Data Science Certificate Program

Certificate Overview

The data science certificate program that I developed and taught consisted of 3 - 15 week classes. Through these 3 classes, I brought students from entry level to advanced data science topics and how to build and run these models from their notebook interface.

DAT201 - Students were introduced to general data science topics popular ML models. Students experimented with models via Orange Machine Learning GUI

Here are the competencies for this course:

1. Analyze foundational concepts in data science

   1. Define data science

   2. Examine data science related case studies

   3. Define foundational concepts in data analysis

2. Examine components of data analysis pipelines

   1. Outline the data science process

   2. Demonstrate understanding of the reasoning for each step in the data analytics pipeline

3. Design and execute programs using a high-level language to solve basic data science problems

   1. Utilize data types, operations, control structures and iterations

   2. Utilize functions and follow scoping rules

   3. Demonstrate use of a debugging method

4. Analyze key concepts in data science project management

   1. Demonstrate an understanding of how a data science project progresses

   2. Explain what advantages or disadvantages exist for popular project methodologies

5. Evaluate ethical issues in data science

   1. Argue the difference between patents, copyrights, designs and trademarks and illustrate their use in the context of data science

   2. Explain how laws and technology safeguard from cyberattacks

   3. Describe the role of trade secrets in relation to data science

   4. Discuss how organizations with international ties must consider privacy laws, regulations, and standards across countries in which they operate.

   5. Compare and contrast individual privacy and security

   6. Compare the needs of society to individual rights to privacy

   7. Discuss potential concerns involving microtargeting and algorithmic fairness

6. Compare and contrast broad classes of learning approaches.

   1. Identify inputs of various learning approaches

   2. Identify outputs of various learning approaches

   3. Describe ranges of problem types to which learning approaches can be applied

7. Analyze and report data collection needs

   1. Document collecting high quality data for a particular purpose

   2. Document the resources needed to carry out a particular investigation

8. Perform data analysis

   1. Format/cleanse a dataset so that it can be better analyzed

   2. Identify interesting information from a dataset that could be used to make better business decisions

9. Report data findings

   1. Write a professional report based on a data analysis findings

   2. Present findings orally

DAT202 - In this course, students moved from GUI based modeling to building and running models programmatically in Jupyter Notebooks

Here are the competencies of this course: 

1. Examine data acquisition

   1. Define data acquisition

   2. Describe exploratory data analysis

2. Incorporate data acquisition

   1. Discuss various storage structures and methods for data gathering

   2. Write a web-scraper to gather data

   3. Access a database in a program

   4. Read data from a file in a program

   5. Discuss how ethics play a role in data acquisitions

3. Evaluate methods of data delivery

   1. Analyze types and uses of various data displays

   2. Apply best practices in displaying data

   3. Construct numerical summary of data

   4. Create visual summaries of data

4. Write a program to construct a pipeline for data analysis

   1. Perform data filtering

   2. Apply data transformation

   3. Perform data aggregation

   4. Construct data visualizations

   5. Model data

5. Outline the data exploration lifecycle

   1. Demonstrate understanding of the data exploration lifecycle and its components

   2. Explain the necessity of this lifecycle in producing quality results

6. Incorporate visual tools, techniques and strategies for data analysis

   1. Use graphical tools for data exploration

   2. Use appropriate techniques for data visualizations

7.  Evaluate the interpretation of results

   1. Describe the four types of data analytics: descriptive, diagnostic, predictive and prescriptive

   2. Explain the role of statistical significance in data analytics

   3. Compare and contrast reproducibility and repeatability

8. Examine ethical issues in data science

   1. Identify ethical issues in data science

   2. Discuss ethical behaviors of a data scientist

9. Report data findings

   1. Write a professional report based on a data analysis findings

   2. Present findings orally

DAT303 - In this final course in the certificate, students were introduced to advanced topics and completed a capstone project to demonstrate their learning.

The competencies for this course include:

1. Compare and contrast general machine learning concepts

   1. Describe the advantages/disadvantages of each model class

   2. Explain when a supervised leaning model should be used vs. an unsupervised one

   3. Discuss problems related to algorithmic and data bias, as well as privacy and integrity of data

2. Evaluate unsupervised learning models and their concepts

   1. Explain when and why a hierarchical clustering model is the appropriate tool for analyzing a dataset

   2. Discuss when and why k-Means clustering is the appropriate tool for analyzing a dataset

3. Analyze neural networks and their concepts

   1. Explain the advantages and disadvantages of using a neural network and when it is appropriate for making predictions

   2. Demonstrate understanding of backward propagation and how it applies to neural networks

4. Demonstrate an understanding of supervised learning models and the advantages/disadvantages of each

   1. Explain when and why a regression model is the appropriate tool for analyzing a dataset

   2. Discuss when and why a Naïve Bayes model is the appropriate tool for analyzing a dataset

   3. Describe when and why a decision trees model is the appropriate tool for analyzing a dataset

5. Build models to analyze datasets utilizing open source desktop machine learning tools

   1. Construct an unsupervised learning model to analyze a dataset

   2. Develop a supervised learning model to analyze a dataset

   3. Create a neural network model to analyze a dataset

6. Explore topics in data sampling

   1. Describe the different types of biases in data sampling

   2. Demonstrate the danger of overfitting

   3. Explain the purpose of training, validation and test datasets

   4. Use k-fold cross validation to evaluate the performance of a model

7. Evaluate modeling results and interpret the meaning/value of the results

   1. Define true/false positive/negative

   2. Give examples of recall, precision and accuracy

   3. Generate and use a ROC curve to evaluate prediction performance

   4. Interpret model quality by applying performance metrics such as root mean squared error (RMSE), confusion matrices, gain charts and silhouette scores

   5. Demonstrate an understanding of overfitting and underfitting and their causes

8. Construct machine learning models in Python to do data analysis on datasets

   1. Design a project that utilizes an unsupervised machine learning model to analyze a dataset

   2. Develop a project that utilizes a supervised machine learning model to analyze a dataset

9. Create an ensemble learning model

   1. Build an ensemble learning model that utilizes multiple machine learning model techniques to analyze a dataset

   2. Demonstrate an understanding of why and when you would most effectively utilize ensemble learning.

My Role

My role as instructor for these courses was to run the courses, grade submissions, and ensure students had the knowledge they needed to be successful data scientists. I was also responsible for justifying the certificate, developing the content, and setting up all courses in Blackboard.