Associate Certified Analytics Professional aCAP

Overview:

Introduction:

This program is designed to prepare participants for the certification exam only.

This training program is designed to enhance participants' knowledge and skills in analytics. Through it, they will gain a deep understanding of analytical methods, tools, and best practices essential for effective data-driven decision-making.

Program Objectives:

By the end of this program, participants will be able to:

• Grasp the core concepts and principles of data analytics.

• Utilize various techniques to analyze data and generate insights.

• Create and validate data models to support decision-making.

• Effectively communicate findings and recommendations to stakeholders.

• Gain the knowledge and skills necessary to pass the aCAP certification exam.

Targeted Audience:

• Aspiring data analysts seeking certification.

• Professionals transitioning into analytics roles.

• Managers looking to enhance their data-driven decision-making skills.

• IT professionals interested in analytics.

Program Outlines:

Unit 1:

Data Analytics Fundamentals:

• Overview of data analytics and its importance in business.

• Understanding different data types and sources.

• Techniques for collecting and sourcing data.

• Ensuring data quality and governance.

• Overview of commonly used analytical tools and software.

Unit 2:

Statistical and Quantitative Analysis:

• Understanding measures of central tendency, variability, and distribution.

• Conducting hypothesis testing and confidence interval estimation.

• Applying linear and logistic regression techniques.

• Analyzing time series data for trends and seasonality.

• Utilizing probability distributions and building predictive models.

Unit 3:

Data Management and Preparation:

• Techniques for cleaning and preparing data for analysis.

• Methods for transforming and normalizing data.

• Combining data from different sources for comprehensive analysis.

• Techniques for exploring and visualizing data.

• Creating new features to improve model performance.

Unit 4:

Advanced Analytical Techniques:

• Introduction to machine learning concepts and algorithms.

• Techniques for classification and regression tasks.

• Methods for clustering and association analysis.

• Basics of NLP and text analytics.

• Techniques for evaluating and validating models.

Unit 5:

Communicating Analytical Results

•  Principles and best practices for effective data visualization.

•  Hands-on practice with tools like Tableau, Power BI, or Python..

•  Techniques for presenting data in a compelling narrative.

•  Creating insightful reports and dashboards.

•  Best practices for presenting findings and recommendations to diverse audiences.

• Prepare for the certifiation exam.

Note: This program is designed to prepare participants for the certification exam only.