PCAD™ – Certified Associate in Data Analytics with Python

PCAD™ – Certified Associate in Data Analytics with Python certification validates that the individual demonstrates proficiency in Python data acquisition, cleaning, manipulation, modeling, analysis, and visualization techniques. The credential confirms the holder’s expertise in the field of data analytics, such as decision-making under uncertainty, data-based decision-making, predictive modelling, and model selection, as well as measures their skills in using Python for file processing and performing programming operations with the use of the NumPy, Pandas, Matplotlib, Seaborn, and SciKit-Learn libraries.

PCAD™ certification gives its holders confidence in their data analytics and programming skills, helps them stand out in the job market, and gives them a head start on preparing for and advancing to the professional level.

PCAD™ Certification Exam Information :

Exam name : PCAD™ – Certified Associate in Data Analytics with Python

Exam Code & Current Exam Versions : PCAD-31-0x (Status: Coming Q1 2023)

Pre-requisites : None

Validity : 5 years (unless retaken or PCPD is earned)

Exam Duration : Exam: 65 minutes, NDA/Tutorial: 10 minutes

Number of Questions : 40

Format : Single- and multiple-select questions | Python 3.x

Passing Score : 70%

Languages : English

Cost :

USD 295 (Exam)
USD 319 (Exam + Practice Test)
USD 49 (Practice Test)

PCAD™ – Certified Associate in Data Analytics with Python certification (Exam PCAD-31-0x) is a professional, high-stakes credential that measures the candidate’s ability to perform Python coding tasks related to mining, importing, cleaning, and preparing data for analysis, manipulating data and performing statistical analyseson them, summarizing and visualizing datasets, using simple machine learning algorithms to perform predictive data modeling, and building Python solutions for data science purposes.

The PCAD™ certification shows that the individual is familiar with the following concepts: basic concepts, methodologies, and best practices in data analytics, data extraction and mining, file processing (csv, html, json, data analytics with NumPy and Pandas, data visualization with Matplotlib and Seaborn, machine learning and data modeling with SciKit-Learn.

Who this course is for:

• Individuals looking to acquire the skills and knowledge essential to continue to more advanced, more specialized, and higher paying Software Development, Security, Networking, IoT, and engineering roles