DATA ANALYTICS FOR THE MANAGEMENT DECISION-MAKING

TRAINING OVERVIEW

In the words of renowned statistician W. Edwards Deming, “In God we trust; all others bring data.” Indeed, effective decision-making hinges on the ability to harness the power of data analytics. DixonTech's Data Analytics for the Management Decision-Making training course is designed to empower managers with the necessary skills to make informed and strategic decisions. This comprehensive program emphasizes the invaluable role of data analytics in guiding policy formulation, supporting operational decision-making, and driving strategic initiatives. Through a management lens, participants will gain insights into the transformative potential of data analytics and its application in real-world scenarios. The course will delve into the interpretation of data analytics findings, ensuring participants are equipped to derive actionable insights and drive organizational success. Join us and unlock the power of data analytics to elevate your decision-making prowess and drive tangible results for your organization.

TRAINING TOPICS

• Setting the statistical scene in management
• Evidence-based observational decision-making
• Statistical decision-making – drawing inferences from sample data
• Statistical decision-making – drawing inferences from hypotheses testing
• Predictive decision-making - statistical modelling and data mining

By the end of this training course, delegates will be able to

• Gain a profound understanding of the potency of data analytics as a pivotal decision support tool in management.
• Develop the ability to critically evaluate the validity of statistically generated business information, ensuring informed decision-making.
• Acquire knowledge of various data analytic tools and discern their appropriate application in diverse management scenarios.
• Learn to interpret statistical evidence meaningfully and validly within a management context, facilitating evidence-based decision-making.
• Enhance communication skills to engage intelligently with data analytic professionals, fostering collaboration and synergy in leveraging data analytics for organizational success.

TRAINING IS TAILORED TO

• HR managers
• Marketing managers
• Operations and logistics managers
• Financial managers
• Policy support personnel
• Engineers / Technical Specialists

TRAINING METHODOLOGY

DixonTech's Data Analytics for the Management Decision-Making training adopts an immersive and interactive methodology tailored to meet the diverse needs of participants. Through a blend of theoretical learning, practical exercises, case studies, and group discussions, participants will engage deeply with the principles and applications of data analytics in the context of managerial decision-making. Our approach emphasizes hands-on learning, enabling participants to apply data analytics tools and techniques to real-world scenarios relevant to their roles and industries. Additionally, expert-led sessions and personalized feedback ensure that participants gain a comprehensive understanding of statistical concepts and their practical implications for management decision-making. By fostering collaboration and experiential learning, our training methodology equips participants with the skills and confidence to leverage data analytics effectively to drive strategic and informed decision-making within their organizations.

DAY ONE:

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

DAY TWO:

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            

DAY THREE:

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

DAY FOUR:

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

DAY FIVE:

PREDICTIVE DECISION-MAKING - STATISTICAL MODELLING 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 &ndash