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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The term ‘strategic management’ is used to denote a branch of management that is concerned with the development of strategic vision, setting out objectives, formulating and implementing strategies, and introducing corrective measures for the deviations (if any) to reach the organization’s strategic intent.
Therefore, this Strategic Management Course focuses on delivering a high-level practical understanding of the complete elements of the subject – analysis, planning, implementation, review, development.
Achieving outstanding sales results in an increasingly competitive world is a difficult task. Only by establishing a modern sales force management system and by training sales management personnel effectively, can today’s firm compete. The Certified Sales Manager course provides frontline sales managers with the knowledge, skills, and tools they need to drive bottom-line performance. It focuses on improving organization and forecasting skills, as well as other technical competencies aimed at guiding salespeople towards higher performance.
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A greater grasp of IT governance and management is the goal of this COBIT 2019 Foundation course. COBIT is a comprehensive library of information, tools, models, and resources that enable business executives to achieve their goals for effective governance. Best practices for corporate information and technology governance and management can be found in COBIT 2019 FOUNDATION, the most recent version of the industry-leading framework for these activities.
The economic analysis is a scientific method of research and a logical method of economic study, through which the factors influencing the behavior of economic phenomena can be explained. And it provides us with the different logical tools that are used to derive the different economic theories. If the theory represents the conclusion, the analysis represents the research platform.
There are different classifications of the types of economic analysis On different criteria related to the size of the economic unit covered by the analysis and the degree of comprehension desired by the analyst and can be classified economic analysis: to partial analysis, and comprehensive analysis.