Requirements

• For a better learning experience
• we suggest you to use a laptop / mobile phone / pen and paper for taking notes
• highlighting important points
• and making summaries to reinforce your learning.

Description

Welcome to Program: Data Analyst: Professional Certificate in Data Analysis by MTF Institute

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 712 000 students.

Course Authors:

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.

Dr. Pedro Nunes has built a multifaceted career combining academia and practical business expertise. His educational journey culminated with a Doctorate in Economic Analysis and Business Strategy with a cum laude mention from the University of Santiago de Compostela. Professionally, he has navigated through various sectors, including technology, international commerce, and consultancy, with roles ranging from business analyst to director. Currently, Pedro serves as a Professor in several DBA programs, applying his extensive industry experience and academic insights to educate the next generation of professionals.

"Data Analyst: Professional Certificate in Data Analysis" course is structured to provide a comprehensive learning experience, starting with foundational data analysis skills and progressing to specialized applications and advanced techniques. Here's a breakdown of the key sections:

Section: Data Analysis (Core Foundational Skills)

• This section lays the groundwork for data analysis, covering essential concepts like data collection, cleaning, preparation, and exploratory data analysis (EDA).

• It delves into statistical analysis, data visualization, and predictive analytics.

• Crucially, it addresses data interpretation, reporting, privacy, and ethics, ensuring a well-rounded understanding.

• You'll also gain insights into the tools and software used in data analysis, portfolio building, and career development.

Section: Introduction to Business and Data Analysis (Supplementary Module)

• This module bridges the gap between data analysis and business application.

• It focuses on understanding business needs, defining problems, and applying data analysis techniques to solve business challenges.

• You'll explore data types and sources, business data analysis techniques, and data visualization for business insights.

• Case studies and discussions on the role of technology in business and data analysis are included.

Section: Hands-on Experience (Practical Application)

• This section emphasizes practical skills development through hands-on exercises using industry-standard tools.

• You'll gain experience with Excel, SQL, Python, R, and Tableau.

• Exercises focus on tasks like retrieving and analyzing data, handling missing data, conducting statistical analysis, and creating data visualizations.

Section: Data-Based Decision Making (Strategic Application)

• This section focuses on using data to inform decision-making processes.

• It covers various types of analytics (descriptive, diagnostic, predictive, and prescriptive) and how they contribute to data-driven decision-making.

• You'll learn about data-driven culture, tools and technologies, and real-world case studies.

Section: Advanced Microsoft Excel Usage (Specialized Skill)

• This module dives deep into advanced Excel functionalities, including advanced formulas, data analysis, visualization, data management, and automation with macros and VBA.

• The module also talks about AI powered excel with Gemini and Copilot.

• It includes numerous practical exercises to reinforce learning.

Section : SQL and SQL for Data Analysis (Database Skills)

• This section provides a comprehensive understanding of SQL, covering basic commands, data retrieval and manipulation, advanced queries, joins, subqueries, and data modification.

• It also covers query optimization, indexing, and advanced SQL features.

• The module concludes with a final project and assessment preparation.

Section: Specialization: Data Analysis in Marketing (Supplementary Module)

• This module is focused on the application of data analysis within a marketing context.

• It covers marketing data types, sources, key metrics, analytical models, segmentation, predictive analytics, A/B testing, campaign analysis, ethical considerations, and building a data-driven marketing culture.

Section: Specialization: Sales & Service Data Analysis & Analytics (Supplementary Module)

• This module is focused on the analysis of data within sales and service enviroments.

• It covers topics such as sales trends, pipeline analysis, conversion rate optimization, customer churn prediction, and using data to improve customer support efficiency.

• It also covers data sources, quality, and cleaning.

Section: Specialization: Data Quality, Management & Governance (Supplementary Module)

• This module focuses on the principles and practices of data governance and management.

• It covers data governance frameworks, roles, responsibilities, data quality management, and the use of data governance tools and technology.

Section: Data Based Decision Making and Cost-Benefit Analysis (Supplementary Module)

• This module combines data-based decision-making with cost-benefit analysis.

• It covers how to gather, analyze, and interpret data for decision-making, and how to conduct cost-benefit analyses to evaluate potential initiatives.

• It also covers the tools and technologies used for data-based decision-making.

Data analysis is the process of collecting, cleaning, and organizing data to uncover patterns, insights, and trends that can help individuals and organizations make informed decisions. It involves examining raw data to find answers to specific questions, identify potential problems, or discover opportunities for improvement.  

Data analysts transform raw data into actionable insights to help organisations improve operations, strategies, and customer experiences. Core skills include statistical analysis, critical thinking, data visualisation, and proficiency in tools like Excel, SQL, Python, and Tableau.

Learning data analysis skills is crucial for career building in today's data-driven world, both for professional positions and managers of all levels. Here's why:  

For Professionals:

Increased Employability: Data analysis skills are in high demand across various industries. Professionals with these skills are more likely to secure well-paying jobs and advance in their careers.  

Improved Decision-Making: Data analysis enables professionals to make informed decisions based on evidence and insights rather than relying on intuition or guesswork.  

Enhanced Problem-Solving: Data analysis helps professionals identify the root causes of problems, develop effective solutions, and track the effectiveness of interventions.  

Increased Efficiency and Productivity: By automating tasks and identifying areas for improvement, data analysis can help professionals work more efficiently and increase their productivity.  

For Managers:

Strategic Planning: Data analysis provides managers with the insights needed to develop effective strategies, set realistic goals, and track progress towards objectives.  

Performance Management: Managers can use data to monitor team performance, identify areas for improvement, and provide targeted feedback to employees.  

Risk Management: Data analysis can help managers identify potential risks, assess their impact, and develop mitigation strategies.  

Innovation and Growth: By analyzing data on customer behavior, market trends, and competitor activities, managers can identify opportunities for innovation and growth.  

Learning data analysis skills is essential for professionals and managers of all levels who want to succeed in today's data-driven world. These skills can help individuals make better decisions, solve problems more effectively, and contribute to the success of their organizations.

Who this course is for:

• No special requirements. A course for anyone who wants to build career in business and Data Analysis
• "Data analysis also is the process of collecting
• cleaning
• and organizing data to uncover patterns
• insights
• and trends that can help individuals and organizations make informed decisions. It involves examining raw data to find answers to specific questions
• identify potential problems
• or discover opportunities for improvement. \xa0 Data analysts transform raw data into actionable insights to help organisations improve operations
• strategies
• and customer experiences. Core skills include statistical analysis
• critical thinking
• data visualisation
• and proficiency in tools like Excel
• SQL
• Python
• and Tableau. Learning data analysis skills is crucial for career building in todays data-driven world
• both for professional positions and managers of all levels."
• For Professionals: Increased Employability: Data analysis skills are in high demand across various industries. Professionals with these skills are more likely to secure well-paying jobs and advance in their careers. \xa0 Improved Decision-Making: Data analysis enables professionals to make informed decisions based on evidence and insights rather than relying on intuition or guesswork. \xa0 Enhanced Problem-Solving: Data analysis helps professionals identify the root causes of problems
• develop effective solutions
• and track the effectiveness of interventions. \xa0 Increased Efficiency and Productivity: By automating tasks and identifying areas for improvement
• data analysis can help professionals work more efficiently and increase their productivity.
• For Managers: Strategic Planning: Data analysis provides managers with the insights needed to develop effective strategies
• set realistic goals
• and track progress towards objectives. \xa0 Performance Management: Managers can use data to monitor team performance
• identify areas for improvement
• and provide targeted feedback to employees. \xa0 Risk Management: Data analysis can help managers identify potential risks
• assess their impact
• and develop mitigation strategies. \xa0 Innovation and Growth: By analyzing data on customer behavior
• market trends
• and competitor activities
• managers can identify opportunities for innovation and growth.
• "Learning data analysis skills is essential for professionals and managers of all levels who want to succeed in todays data-driven world. These skills can help individuals make better decisions
• solve problems more effectively
• and contribute to the success of their organizations."
• "Section: Data Analysis (Core Foundational Skills) This section lays the groundwork for data analysis
• covering essential concepts like data collection
• cleaning
• preparation
• and exploratory data analysis (EDA). It delves into statistical analysis
• data visualization
• and predictive analytics. Crucially
• it addresses data interpretation
• reporting
• privacy
• and ethics
• ensuring a well-rounded understanding. Youll also gain insights into the tools and software used in data analysis
• portfolio building
• and career development."
• "Section: Introduction to Business and Data Analysis (Supplementary Module) This module bridges the gap between data analysis and business application. It focuses on understanding business needs
• defining problems
• and applying data analysis techniques to solve business challenges. Youll explore data types and sources
• business data analysis techniques
• and data visualization for business insights. Case studies and discussions on the role of technology in business and data analysis are included."
• "Section: Hands-on Experience (Practical Application) This section emphasizes practical skills development through hands-on exercises using industry-standard tools. Youll gain experience with Excel
• SQL
• Python
• R
• and Tableau. Exercises focus on tasks like retrieving and analyzing data
• handling missing data
• conducting statistical analysis
• and creating data visualizations."
• "Section: Data-Based Decision Making (Strategic Application) This section focuses on using data to inform decision-making processes. It covers various types of analytics (descriptive
• diagnostic
• predictive
• and prescriptive) and how they contribute to data-driven decision-making. Youll learn about data-driven culture
• tools and technologies
• and real-world case studies."
• Section: Advanced Microsoft Excel Usage (Specialized Skill) This module dives deep into advanced Excel functionalities
• including advanced formulas
• data analysis
• visualization
• data management
• and automation with macros and VBA. The module also talks about AI powered excel with Gemini and Copilot. It includes numerous practical exercises to reinforce learning.
• Section : SQL and SQL for Data Analysis (Database Skills) This section provides a comprehensive understanding of SQL
• covering basic commands
• data retrieval and manipulation
• advanced queries
• joins
• subqueries
• and data modification. It also covers query optimization
• indexing
• and advanced SQL features. The module concludes with a final project and assessment preparation.
• Section: Specialization: Data Analysis in Marketing (Supplementary Module) This module is focused on the application of data analysis within a marketing context. It covers marketing data types
• sources
• key metrics
• analytical models
• segmentation
• predictive analytics
• A/B testing
• campaign analysis
• ethical considerations
• and building a data-driven marketing culture.
• Section: Specialization: Sales & Service Data Analysis & Analytics (Supplementary Module) This module is focused on the analysis of data within sales and service enviroments. It covers topics such as sales trends
• pipeline analysis
• conversion rate optimization
• customer churn prediction
• and using data to improve customer support efficiency. It also covers data sources
• quality
• and cleaning.
• Section: Specialization: Data Quality
• Management & Governance (Supplementary Module) This module focuses on the principles and practices of data governance and management. It covers data governance frameworks
• roles
• responsibilities
• data quality management
• and the use of data governance tools and technology.
• Section: Data Based Decision Making and Cost-Benefit Analysis (Supplementary Module) This module combines data-based decision-making with cost-benefit analysis. It covers how to gather
• analyze
• and interpret data for decision-making
• and how to conduct cost-benefit analyses to evaluate potential initiatives. It also covers the tools and technologies used for data-based decision-making.