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
Each statistical tool or methodology used during the course is supported by its own case study with step-by-step outputs that go in parallel with multi stage analysis.
In addition to group discussions, all analysis tools are detailed and demonstrated with sequential screen shot applications on comparative technologies (EXCEL – STATISTICA and SAS – R and Python).
Course Objectives
By the end of the course, participants will be able to:
- Plan and manage the lifecycle of a successful data analysis project
- Translate business challenges into comprehensive databases
- Assess and improve data quality for analysis and reporting
- Summarize and interpret data using descriptive statistics
- Uncover the complete narrative behind data analysis
Target Audience
Applied Data Analysis is the foundation for all Machine Learning and Artificial Intelligence (AI) practitioners. It is prerequisite knowledge that is applicable in all industries and data related functions.
Target Competencies
- Project Design
- Findings Visualization
- Data Analysis
- Problem Solving using analytical tools
Course Outline
- Data visualization and descriptive statistics
- Understanding Data: Types, sources, variables
- Visualization Techniques:
- Pie and doughnut charts
- Bar charts, histograms, line graphs, and scatter plots
- Heat maps and Tukey box plots
- Geographical maps
- Central Tendency Measurements:
- Mean, median, and mode
- Scatter Measurements:
- Quartiles, variance, and standard deviation
- Estimation Methods:
- Point estimation
- Confidence intervals
- Comparing two groups
- Two Mean Test:
- Equal variances (t-test)
- Unequal variances (t-test with Welch correction)
- Two Variance Test (F-test)
- Two Proportion and Distribution Tests (Chi-Square)
- Attraction-Repulsion Matrix
- Vertical and horizontal profiling
- Two Mean Test:
- Comparing multiple groups
- Multiple Mean Tests:
- Equal variances (F-test and ANOVA)
- Unequal variances (F-test with Welch correction)
- Multiple Variance Test (Levene test)
- Proportion and Distribution Tests (Chi-Square)
- Advanced Profiling Techniques:
- Attraction-Repulsion Matrix
- Vertical and horizontal profiling
- Pairwise Mean Comparison Methods:
- General comparisons
- Bonferroni and Tukey-Kramer adjustments
- Multiple Mean Tests:
- Simple regressions
- Simple Linear Regression:
- Line equation and validity testing (t-test)
- R and R² interpretation
- ANOVA table analysis
- Simple Logistic Regression:
- Probabilistic models and validity testing (Chi-Square)
- Classification predictions and odds ratio interpretation
- Simple Linear Regression:
- Data analysis project best practices
- Project Lifecycle:
- Asking questions
- Designing the study
- Previewing data
- Analyzing results
- Communicating findings
- Sampling Methods:
- Random and systematic sampling
- Multilevel, stratified, and cluster sampling
- Convenience, quota, and judgmental sampling
- PMP Overview for Research Projects:
- Integration, cost, and scope management
- Time, quality, and communication strategies
- Managing risks, procurement, and stakeholder engagement
- Project Lifecycle:
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!
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