Per participant
Applied Data Analysis Masterclass: Visualization, Statistics and Advanced Programs
Why Attend
Effective data analysis begins with accurate data collection and selection, which requires a solid understanding of various data types and their diverse sources. Properly structuring this data ensures seamless visualization across different chart types and enables the use of efficient descriptive statistical measures to summarize results.
This course focuses on the essentials of designing a robust data collection process, selecting optimal sampling techniques, validating data quality, and exploring visualization options alongside their corresponding descriptive statistical KPIs. Participants will also gain insight into advanced techniques and tools for comprehensive data analysis, laying the groundwork for a successful career in the field of data or as preparation for Machine Learning courses or programs.
This course is designed to provide participants with a clear understanding of data structuring for efficient analysis, scientific profiling of different groups through smart data examination, and practical experience with current technology tools available in the market.
This course is also offered in Virtual Learning, click on the course below.
Overview
Course Methodology
This course is highly practical and focuses on learning by doing, with participants using familiar data tools such as Excel and modern platforms like Python, Tableau, Power BI, Dataiku, and ChatGPT Data Analyst. The sessions combine short, focused explanations with real-world examples so that participants can clearly see how data analytics supports better decisions in their own organization. Throughout the program, participants work through structured exercises and small group activities that follow the full data analytics lifecycle: from asking the right question, to cleaning data, analyzing it, and telling a clear story with the results. Confidence is built step by step, using simple language, visual explanations, and plenty of time for questions and discussion.
Course Objectives
By the end of the course, participants will be able to:
- Explain the basics of data analytics and statistics in simple business language.
- Use tools such as Excel, Python, Tableau, Power BI, Dataiku, and ChatGPT Data Analyst to explore, summarize, and visualize data.
- Apply key statistical concepts (descriptive statistics, probability, sampling, confidence intervals, hypothesis testing, A/B testing) to real business questions.
- Clean and prepare data for analysis, including handling missing values, outliers, and inconsistent formats.
- Design and communicate clear, visual “data stories” that support decisions and actions in their organization.
Target Audience
This course is designed for professionals and managers from any department or industry who want to use data more confidently in their daily work. It is suitable for non-technical participants who may not have a background in statistics or programming but want to understand and apply modern data analytics tools in a simple, practical way. The program is especially relevant for individuals who work with reports, dashboards, spreadsheets, or performance indicators and want to move from “reading numbers” to “drawing insights”. It is also valuable for those involved in improvement, policy, strategy, or digital transformation who need a stronger grasp of data-driven decision making.
Target Competencies
- Data literacy and confidence with modern analytics tools
- Descriptive and inferential statistics for decision making
- Data preparation, quality checks, and exploratory analysis
- Data visualization, storytelling, and communication of insights
Course Outline
- Data Analytics Essentials & Modern Tools
- Understand what data analytics is, how it supports better decisions, and how it connects to AI and machine learning.
- Learn the typical data analytics lifecycle: ask the question, get the data, clean it, analyze it, and present the findings.
- Get an overview of modern tools such as Excel, Python, Tableau, Power BI, Dataiku, and ChatGPT Data Analyst and when to use each.
- See how data analytics is used in different functions (operations, finance, etc)
- Descriptive Statistics & Understanding Your Data
- Review types of data and variables, and the difference between descriptive and inferential statistics.
- Learn measures of central tendency (mean, median, mode) and when each is most appropriate.
- Understand measures of spread and position (range, variance, standard deviation, percentiles, quartiles) and what they say about risk and variability.
- Practice summarizing datasets into simple tables and visual summaries that highlight the main message.
- Probability, Sampling & Confidence
- Build an intuitive understanding of probability, including basic rules, multiple events, and conditional probability.
- Get a simple view of key probability distributions (binomial, Poisson, normal) and where they appear in real data.
- Understand the relationship between population and sample, and how sampling methods and bias affect conclusions.
- Learn the idea of the Central Limit Theorem and confidence intervals to estimate unknown values with a clear level of confidence.
- Hypothesis Testing, A/B Testing & Simple Comparisons
- Turn business questions into testable hypotheses and understand the roles of the null and alternative hypotheses.
- Learn how to compare groups using tests for means and proportions, and how to interpret p-values and statistical significance.
- Understand Type I and Type II errors, and the difference between one-tailed and two-tailed tests in simple terms.
- See how A/B testing and simple experimental design are used to compare versions of a process, message, or product.
- Data Cleaning, Exploration & Visual Storytelling
- Learn key steps in data cleaning: handling missing data, outliers, inconsistent dates, and categorical variables.
- Perform exploratory data analysis (EDA) to detect patterns, anomalies, and relationships, including correlation vs causation.
- Apply core principles of effective data visualization: choosing the right chart, simplifying design, and avoiding misleading visuals.
- Bring everything together in a small data project that turns raw data into insights.
Schedule & Fees
Virtual Learning
This course is also offered in Virtual Learning, click on the course below.
Course Contact
Contact me if you have any questions.
I speak English & Arabic!
