError, TypeError, or syntax error in a short program.
Use an error message to correct a NameError, TypeError, or syntax error in a short program.
Module 3
Tabular Data with pandas
9
Loading a CSV File into a Pandas DataFrame in Google Colab
Load a CSV file into a pandas DataFrame in Google Colab.
Load a CSV file into a pandas DataFrame in Google Colab.
10
Exploring DataFrame Structure and Missing Values
Inspect a DataFrame's rows, dimensions, column names, data types, and missing values.
Inspect a DataFrame's rows, dimensions, column names, data types, and missing values.
11
Selecting Columns and Filtering Rows with Conditions
Select DataFrame columns and filter rows using one or more conditions.
Select DataFrame columns and filter rows using one or more conditions.
12
Handling Missing Values and Converting Data Types
Handle missing values and convert a DataFrame column to an appropriate data type.
Handle missing values and convert a DataFrame column to an appropriate data type.
Module 4
Analysis Practice and Interview Readiness
13
Calculating Descriptive Statistics and Grouped Summaries
Calculate descriptive statistics and grouped summaries from a DataFrame.
Calculate descriptive statistics and grouped summaries from a DataFrame.
14
Creating Clearly Labeled Bar and Line Charts
Create a clearly labeled bar chart or line chart from tabular data.
Create a clearly labeled bar chart or line chart from tabular data.
15
Drawing Conclusions and Identifying Limitations from Evidence
Interpret a summary and chart to state an evidence-based conclusion and one limitation.
Interpret a summary and chart to state an evidence-based conclusion and one limitation.
16
Explaining Python Data Analysis Steps in a Mock Interview
Explain the purpose and output of each step in a short Python data analysis during a mock interview.
Explain the purpose and output of each step in a short Python data analysis during a mock interview.