Good data selection and/or collection are the foundation for effective data analysis. This demands a thorough understanding of all data kinds and the various sources that they come from. Additionally, by arranging it correctly, all of the data may be easily shown under various charts, and they can all be described using appropriate and effective descriptive statistics measures.
The foundational concepts of this course include developing a clever data collection procedure, choosing the optimum sampling strategy, confirming the accuracy of the data stored for analysis, and comprehending all the choices for visualization and the related descriptive statistical KPIs. Prior to beginning any work or even a career in the realm of data, this course addresses all approaches and instruments for thorough data analysis. The course also acts as a prerequisite for any course or program in machine learning.
Additionally, this course aims to give participants a clear and comprehensive understanding of data structuring for effective data analysis, scientific group profiling through intelligent and effective data analysis, and appropriate data manipulation using a variety of tools currently available on the market.
Understand and organize the phases of a successful data analysis project.
Convert any industry into a thorough database.
Analyze and assess the quality of the data.
Basic data interpretation and description using full descriptive statistics
Discover the full history of data analysis.
All practitioners of machine learning and artificial intelligence (AI) start with applied data analysis. It is fundamental information that applies to all fields and data-related jobs.
The different types of Data
Data sources
Data
Variables
Data visualization
Pies, Doughnuts, Bars
Histograms, Lines, Scatter plots
Heat maps and Tuckey boxes
Geographical maps
Central tendency measurements
Average
Median
Mode
Scatter tendency measurements
Quartile
Variance
Standard deviation
Estimations
Punctual
Confidence Interval
Two men test
Equal variances (t-test)
Unequal variances (t-test – Welch correction)
Two variance tests (F-Test)
Two proportion test (Chi-Square test)
Two distribution tests (Chi-Square test)
Attraction – Repulsion Matrix
Vertical and horizontal profiling
Multiple mean tests
Equal variances (F-Test and ANOVA Table)
Unequal variances (F-Test – Welch Correction)
Multiple Variance test
Levene test
Chi-Square test
Multiple proportion test (Chi-Square test)
Multiple distribution test (Chi-Square test)
Attraction – Repulsion Matrix
Vertical and horizontal profiling
Mean pair comparisons methods:
General
Bonferroni
Tukey - Kramer
Simple linear regression
Line equation
Testing the regression line validity (t-nullity test)
R vs. R Square interpretation
ANOVA table analysis
Simple logistic regression
Probabilistic model
Testing the model validity (Chi-Square test)
Predicting classification
Odds ratio interpretation
Data analysis project best practices
Ask
Design
Preview
Analyze
Communicate
Sampling methods
Random and systematic
Multilevel, stratified, and cluster
Convenient, quota, and judgmental
PMP for research projects overview
Integration, cost, scope, time, cost, quality, communication
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