Learning Goals
Academic success in DA M1 means that students demonstrate proficiency and comfort with the concepts below. The expected mastery level can be understood with the following scale:
- Mastery: student is able to explain and implement the concept independently or with light reference.
- Functional: student recognizes when to use the concept and can implement it with the support of * documentation and/or a collaborator.
- Familiarity: student can recognize and describe the concept when needed/appropriate.
A student who promotes from this module will be able to do the following:
Querying Data
Mastery
- Students will be able to write SQL statements to query data.
- Students will be able to perform basic arithmetic.
- Students will be able to implement conditional statements.
- Students will be able to group data by clauses.
- Students will be able to apply an SQL condition after grouping data.
- Students will be able to find data in different tables using appropriate types of joins.
- Students will be able to set up a database connection.
- Students will be able to write SQL following best practices.
Functional
- Students will be able to format dates.
- Students will be able to avoid SQL pitfalls like cartesian products and fan outs.
- Students will be able to insert data in a database.
- Students will be able to update data in a database.
- Students will be able to use window functions.
- Students will be able to optimize SQL queries to reduce the consumption of resources.
- Students will be able to load CSV files.
- Students will be able to export data to CSV files.
- Students will be able to reference data in multiple tables using keys.
Python
Mastery
- Students will be able to use conditional logic to control programming execution.
- Students will be able to use loops to perform the same operation multiple times.
- Students will be able to use variables to store data.
- Students will be able to perform basic mathematical operations.
- Students will be able to use the right data types when dealing with data.
- Students will be able to install Anaconda.
- Students will be able to install Python packages using Anaconda.
- Students will be able to use Jupyter notebooks to explore data and write reports.
- Students will be able to use Pandas to load data into dataframes.
- Students will be able to use database connectors to access databases.
- Students will be able to use version control to collaborate with their team.
- Students will be able to export data from Jupyter.
Functional
- Students will be able to use Numpy to create series objects.
- Students will be able to use Seaborn to create data visualizations.
Familiar
- Students will be able to use Matplotlib to create data visualizations.
Statistics
Mastery
- Students will be able to calculate central tendency indicators and use them to describe a population or a sample.
- Students will be able to use descriptive statistics to analyze a population.
- Students will be able to use the standard deviation to calculate how spread out are the measurements for a group from the average.
Functional
- Students will be able to apply the central limit theorem as a basis for sampling statistics.
Reporting
Mastery
- Students will be able to use bar graphs to show comparisons betweens categories of data.
- Students will be able to use line graphs to track changes over time.
Professional Skills
Mastery
- Students will be able to handle data discrepancies.
- Students will be able to present their results to stakeholders.
