Data Analyst: Professional Certificate in Data Analysis

Data Based Decision Making, Data Analysis. Data Collection, Cleaning, Statistical Analysis, Visualisation, Privacy.

Note: Course Free for limited time and limited users(500 limit) so Enroll ASAP. If not free it means you are late. Scroll Down and click Enroll Now button to get enrolled

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What you’ll learn

Business Analysis
Data Analysis
Microsoft Excel
Data Science
Data Collection and Acquisition
Data Cleaning and Preparation
Statistical Analysis
Data Interpretation and Reporting
Data Visualisation
Data Privacy and Ethics
Tools and Software for Data Analysis
Career Development and Job Market Trends
Data Analysis (Core Foundational Skills)
Introduction to Business and Data Analysis (Supplementary Module)
Hands-on Experience (Practical Application)
Data-Based Decision Making (Strategic Application)
Advanced Microsoft Excel Usage (Specialized Skill)
SQL and SQL for Data Analysis (Database Skills)
Specialization: Data Analysis in Marketing (Supplementary Module)
Specialization: Sales & Service Data Analysis & Analytics (Supplementary Module)
Specialization: Data Quality, Management & Governance (Supplementary Module)
Data Based Decision Making and Cost-Benefit Analysis (Supplementary Module)

Explore related topics

This course includes:

Role Play
22 hours on-demand video
156 articles
89 downloadable resources
Access on mobile and TV
Full lifetime access
Certificate of completion

Course content

18 sections • 312 lectures • 30h 3m total length

Presentation
Welcome to MTF

Course Slide-deck
A Guide for Data Analysts
Introduction
Data Collection and Acquisition
Data Cleaning and Preparation
Exploratory Data Analysis (EDA)
Statistical Analysis
Data Visualisation
Predictive Analytics
Data Interpretation and Reporting
Data Privacy and Ethics
Tools and Software for Data Analysis
Building a Data Analyst Portfolio
Career Development and Job Market Trends
A Guide to build a Data Analyst Portfolio
A Guide to Find a Data Analyst Job
Practical exercises
Next Steps

Introduction to Business and Data Analysis
Main business goals of data analysis
Data types and sources
Exploratory Data Analysis
Data Analysis in Business
Introduction to Business and Data Analysis
Understanding Business Needs and Defining Problems
Data Collection: Sources and Methods
Data Cleaning and Preparation
Exploratory Data Analysis (EDA)
Business Data Analysis Techniques
Data Visualization for Business Insights
Case Studies: Business and Data Analysis in Action
The Role of Technology in Business and Data Analysis
Building a Career in Business and Data Analysis

Module presentation
Module overview
Excel
Excel Practical Task
SQL
Exercise: Retrieve and Analyze Customer and Order Data with SQLite
Python
Handling Missing Data and Analysing the Data with Python
R practice
Exercise: Conduct Statistical Analysis Using R
Tableau
Exercise: Visualizing Global Earthquake Data with Geographic Representation
Next steps

Introduction to Data-Based Decision Making (DBDM)
The Data Landscape: Types, Sources, and Quality.
Data Collection and Preparation
Descriptive Analytics: Understanding the “What”
Diagnostic Analytics: Exploring the “Why”
Predictive Analytics: Forecasting the “Future”
Prescriptive Analytics: Recommending the “How”
Data-Driven Culture and Organizational Change
Tools and Technologies for Data-Based Decision Making
Case Studies in Data-Based Decision Making

Presentation
Course overview
Course Slide-deck
1. Advanced Formulas and Functions. Exercise 1: Logical Functions
Exercise 2: Lookup and Reference Functions
Exercise 3: Text Functions
Module 2: Data Analysis and Visualisation. Exercise 4: Pivot Tables
Exercise 5: Advanced Charting
Exercise 6: Conditional Formatting
Exercise 7: Data Tables
Module 3: Data Management and Cleaning. Exercise 8: Data Validation
Exercise 9: Text to Columns
Exercise 10: Removing Duplicates
Module 4: Automation with Macros and VBA
Exercise 11: Recording Macros
Exercise 12: Editing Macros
Exercise 13: Automating Tasks
Exercise 14: Debugging Macros
Module 5: Advanced Data Import and Export. Exercise 15: Using JSON
Exercise 16: Importing data
Exercise 17: Data Integration with SQLite and Excel
Exercise 18: Advanced Filtering and Sorting with Dynamic Arrays
AI-Powered Excel: Unleashing the Power of Gemini and Copilot
Next Steps in Excel Learning

Presentation
Course Materials
Course overview
Introduction
Basic SQL Commands
Retrieving and Manipulating Data
Advanced Queries and Data Aggregation
Working with Joins
Subqueries and Nested Queries
Modifying Data in SQL
Optimising and Indexing Your Queries
Advanced SQL Features
Final Project
Final Assessment Preparation
Recommended Resources
Introduction to SQL and Relational Databases
SQL Fundamentals: Building a Strong Foundation
Querying and Manipulating Data Effectively
Combining Data from Multiple Tables: Mastering Joins
Advanced Querying Techniques for Complex Analysis
Recursive Queries
Unlocking the Power of SQL for Data Analysis
Optimizing SQL Queries for Efficiency
Real-World SQL Data Analysis Examples and Use Cases
Integrating SQL with Popular Data Analysis Tools
SQL Security Best Practices
Exploring the Landscape of SQL Dialects

Module Presentation
Module Slide-deck and materials
Module Overview
Types of Marketing Data
M1_A01_The Role of Data Analysis in Modern Marketing
M1_A02_Data Sources for Marketers_ Internal and External Options
Developing an Analytical Mindset
M2_A03_Key Marketing Metrics
M2_A04_Limitations and Challenges in Metrics-Based Decision-Making
Data Analysis Frameworks in Marketing
M3_A05_Understanding Statistical Concepts for Marketers
M3_A06_Key Analytical Models
M4_A07_Principles of Data-Driven Segmentation
M4_A08_Theories of Customer Segmentation
M4_A09_Linking Segmentation to Campaign Strategies
Predictive Analytics A Marketer’s Perspective
M5_A10_Applications in Marketing
M5_A11_Introduction to Forecasting Techniques
Designing Effective AB Tests
M6_A12_Theoretical Basis of Campaign Analysis
M6_A13_Continuous Optimisation Frameworks
M7_A14_Ethical Considerations in Marketing Data
M7_A15_Bias in Marketing Data
M7_A16_Sustainability in Marketing Data Practices
M8_A17_The Data-Driven Marketing Cycle
M8_A18_Building a Data-Informed Marketing Culture
Short Summary
Study Guide and Quiz
Briefing Document: Data Analysis in Modern Marketing

Module overview
Module Slide-deck
The Importance of Data in Sales and Customer Service
Types of Data in Sales and Service Environments
Key Metrics and KPIs for Sales and Service
Overview of Data Analysis Tools
Sources of Sales and Service Data
Data Quality – Identifying & Handling Issues
Data Cleaning Techniques (Excel & Python Basics)
Structuring Data for Analysis
Customer Segmentation Techniques
Analysing Customer Feedback and Support Data
Understanding Sales Trends and Patterns
Sales Pipeline Analysis
Conversion Rate Optimisation Using Data
Analysing Customer Lifetime Value (CLV)
Sentiment Analysis for Customer Feedback
Personalisation Strategies Based on Data
Customer Churn Prediction and Prevention
Analysing Service Response Times and Customer Satisfaction
Identifying Service Bottlenecks
Optimising Workflows Using Data
Using Data to Improve Customer Support Efficiency

Module Introduction
Course Slides
Introduction to Data Governance
Data Governance Frameworks
Data Governance Roles and Responsibilities
Data Quality Management
Data Management Fundamentals
Data Governance and Management in Practice
Data Governance Tools and Technology
Next steps
Optional Quiz

Module Overview
Module Slide-deck
Cost-Benefit Analysis (CBA)
CBA Exercise: Evaluating a Digital Transformation Initiative
What is Data-Based Decision Making
Gathering the Right Data
Analysing and Interpreting Data
Conducting a Cost-Benefit Analysis
Integrating Data into Decision-Making Processes
Tools & Technologies for Data-Based Decision Making
Next Steps

Module Overview
Course Slide-deck
What is Financial Data Analysis
Importance of Data Analysis in Financial Decision-Making
Overview of Financial Data Sources
Tools for Financial Data Analysis
Setting Up Your Analytical Toolkit
Introduction to Yahoo Finance, Alpha Vantage, and Quandl
Types of Financial Data
Data Collection Methods
Handling Large Datasets in Excel and Python
Identifying and Handling Missing Data in Financial Analysis
Normalising and Transforming Financial Data
Outlier Detection in Financial Datasets
Working with Time-Series Data in Pandas
Common Financial Data Issues and How to Fix Them
Summary Statistics and Descriptive Analysis in Finance
Visualising Financial Data with Matplotlib and Seaborn
Identifying Trends and Seasonality in Financial Data
Correlation and Covariance in Financial Datasets
Detecting Market Anomalies through Data Analysis
Key Financial Ratios
Market Indicators
Portfolio Performance Metrics
Risk Measurement
Introduction to Time-Series Forecasting in Finance
Moving Averages and Exponential Smoothing
ARIMA Modeling
Forecasting Stock Prices Using Python
Limitations and Challenges in Financial Forecasting
Basics of Machine Learning for Financial Data
Supervised vs Unsupervised Learning in Finance
Algorithmic Trading Basics (Introduction to Trading Bots)
Predicting Stock Prices with Regression Models
Clustering Techniques for Market Segmentation
Understanding Risk and Return in Finance
Modern Portfolio Theory (MPT) and the Efficient Frontier
Monte Carlo Simulation for Risk Assessment
Portfolio Diversification and Asset Allocation
Real-World Portfolio Optimization Using Python
Basics of SQL for Financial Data Retrieval
Writing Queries to Extract Market and Company Data
Joining and Aggregating Financial Datasets with SQL
Automating Financial Reports with SQL
Big Data Analytics in Finance: An Overview
Risk Assessment in Finance with Big Data
Customer Segmentation in Finance with Big Data
Fraud Detection in Finance with Big Data
Finance Data Analysis & Analytics: Tools, Techniques, and Best Practices

Module Slide-deck
The Role of Data in Modern Operations
Foundational Concepts of Data Analysis
Introduction to Tools and Software
Data Sources in Operations
Data Cleaning and Preprocessing
Data Integration and Transformation
Data Collection Methods
Creating Derived Metrics and KPIs
Analysing Key Performance Indicators (KPIs)
Basic Statistical Analysis for Operations
Root Cause Analysis
Process Analysis and Improvement
Variance Analysis
Flowcharts
Mapping and Analysing Operational Processes
Introduction to Forecasting Techniques
Visualising Operational Data
Predictive Modelling in Operations
Scenario Planning and Simulation
Regression Analysis for Forecasting
Regression Analysis for Forecasting
Statistical Process Control (SPC)
Optimisation Techniques
Machine Learning in Operations
Linear Programming for Resource Allocation
Linear Programming for Resource Allocation
Developing Data-Driven Strategies
Change Management and Communication

Presentation of the Module
Module overview
Decoding the Context for Effective Communication
How We See: The Principles of Visual Perception
Selecting the Right Visual for Your Message
Designing for Clarity and Impact: Eliminating Clutter
Guiding the Eye: Focusing Your Audience’s Attention
The Magic of Narrative
Dissecting Effective Visual Stories and Seeking Fresh Perspectives
Congrats on finishing the Module
The Little Red Riding Hood

Course presentation
Course overview
Python basics recap
Intro to data analysis
Intro to Pandas
File handling data storage
Descriptive statistics
Handling missing data
Data validation and quality control
Inferential statistics
Regression analysis
Merging and joining datasets
Handling time series data
Pivot tables and reshaping
Introduction to visualization libraries
Expanding your visualization toolkit
Lets practice
Exercise 1
Exercise 2
Exercise 3
Final words

Course overview
Introduction to Cognitive and Emotional Drivers
Heuristics and Mental Shortcuts
Emotional Intelligence and Decision Making
Stress, Pressure, and Decision Quality
Decision Fatigue and Choice Overload
Biases in Group Settings
Nudging and Behavioural Economics in Decision Making
Motivation and Behavioural Drivers
Risk Perception and Cognitive Framing
Psychological Safety in Teams
Decision Making in Uncertainty
Ethical and Moral Dimensions of Behavioural Decisions
Applying Behavioural Insights in Organisational Contexts
Integrating Psychology & Emotional Intelligence with Decision Making

Communicating Data Insights Online

Interactive Part
Tactics for Clear Storytelling Logic and Structure
Congratulations with finishing from MTF
Bonus Section: Next Steps

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. 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.
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.
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.

Note: The course is free for a limited time and limited to 500 users. Enroll as soon as possible. If it is no longer free, it means you are late, and it has already exceeded its limits.

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