Data Analyst

Why Attend

Catalyze impactful change in your organization with Meirc’s in-depth course on data analytics. Designed with a focus on practical application, this IIBA®-endorsed course aims to equip participants with a comprehensive understanding of the transformative role of data analytics in modern decision-making processes. Participants will learn to formulate data-driven questions that align with their organization’s objectives, identify suitable data sources, and analyze large datasets using advanced Excel techniques. In addition, participants in this course will acquire the skills needed to create compelling data visualizations that can be used to present data-driven insights.

Course Registration

Course Methodology This course uses Excel for analyzing complex datasets. Participants will collaborate on analyzing a real-world business case and will be expected to present their data-driven insights in a simulated board meeting. Course Objectives By the end of the course, participants will be able to:

Evaluate the role of data analytics in improving organizational decision making
Formulate data-driven analytical questions tied to business objectives
Identify suitable data sources and ensure the accuracy of collected information
Analyze large datasets using advanced Excel techniques
Create effective data visualizations and present data-driven insights to stakeholders

Target Audience Analysts and professionals who are looking to build their data analysis skills as well as those who are interested in improving their decision making capabilities based on data-driven insights Target Competencies

Analytical thinking
Sourcing data
Validating data
Structuring data
Analyzing data
Data visualization

Data Analytics and Organizational Decision Making

The objective of data analytics
The role of the data analyst
Data analytics as a set of activities
Data analytics as a decision-making paradigm
Data analytics as a set of practices and technologies

Framing the Data Analytics Approach

Identifying the real issue
Establishing key metrics
Understanding the problem
Using logic trees
Using the prioritization matrix

Sourcing Data

Data sources
Gathering quantitative data
Using qualitative data
Benchmarking
Conducting informational interviews
Validating data

Analyzing Data

Preprocessing data
Exploratory data analysis
Statistical data analysis
Prescriptive analysis
Predictive analysis
Introduction to machine learning

Communicating Data-Driven Results

Principles of data visualization
Creating compelling data visualizations
Understanding stakeholder needs
Presenting data-driven insights
Telling the story behind the data

Data Analyst

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