1.1 Given a scenario, apply the appropriate statistical method or concept.

For each of the following concepts: Define what it is. What are the pros and cons of the method or concept? When would you use it? When would you use it in lieu of something else and why? In which situations is the concept used? What is required to use the concept? Apply the method

• t-tests
• Chi-Squared test
• Analysis of variance (ANOVA)
• Hypothesis testing
• Confidence intervals
• Regression performance metrics
  • R-squared
  • Adjusted R-squared
  • Root mean square error (RMSE)
  • F statistic
• Gini Index
• Entropy
• Information Gain
• p-value
• Type I and Type II Errors
• Receiver operating characteristics/area under the curve (ROC/AUC)
• Akaike information criterion/Bayesian infromation criterion (AIC/BIC)
• Correlation coefficients
  • Pearson correlation
  • Spearman correlation
• Confusion matrix
  • Classifier performance metrics
    • Accuracy
    • Recall

    […]

CompTIA Data Science Certification Beta Exam

I have 2 weeks to study for the CompTIA Data Science Certification Exam. I was accepted into the Beta exam group, so I get to take the exam before it’s available to the public and in return, I will provide feedback.

I was chosen because I have over 5 years of experience in Data Science and I have a background in Math (Bachelor’s Degree) and Data Science (Master’s Degree). So CompTIA felt like I would be qualified to take the exam and provide feedback.

There are 5 domains that the test covers:

1.0 Mathematics and Statistics (17% of exam)
2.0 Modeling, Analysis, and Outcomes (24% of exam)
3.0 Machine Learning (24% of exam)
4.0 Operations and Processes (22% of exam)
5.0 Specialized Applications of Data Science (13% of exam)

According to CompTIA, the CompTIA Data Science certification exam will certify the successful candidate has the knowledge and skills required to:

• Understand and implement data science operations and processes.
• Apply mathematical and statistical methods appropriately and understand the importance of data processing and cleaning, statistical modeling, linear algebra, and calculus concepts.
• Apply machine-learning models and understand deep-learning concepts.
• Utilize appropriate analysis and modeling methods and make justified model recommendations.
• Demonstrate understanding of industry […]

Evaluating a model from the business perspective is critical to ensure that it is meets the goals and needs of the organization, provides a good return on investment (ROI), is resource efficient, mitigates risks, has a positive impact on the business, and is aligned with the overall strategy of the organization.

In this post, we look at the model building process from a high level and walk through some specific tasks and considerations that should be made throughout this step. After reading, you will have enough information to make a model development game plan for your project!

Slow AI is about the responsible creation of data-driven systems through responsible, methodical, and well-documented processes.