The Scandinavian Academy adopts modern methods of training and development and raising the efficiency of human resource development, and we follow in that:
Theoretical lectures through PowerPoint presentations and visual presentations (videos - and short films)
Scientific evaluation of the trainee (pre-evaluation and post-evaluation)
Brainstorming and application and representation of roles
Practical cases that suit the scientific material and the nature of the trainees' work
Tests at the end of the course
The participant gets the scientific and practical material for the training program printed on paper and saved on CD or flash memory
Attendance reports for participants and final results reports with a general evaluation of the program
The scientific material for the training program is prepared in a scientific and professional manner, and this is done by the best professors and trainers in various fields and all specialties
At the end of the program, the trainee is awarded a professional attendance certificate (signed and certified) issued by the Scandinavian Academy for Training and Development in the Kingdom of Sweden (with the possibility of attestation from the Swedish Ministry of Foreign Affairs and the trainee's embassy in the Kingdom of Sweden)
Training program times are from ten o'clock in the morning until two o'clock in the afternoon, and the program includes a snack buffet during the lectures
Why Choose this Course?
This interactive, applications-driven 5-day 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. The 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.
This course will feature:
Discussions on applications of data analytics in management
The importance of data in data analytics
Applying data analytical methods through worked examples
Focusing on management interpretation of statistical evidence
How to integrate statistical thinking into the work domain
What are the Goals?
By the end of this course, 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.
Who is this Course for?
This course is suitable to a wide range of professionals but will greatly benefit:
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
How will this be Presented?
This course will utilise a variety of proven adult learning techniques to ensure maximum understanding, comprehension and retention of the information presented. The daily workshops will be highly interactive and participative. This involves regular discussion of applications as well as hands-on exposure to data analytics techniques using Microsoft Excel. Delegates are strongly encouraged to bring and analyse data from their own work domain. This adds greater relevancy to the content. Emphasis is also placed on the valid interpretation of statistical evidence in a management context.
The Course Content
Setting the Statistical Scene in Management
Introduction; 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
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
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
Statistical Decision Making – Drawing Inferences from Hypotheses Testing
Thate rionale 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
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
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