Cambridge
Academy
In London

Course Duration: Five Training Days

Course Language: Arabic or English

Include:

-Scientific material with TAB

-Workshops

-Reception and farewell at the airport

Introduction:

This course will highlight the added value that data analytics can offer a professional as a decision support tool in management decision making. It will show the use of data analytics to support strategic initiatives; to inform on policy information; and to direct operational decision making. This course will emphasize applications of data analytics in management practice; focus on the valid interpretation of data analytics findings; and create a clearer understanding of how to integrate quantitative reasoning into management decision making. Exposure to the discipline of data analytics will ultimately promote greater confidence in the use of evidence-based information to support management decision making

Targeted Groups:

-Professionals in management support roles

-Analysts who typically encounter data / analytical information regularly in their work environment

-Those who seek to derive greater decision making value from data analytics

Course Objectives:

At the end of this course the participants will be able to:

-Appreciate data analytics in a decision support role

-Explain the scope and structure of data analytics

-Apply a cross-section of useful data analytics

-Interpret meaningfully and critically assess statistical evidence

-Identify relevant applications of data analytics in practice

Targeted Competencies:

-Applications of data analytics in management

-Data analytics

-Applying data analytical methods through worked examples

-Focusing on management interpretation of statistical evidence

-Integrating the statistical thinking into the work domain

Course Content:

Unit 1: Setting the Statistical Scene in Management:

-The quantitative landscape in management

-Thinking statistically about applications in management (identifying KPIs)

-The integrative elements of data analytics

-Data: The raw material of data analytics (types, quality and data preparation)

-Exploratory data analysis using excel (pivot tables)

-Using summary tables and visual displays to profile sample data

Unit 2: Evidence-Based Observational Decision Making:

-Numeric descriptors to profile numeric sample data

-Central and non-central location measures

-Quantifying dispersion in sample data

-Examine the distribution of numeric measures (skewness and bimodal)

-Exploring relationships between numeric descriptors

-Breakdown analysis of numeric measures

Unit 3: Statistical Decision Making – Drawing Inferences from Sample Data:

-The foundations of statistical inference

-Quantifying uncertainty in data – the normal probability distribution

-The importance of sampling in inferential analysis

-Sampling methods (random-based sampling techniques)

-Understanding the sampling distribution concept

-Confidence interval estimation

Unit 4: Statistical Decision Making – Drawing Inferences from Hypotheses Testing:

-The rationale of hypotheses testing

-The hypothesis testing process and types of errors

-Single population tests (tests for a single mean)

-Two independent population tests of means

-Matched pairs test scenarios

-Comparing means across multiple populations

Unit 5: Predictive Decision Making - Statistical Modeling and Data Mining:

-Exploiting statistical relationships to build prediction-based models

-Model building using regression analysis

-Model building process – the rationale and evaluation of regression models

-Data mining overview – its evolution

-Descriptive data mining – applications in management

-Predictive (goal-directed) data mining – management applications