System/Business Analyst

System/Business Analyst oversee the analysis and development of a company’s business operations. Also known as System Analyst, that highly-analytical specialist has both business and technical expertise. Duties include analyzing requirements, estimating the cost, and establishing system protocols.

“Business analysts possessing intellectual curiosity have voracious appetites for learning. They do not shy away from new or unfamiliar concepts but rather try to incorporate those concepts into their understanding. They tend to be very good listeners who absorb information like sponges.” – Steve Blais, PMP

I have over 15 years professional experience in business managing, so as developer I can help accelerate the delivery of apps and provide consistently outstanding experiences to users. This cohesion between core business, developer and IT teams is essential if enterprises are to embed apps at the centre of a truly digital-first business strategy.

I work by BABOK® Guide, which is the standard for the practice of business analysis and is for professionals who perform business analysis tasks. Recognized globally as the standard of business analysis, it guides business professionals within the six core knowledge areas, describing the skills, deliverables, and techniques that business analysis professionals require to achieve better business outcomes.

My soft skills as a Business Analyst: needs analysis, eliciting requirements, business case definition, requirements writing, requirements review, flow and process diagramming, wireframing,, business data analytics, problem solving.

I am a Microsoft Certified: Power BI Data Analyst Associate, that confirm my knowledge of Power Query and writing expressions by using Data Analysis Expressions (DAX).


As a System/Business Analyst I can:

  • collecting functional and non-functional requirements and analyzing data sources,
  • creating business requirements, user stories, mockups, functional specifications and technical requirements (incl. flow diagrams, data mappings, examples),
  • close collaboration with developers (requirements presentation, backlog grooming, requirements change management, technical solution design together with Tech Lead, etc),
  • testing the developed solutions,
  • creating documentation (user guides, technical guides, presentations),
  • analytical documentation management,
  • working in Agile methodologies (SCRUM, Agile PM).

My Python, SQL, Tableau, Power BI and Spreadsheets Learning Path

With Data Camp and Microsoft Learn I build my skills and experience and validate my knowledge:

Data Skills for Business (Datacamp) 20 hours (skill track ⇒ certificate)

This course helped me sharpen my data skills and identify when data can be used to solve business challenges. I learned indispensable data terminology, tools, and questions that I can ask to communicate more effectively with my team. I introduced to statistics in spreadsheets, Python, machine learning, and AI to help me better lead my team.

In this course I tought about the skills I need on my data team, and how I can structure that team to meet my organization’s needs. I also provided with an understanding of data sources my company can use and how to store that data. I also discovered ways to analyze and visualize my data through dashboards and A/B tests. To wrap up the course, I discussed exciting topics in machine learning, including clustering, time series prediction, natural language processing (NLP), deep learning, and explainable AI. I also learned about a variety of real-world applications of data science and gain a better understanding of these concepts through practical exercises.

Statistics is the science that deals with the collection, analysis, and interpretation of data. I this course I used Spreadsheets functions, I divided into averages, distributions, hypothesis testing, and conclude the course by applying my newfound knowledge in a case study.

In this course I learned about the key elements of machine learning to the business leaders. I focused on the key insights and base practices how to structure business questions as modeling projects with the machine learning teams. I understood the different types of models, what kind of business questions they help answer, or what kind of opportunities they can uncover, also learn to identify situations where machine learning should NOT be applied, which is equally important. I also understood the difference between inference and prediction, predicting probability and amounts, and how using unsupervised learning can help build meaningful customer segmentation strategy.

Data literacy is an essential skill for every role within an organization- not just data scientists and analysts. As companies collect more data than ever before, it’s critical that everyone can read and analyze that data efficiently. In this course, I learned the basics of data-driven decision-making and get to apply these skills to three real-life examples from the world of finance, marketing, and operations. I also discovered how to uncover new insights and opportunities by applying supply and demand, cost and benefit, and risk and rewards frameworks-gaining practical skills to help me thrive in the new data-driven world.

In this course I learned about the role of a Marketing Analyst and how they leverage data to better understand their customers and help companies grow. Through hands-on exercises, I learned how to answer the big questions like “did my campaign increase sales six months after launch?” I also worked with real-world data to perform essential marketing analysis, including building customer segments, market response models, calculating customer lifetime value (LTV), and much more.

In this course I understood the definition of AI ( “general” and “narrow”), the relationship between AI and Machine Learning. I also learned about supervised learning, work with labeled data and train regression models.

In this course I used data to solve the mystery of Bayes, the kidnapped Golden Retriever, and along the way I became familiar with basic Python syntax and popular Data Science modules like Matplotlib (for charts and graphs) and pandas (for tabular data).

SQL for Business Analysts (Datacamp) 20 hours (skill track ⇒ certificate)

In this track, I learned how to quickly explore and analyze data to make smarter business decisions. Through hands-on practice, I learned everything from creating and joining tables to writing queries, subqueries, and aggregate functions, providing me with the skills you need to excel and overcome real-world business challenges.

I used functions to aggregate, summarize, and analyze data without leaving the database. I learned common problems to look for and strategies to clean up messy data. I also learned how to exploring your own PostgreSQL databases and analyzing the data in them.

In this course, I learned how to use SQL to support decision making. I learned to apply SQL queries to study for example customer preferences, customer engagement, and sales development. This course also covered SQL extensions for online analytical processing (OLAP), which makes it easier to obtain key insights from multidimensional aggregated data.

In this course, I learned new skills that empowered me to find the tables I need. I learned how to store and manage this data in tables and views that I create. Best of all I also learned how to write code that not only clearly conveys my intent but is also legible.

In this course, I learned about the key metrics that businesses use to measure performance. I wrote SQL queries to calculate these metrics and produce report-ready results.

In this course, I applied all the SQL concepts and functions I have learned in previous courses to build out my very own dashboard.

AI Business Fundamentals (Datacamp) 11 hours (skill track ⇒ certificate)

In this track I learned the essential knowledge and tools necessary to make an immediate impact in the fast-paced world of Artificial Intelligence (AI). This course is designed for professionals who want to harness the power of Artificial Intelligence. I understood how to utilize generative AI and large language models effectively. I mastered these tools and techniques, and extracted significant business value from AI and stay ahead of the competition. 

Artificial Intelligence (AI) is without any doubt the most breakthrough and omnipresent discipline in today’s economy, media, industry, and society at large. This non-technical course will expose you to the basic elements of the rapidly evolving field of AI. Through plenty of hands-on activities and without any coding required, we will uncover together those jargon terms everyone keeps talking about, such as machine learning, deep learning, explainable AI, natural language processing, generative models, and more. We’ll also explore a variety of AI applications in everyday life and the AI techniques behind the scenes, how can organizations become AI-driven, and what are the key lessons learned, challenges, and societal implications of AI’s unstoppable progress. 

In this course I dived headfirst into the exciting world of generative artificial intelligence (AI) and discover how to wield ChatGPT like a pro. From text summarization, explaining complex concepts, drafting engaging marketing content, and generating and explaining code, I learned about the most common applications of ChatGPT. Finally, I explored the legal and ethical considerations that come with implementing ChatGPT in various situations.

In this course I learned about generative AI and how it differs from traditional AI. I dived deeper into the generative AI ecosystem and its key players: universities, governments, companies, and open-source communities. I discovered the impact of the cloud and discussed various model breakthroughs. I understood how we got to this huge technological breakthrough.

In this course I delved into the business applications of Large Language Models (LLMs). I discovered how they are empowering businesses to improve their workflows and find success in the new AI-powered era. Grasping their foundational elements equiped me with key insights into LLMs and their impact on modern business.

In this course I delved into the interconnectedness of business, data, and AI strategies, equipping me with the knowledge to create a strong strategic framework. I learned to differentiate AI from traditional software, set realistic business goals, and assess ROI for AI projects. I discovered the key components of a successful AI strategy, emphasizing innovation and risk assessment. Finally, I explored efficient AI scaling and the role of executive sponsors and champions in driving AI adoption.

In this course I learned about the core principles of ethical AI and expand your understanding of common challenges and opportunities in the field of AI ethics. Through hands-on exercises, I built your skills to craft ethical AI by design.

In this course, I learned how to successfully implement an AI solution for my business. I identified appropriate business use cases, build a proof of concept, measure success, and evaluated how to move from POC to a fully implemented solution. I also learned how Responsible AI, security, and upskilling are equally important components.

Business Analytics with Excel: Elementary to Advanced (Johns Hopkins University, Coursera) 23 hours (course ⇒ certificate)

This Business Analytics class focused on introduces to analytical frameworks used for decision making though Excel modeling. These include Linear and Integer Optimization, Decision Analysis, and Risk modeling. For each methodology I exposed to the basic mechanics, and then apply the methodology to real-world business problems using Excel.

Emphasis was not on the “how-to” of Excel, but rather on formulating problems, translating those formulations into useful models, optimizing and/or displaying the models, and interpreting results. The course prepared managers who were comfortable with translating trade-offs into models, understanding the output of the software, and who are appreciative of quantitative approaches to decision making.

Business analytics makes extensive used of data and modeling to drive decision making in organizations. This class focused on introducing me to analytical frameworks used for decision making to make sense of the data, starting from the basics of Excel and working up to advanced modeling techniques.


The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.

⇒ Verify at: Coursera

Google Business Intelligence Professional (Google, Coursera) 74 hours (course ⇒ certificate)

Business intelligence professionals collect, organize, interpret, and report on data to help organizations make informed business decisions. Some responsibilities include measuring performance, tracking revenue or spending, and monitoring progress.

This certificate builds on my data analytics skills and experience to take my career to the next level. I expanded my knowledge with practical, hands-on projects, featuring BigQuery, SQL, and Tableau.

I learned how to prepare for jobs like business intelligence analyst, business intelligence engineer, business intelligence developer, and more.

⇒ Verify at: Coursera

Agile with Atlassian Jira (Atlassian, Coursera) 12 hours (course ⇒ certificate)

In this course I learned foundational principles and practices used by agile methodologies, providing you with a flexible set of tools to use in my role (e.g. product owner, scrum master, project manager, team member) on an agile team. I also learned agile and lean principles, including kanban and scrum, and used Jira Software Cloud as the tool to apply hands-on exercises in these topics. The course included instruction on company-managed and team-managed Jira projects.

⇒ Verify at: Coursera

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