Available Until 11/8/2026

Basics of Data Analytics Certificate

Release Date: November 8, 2023
Expiration Date: November 8, 2026
Activity Type: Application/Knowledge-based
CE Credit Hour(s): 16.25 contact hours


The use of big data has the potential to transform the healthcare industry from a fee-for-service model to value-based care. This emerging area faces several challenges on this path. To help combat these challenges, the Basics of Data Analytics certificate provides education on the basic concepts and tools used for data analysis. This certificate provides 16.25 contact hours and includes an opportunity for learners to apply their newly acquired skills in a data analysis project.

Professional Certificate Requirement

Once a learner has completed the educational curriculum, they will have the opportunity to complete an online comprehensive exam. Once the learner completes the exam (minimum 80% passing rate; unlimited attempts permitted), they will earn the professional certificate.

The American Society of Health-System Pharmacists is accredited by the Accreditation Council for Pharmacy Education as a provider of continuing pharmacy education with Commendation.

These activities are designed for pharmacists and pharmacy technicians who are interested in expanding their knowledge and skills in data analytics.

Learning Activity

ACPE Number

Contact Hours

 Introduction to Data Analytics

 0204-0000-23-836-H04-P & T


 Medication Use Process & Data Culture, Ethics, Security

 0204-0000-23-837-H04-P & T


 Step 1: Requirement Gathering

 0204-0000-23-838-H04-P & T


 Step 2: Get the Data

 0204-0000-23-839-H04-P & T


 Step 3: From Chaos to Confidence—Unveiling Effective Strategies in Pharmacy Data Validation

 0204-0000-23-840-H04-P & T


 Step 4: Clean, Manipulate, and Analyze Data

 0204-0000-23-841-H04-P & T


 Artificial Intelligence

 0204-0000-23-842-H04-P & T  1.0

 Step 5: Visualize & Communicate Concepts

 0204-0000-23-843-H04-P & T  3.25

 Project: Putting It All Together

 0204-0000-23-844-H04-P & T


  →  Final Assessment: (80% passing score required)

Introduction to Data Analytics
ACPE: 0204-0000-23-836-H04-P & T
Activity Type: Knowledge-based

  • Define the data literacy dimensions from the Maturity Model for Data Literacy.
  • Describe various tools for data analytics.
  • List resources for additional learning, troubleshooting approaches, and technical tips.

Medication Use Process & Data Culture, Ethics, Security
ACPE: 0204-0000-23-837-H04-P & T
Activity Type: Application-based

  • Summarize the lifecycle of data in the medication use process.
  • Explain uses of data that pose ethical concerns.

Step 1: Requirement Gathering
ACPE: 0204-0000-23-838-H04-P & T
Activity Type: Application-based

  • Differentiate the perspectives of business users and analysts for analytics asset development.
  • Prioritize core questions for implementing the development of new analytics tools.
  • Create an analytics request that meets business user and analyst needs.

Step 2: Get the Data
ACPE: 0204-0000-23-839-H04-P & T
Activity Type: Application-based

  • Describe the purpose of databases.
  • Explain the types of Structured Querying Language (SQL) database relationships.
  • Describe how to use SQL to retrieve data.
  • Summarize how SQL can be used for data aggregation.

Step 3: From Chaos to Confidence—Unveiling Effective Strategies in Pharmacy Data Validation
ACPE: 0204-0000-23-840-H04-P & T
Activity Type: Application-based

  • Differentiate common Structured Query Language (SQL) issues and their impact on data accuracy.
  • Apply strategic validation techniques (e.g., sampling, critical field validation) to validate row-level data.
  • Apply cross-source validation techniques to confirm the absence of false positives and false negatives in population-level data.

Step 4: Clean, Manipulate, and Analyze Data
ACPE: 0204-0000-23-841-H04-P & T
Activity Type: Application-based

  • Evaluate data to determine whether manipulation is required.
  • Choose the appropriate data manipulation tool for a project.
  • Identify the scope and strengths of numerical data types.
  • Identify the scope and strengths of text data types.
  • Summarize various techniques to interrogate and clean data in Microsoft Excel.
  • Differentiate solutions to common data errors in Microsoft Excel.
  • Apply appropriate data manipulation techniques to various dataset scenarios.
  • Select the correct method to load data into Python, given a specific source format.
  • Use a Lambda function to manipulate date based on two columns.
  • Compare different ways to select subsets of a DataFrame.
  • Select appropriate packages and functions to clean data using R.
  • Modify datasets through filtering, joining, and transformation using R.
  • Analyze data using descriptive methods and inferential statistics in R.     

Artificial Intelligence
ACPE: 0204-0000-23-842-H04-P & T
Activity Type: Application-based

  • Describe potential uses for artificial intelligence in pharmacy practice.
  • Compare approaches for training machine learning models.
  • Assess challenges and limitations of artificial intelligence in clinical practice. 

Step 5: Visualize & Communicate Concepts
ACPE: 0204-0000-23-843-H04-P & T
Activity Type: Application-based

  • Explain why visualization strategies matter in data communication.
  • Describe the guiding principles for creating effective data visualizations.
  • Assess various data visualization techniques for clarity of messaging.
  • Compare approaches to the communication of data to best meet the customer’s needs.
  • Identify best practices in data visualization for different analyses using Microsoft Excel.
  • Contrast benefits and weaknesses of different data visualization types using Microsoft Excel.
  • Explain capabilities within Tableau to load and connect data sources.
  • Apply data manipulation techniques within Tableau to facilitate visualization of pharmacy data.
  • Create an informative Tableau visual with pharmacy data.
  • Assess methods to improve user interaction with a Tableau dashboard featuring pharmacy data.
  • Choose a library for visualizing data with a Python-based communication approach.
  • Use the R function ggplot2 to organize and visualize data. 

Project: Putting It All Together
ACPE: 0204-0000-23-844-H04-P & T
Activity Type: Application-based

  • Apply skills and concepts from the Maturity Model for Data Literacy to a project.
  • Apply data analytics techniques in Microsoft Excel to effectively answer a case-based analytics scenario.
  • Apply data analytics techniques in Python to effectively answer a case-based analytics scenario.
  • Apply data analytics techniques in R to effectively answer a case-based analytics scenario.

Kent Bridgeman, PharmD, MHI
Informatics Pharmacist
Allina Health
Minneapolis, Minnesota 

Evan Draper, PharmD, BCPS
Medication Management Informaticist
Mayo Clinic
Rochester, Minnesota 

Dalton Fabian, PharmD
Data Scientist/Senior Analytics Consultant
Wellmark Blue Cross and Blue Shield
Des Moines, Iowa 

Jackie Ho, PharmD, MPH, BCPS
Data Analyst Pharmacist – Pharmacy Program Coordinator
Cedars-Sinai Medical Center
Los Angeles, California 

Ben Moore, PharmD, MS
Pharmaceutical Data Analytics Coordinator
PGY2 Pharmacy Informatics Residency Coordinator
St. Jude Children’s Research Hospital
Memphis, Tennessee 

Scott Nelson, PharmD, MS, FAMIA, ACHIP
Program Director, MS Applied Clinical Informatics (MS-ACI)
Associate Professor, Biomedical Informatics
Venderbilt University Medical Center
Nashville, Tennessee 

Nick Schutz, PharmD
Pharmacy Informatics Specialist
Allina Health
Minneapolis, Minnesota 

Hilary Teaford, PharmD, MHI, BCPS
Medication Management Informaticist
Mayo Clinic
Rochester, Minnesota 

Mary-Haston Vest, PharmD, MS, BCPS
System Director of Pharmacy
UNC Health
Associate Professor of Clinical Education
UNC Eshelman School of Pharmacy
Chapel Hill, North Carolina 

Andrew Webb, PharmD, BCCCP
Clinical Pharmacist, Neurocritical Care
Massachusetts General Hospital
Boston, Massachusetts 

In accordance with our accreditor’s Standards of Integrity and Independence in Accredited Continuing Education, ASHP requires that all individuals in control of content disclose all financial relationships with ineligible companies. An individual has a relevant financial relationship if they have had a financial relationship with ineligible company in any dollar amount in the past 24 months and the educational content that the individual controls is related to the business lines or products of the ineligible company.

An ineligible company is any entity producing, marketing, re-selling, or distributing health care goods or services consumed by, or used on, patients. The presence or absence of relevant financial relationships will be disclosed to the activity audience. 

  • Scott Nelson: Merative Micromedex, advisory counsel 

As defined by the Standards of Integrity and Independence definition of ineligible company. All relevant financial relationships have been mitigated prior to the CPE activity.

This online activity consists of a combined total of 9 learning modules. Pharmacists and Pharmacy Technicians are eligible to receive a total of 16.25 hours of continuing education credit by completing all 9 modules within this professional certificate. 

Participants must participate in the entire activity and complete the evaluation and all required components to claim continuing pharmacy education credit online at ASHP Learning Center. Follow the prompts to claim credit and view your statement of credit within 60 days of completing the activity.

Important Note – ACPE 60 Day Deadline: 

Per ACPE requirements, CPE credit must be claimed within 60 days of being earned. To verify that you have completed the required steps and to ensure your credits have been reported to CPE Monitor, check your NABP profile account to validate that your credits were transferred successfully before the ACPE 60-day deadline. After the 60-day deadline, ASHP will no longer be able to award credit for this activity.