Applied Data Analysis Masterclass: Visualization, Statistics and Advanced Programs
5 DaysApplied Data Analysis Masterclass: Visualization, Statistics and Advanced Programs - Virtual Learning
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:
Mawred Certificate
Mawred Certificate courses by Al mawred Training & Consulting are designed for those willing to challenge themselves and go the extra distance...
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