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Welcome to course: Professional Certificate: Operations and Process Management Data Analysis & Analytics by MTF Institute

This course equips participants with the essential skills to transform operational data into actionable insights. It emphasizes the strategic role of data in modern operations, guiding learners through the process of identifying, cleaning, and analyzing crucial data sources. Participants will master a range of techniques, from basic statistical analysis and KPI development to advanced predictive modeling and machine learning applications, enabling them to optimize processes, forecast performance, and drive data-driven decision-making.

Through practical exercises and real-world applications, students will learn to utilize industry-standard tools and software to visualize and interpret operational data. The curriculum covers vital methodologies like root cause analysis, process mapping, and variance analysis, empowering them to identify inefficiencies and implement targeted improvements. Furthermore, the course delves into advanced topics such as scenario planning, linear programming for resource allocation, and statistical process control, preparing participants to develop robust data-driven strategies and effectively manage change within operational environments.

Course provided by MTF Institute of Management, Technology and Finance

MTF is the global educational and research institute with HQ at Lisbon, Portugal, focused on business & professional hybrid (on-campus and online) education at areas: Business & Administration, Science & Technology, Banking & Finance.

MTF R&D center focused on research activities at areas: Artificial Intelligence, Machine Learning, Data Science, Big Data, WEB3, Blockchain, Cryptocurrency & Digital Assets, Metaverses, Digital Transformation, Fintech, Electronic Commerce, Internet of Things.

MTF is the official partner of: IBM, Intel, Microsoft, member of the Portuguese Chamber of Commerce and Industry.

MTF is present in 216 countries and has been chosen by more than 740 000 students.

Course Author:

Dr. Alex Amoroso is a seasoned professional with a rich background in academia and industry, specializing in research methodologies, strategy formulation, and product development. With a Doctorate Degree from the School of Social Sciences and Politics in Lisbon, Portugal, where she was awarded distinction and honour for her exemplary research, Alex Amoroso brings a wealth of knowledge and expertise to the table.

In addition to her doctoral studies, Ms. Amoroso has served as an invited teacher, delivering courses on to wide range of students from undergraduate level to business students of professional and executives courses. Currently, at EIMT in Zurich, Switzerland, she lectures for doctoral students, offering advanced instruction in research design and methodologies, and in MTF Institute Ms. Amoroso is leading Product Development academical domain.

In synergy between academical and business experience, Ms. Amoroso achieved high results in business career, leading R&D activities, product development, strategic development, market analysis activities in wide range of companies. She implemented the best market practices in industries from Banking and Finance, to PropTech, Consulting and Research, and Innovative Startups.

Alex Amoroso's extensive scientific production includes numerous published articles in reputable journals, as well as oral presentations and posters at international conferences. Her research findings have been presented at esteemed institutions such as the School of Political and Social Sciences and the Stressed Out Conference at UCL, among others.

With a passion for interdisciplinary collaboration and a commitment to driving positive change, Alex Amoroso is dedicated to empowering learners and professionals for usage of cutting edge methodologies for achieving of excellence in global business world.

Operations and Process Management - Data Analysis & Analytics

This program provides a comprehensive journey into the world of data analysis and its application within operations and process management. It equips participants with the theoretical knowledge and practical skills necessary to extract valuable insights from data, drive informed decision-making, and optimize operational efficiency. The curriculum is structured into four key sections: Data Analysis, Hands-on Experience, and Data Analysis in Operations.

Section 2: Data Analysis (Lectures 3-17)

This section lays the foundation for data analysis by introducing fundamental concepts and methodologies. Participants will gain a thorough understanding of the data analysis lifecycle, from data acquisition to interpretation and reporting. Key topics include:

• Introduction to Data Analysis: Defining data analysis, its importance, and its role in various industries.

• Data Collection and Acquisition: Exploring various data sources and methods for collecting relevant data.

• Data Cleaning and Preparation: Learning techniques for handling missing values, outliers, and inconsistencies to ensure data quality.

• Exploratory Data Analysis (EDA): Discovering patterns, trends, and relationships in data through summary statistics and visualizations.

• Statistical Analysis: Applying statistical methods to test hypotheses, draw inferences, and quantify uncertainty.

• Data Visualization: Creating effective visualizations to communicate insights and facilitate understanding.

• Predictive Analytics: Building models to forecast future outcomes and make predictions.

• Data Interpretation and Reporting: Translating analysis results into actionable insights and communicating them effectively.

• Data Privacy and Ethics: Understanding ethical considerations and best practices for handling sensitive data.

• Tools and Software for Data Analysis: Introduction to popular tools and software used in data analysis.

• Building a Data Analyst Portfolio: Strategies for showcasing skills and experience to potential employers.

• Career Development and Job Market Trends: Exploring career paths and industry trends in data analysis.

• Practical Exercises: Reinforcing theoretical concepts with hands-on exercises.

• Next Steps: Guidance on continued learning and career advancement.

Section 3: Hands-on Experience (Lectures 18-29)

This section provides practical experience with industry-standard tools and technologies used in data analysis. Participants will apply their knowledge to real-world scenarios and develop proficiency in:

• Excel: Performing data manipulation, analysis, and visualization using Excel.

• SQL: Retrieving and analyzing data from relational databases using SQL queries.

• Python: Utilizing Python libraries such as Pandas and NumPy for data analysis and manipulation.

• R: Conducting statistical analysis and creating visualizations with the R programming language.

• Tableau: Creating interactive dashboards and visualizations for data exploration and communication.

• Practical Tasks and Exercises: Hands-on exercises utilizing the above mentioned tools to analyze and visualize real world data.

• Next Steps: guidance on how to continue to develop skills with these tools.

Section 4: Data Analysis in Operations (Lectures 30-58)

This section focuses on the application of data analysis techniques to optimize operational processes and improve decision-making. Participants will learn how to:

• Understand the Role of Data in Modern Operations: Exploring the importance of data-driven decision-making in operations management.

• Apply Foundational Concepts of Data Analysis: Utilizing data analysis principles to address operational challenges.

• Utilize Tools and Software for Operational Data Analysis: Selecting and applying appropriate tools for specific operational tasks.

• Identify and Utilize Data Sources in Operations: Recognizing relevant data sources within operational systems.

• Clean and Preprocess Operational Data: Ensuring data quality for accurate analysis.

• Integrate and Transform Operational Data: Combining data from multiple sources for comprehensive analysis.

• Employ Data Collection Methods: Choosing appropriate data collection techniques for operational data.

• Create Derived Metrics and KPIs: Developing key performance indicators to measure operational performance.

• Visualize Operational Data: Creating effective visualizations to communicate operational insights.

• Analyze Key Performance Indicators (KPIs): Monitoring and analyzing KPIs to identify areas for improvement.

• Apply Basic Statistical Analysis for Operations: Using statistical methods to analyze operational data.

• Conduct Root Cause Analysis: Identifying the underlying causes of operational problems.

• Perform Process Analysis and Improvement: Optimizing operational processes through data analysis.

• Conduct Variance Analysis: Analyzing deviations from planned performance.

• Utilize Flowcharts and Process Mapping: Visualizing and analyzing operational processes.

• Apply Forecasting Techniques: Predicting future operational performance.

• Develop Predictive Models in Operations: Building models to forecast demand, predict equipment failures, and optimize resource allocation.

• Conduct Scenario Planning and Simulation: Evaluating the impact of different operational scenarios.

• Apply Regression Analysis for Forecasting: Using regression models to predict future values.

• Implement Statistical Process Control (SPC): Monitoring and controlling process variability.

• Utilize Optimization Techniques: Finding optimal solutions for resource allocation and scheduling.

• Apply Machine Learning in Operations: Utilizing machine learning algorithms for tasks such as predictive maintenance and quality control.

• Apply Linear Programming for Resource Allocation: Optimizing resource allocation through linear programming techniques.

• Develop Data-Driven Strategies: Formulating operational strategies based on data insights.

• Manage Change and Communication: Effectively communicating data-driven insights and facilitating change management.

  
  

  
  

The Role of Data in Modern Operations

The Power of Data in Operations:

• Data-driven decisions: Key to operational success.

• Enhance efficiency, reduce costs, boost quality.

• Gain competitive advantage through insights.

Key Operational Areas for Data Analysis:

• Supply Chain: Optimise logistics, reduce delays.

• Production: Improve output, minimise waste.

• Service: Enhance customer satisfaction, predict demand.

• Inventory Management: reduce stock-outs, and overstocking.

Operations and Process Management Data Analysis & Analytics involves using data and analytical techniques to optimize and improve the efficiency and effectiveness of an organization's operational processes. Here's a breakdown:

What it is:

• Operations Management:

  • Focuses on planning, organizing, and supervising production, manufacturing, or service delivery.

  • Aims to maximize efficiency and minimize costs while maintaining quality.

• Process Management:

  • Involves designing, controlling, and improving business processes.

  • Seeks to streamline workflows and eliminate bottlenecks.

• Data Analysis & Analytics:

  • Utilizes statistical methods, data mining, and machine learning to extract meaningful insights from data.

  • Enables data-driven decision-making.

  • In the context of operation and process management, this invloves analyzing data related to production, supply chains, customer service, and other operational areas.

  • Key types of analytics used are:

    • Descriptive Analytics: Understanding what has happened.

    • Predictive Analytics: Forecasting future trends.

    • Prescriptive Analytics: Recommending optimal actions.

Importance for Companies:

• Improved Efficiency:

  • Data analysis can identify inefficiencies and bottlenecks in processes, leading to streamlined operations.

• Cost Reduction:

  • By optimizing resource allocation and reducing waste, companies can significantly lower operational costs.

• Enhanced Decision-Making:

  • Data-driven insights enable managers to make informed decisions, leading to better outcomes.

• Increased Productivity:

  • Optimized processes and resource allocation result in higher productivity and output.

• Better Quality Control:

  • Data analysis helps monitor quality in real-time, allowing for proactive identification and resolution of issues.

• Supply Chain Optimization:

  • Data analytics assists with inventory management, demand forecasting, and logistics, leading to a more efficient supply chain.

Importance for Managers' Careers:

• Enhanced Skills:

  • Developing data analysis and analytics skills makes managers more valuable assets to their organizations.

• Career Advancement:

  • Proficiency in these areas opens up opportunities for career advancement into higher-level management roles.

• Increased Efficacy:

  • Managers with these skills are able to make better informed decisions, leading to better results, and therefore increased efficacy in their roles.

• Competitive Advantage:

  • In today's data-driven business world, these skills provide a significant competitive advantage.

• Future-Proofing:

  • As businesses increasingly rely on data, these skills are essential for staying relevant and successful in the long term.

In essence, Operations and Process Management Data Analysis & Analytics empowers companies and managers to make smarter, more efficient decisions, driving success in an increasingly competitive business landscape.