Credit by Portfolio Review

Learning objectives:

• Calculate descriptive statistics and generate basic visualizations using Python
• Explain and apply principles and rules of probability
• Utilize inferential statistics and communicate the uncertainty in statistical estimates
• Perform regression analysis and distinguish between correlation and causation

Include in your portfolio:

1. A learning narrative statement summarizing your experience using Python to perform the following data cleaning and statistical analysis tasks:
   • Preparing data—changing column names, identifying and addressing null values, replacing values, and creating features
   • Exploring data—determining data shape, generating descriptive statistics, and identifying correlated fields
   • Visualizing data—generating histograms, scatterplots, pair plots, and heat maps
   • Interpreting data—providing meaningful interpretation within a business context based on exploratory data analysis and data visualizations
   • Testing hypotheses—developing a null and alternative hypothesis, testing the hypothesis, and interpreting the test results
   • Performing predictive modeling—using multiple regression; making a prediction; and interpreting key model attributes, including coefficients and r-squared
2. A data analysis project demonstrating the above skills. You must:
   • Submit a well-documented code notebook.
   • Record and submit a 3–5 minute video explaining your analysis as you would to a new data team member who is familiar with Python but new to using it for data analysis. The video should be a screencast of you speaking while explaining the code notebook.

Learning objectives:

• Construct a variety of SQL statements
• Query and prepare data in response to business questions
• Design a database to meet technical requirements and a business need

Include in your portfolio:

1. A learning narrative statement summarizing your experience with the following SQL statements and concepts:
   • SELECT, INSERT, UPDATE, and DELETE
   • Grouping and filtering (e.g., GROUP BY, HAVING)
   • Aggregate functions (e.g., SUM, MIN, MAX)
   • Joins (e.g., LEFT, RIGHT, INNER)
   • Conditional logic (e.g., CASE WHEN)
   • Subqueries
   • Common table expressions
   • Window functions
   • Optimization
2. A document with SQL statements you have written that demonstrate the learning objectives and above concepts.

Learning objectives:

• Create compelling data visualizations and dashboards
• Explain the cloud landscape for business analytics (i.e., vendors, services, and costs)
• Setup and manage a cloud data warehouse
• Build and deploy a machine learning model in the cloud
• Analyze big data in a cloud environment

Include in your portfolio:

1. A learning narrative statement summarizing your experience with the following data visualization and cloud technologies concepts:
   • Creating data visualizations in business intelligence software (e.g., Tableau, Power BI, or Google Looker Studio). Include a highlight table, bar chart, pie chart, time series, scatterplot, and map.
   • Creating dynamic dashboards that tell a data story. Feature multiple visualizations and enable interaction.
   • Creating simple cloud-native data architectures that connect object storage, data warehouse, and machine learning tools to perform end-to-end analysis using one of the three major cloud providers (AWS, Azure, or GCP).
2. A demonstration of cloud architecture, including the tools used and analysis performed in that environment. The analysis:
   • Should be interpreted within a business context
   • Can be a 3–5 minute video, a slide deck presentation, or another format