Ammar - Databases tutor - Montréal
1st lesson free
Ammar - Databases tutor - Montréal

One of our best tutors. Quality profile, experience in their field, verified qualifications and a great response time. Ammar will be happy to arrange your first Databases lesson.

Ammar

One of our best tutors. Quality profile, experience in their field, verified qualifications and a great response time. Ammar will be happy to arrange your first Databases lesson.

  • Rate £31,047
  • Response 2h
  • Students

    Number of students Ammar has accompanied since arriving at Superprof

    50+

    Number of students Ammar has accompanied since arriving at Superprof

Ammar - Databases tutor - Montréal
  • 5 (24 reviews)

£31,047/hr

1st lesson free

Contact

1st lesson free

1st lesson free

  • Databases
  • MySQL
  • Data Analysis
  • Oracle
  • Microsoft Access

Master Database Management, Data Analysis & Visualization with Excel, SQL, MySQL, Python, Power BI & Tableau | PhD Engineer and Professor | Beginner to Professional

  • Databases
  • MySQL
  • Data Analysis
  • Oracle
  • Microsoft Access

Lesson location

Ambassador

One of our best tutors. Quality profile, experience in their field, verified qualifications and a great response time. Ammar will be happy to arrange your first Databases lesson.

About Ammar

I am a multidisciplinary educator, researcher, engineer, trainer, and consultant with more than 25 years of experience in data analysis, information systems, research, professional training, and applied problem solving.
My academic background includes a Bachelor’s degree in Engineering, a Master’s degree in Management Information Systems, and a PhD in Knowledge Management and Artificial Intelligence. This combination allows me to connect data, technology, systems thinking, research, and real organizational needs rather than teaching software commands in isolation.
Over the years, I have completed and supported hundreds of analytical, database, research, and applied projects and have guided university learners and researchers working on complex quantitative and information-management problems. My teaching approach is structured, example-based, and focused on helping the learner understand what is happening, why it works, and how to apply it independently.
For Data Analysis specifically, I combine analytical reasoning with practical workflows using Microsoft Excel, SQL, MySQL, relational databases, Microsoft Access, and related tools when appropriate. I focus on data quality, query logic, database relationships, exploratory analysis, reporting, interpretation, and the complete path from raw data to useful evidence.
I teach in English, French, and Arabic and adapt my explanations to the learner’s academic background, profession, technical confidence, software environment, and goals.

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About the lesson

  • Primary
  • secondary
  • Seconde
  • +16
  • levels :

    Primary

    secondary

    Seconde

    BTS

    Higher education

    Professional training

    Masters

    MBA

    Pre-primary

    Beginner

    Intermediate

    Advanced

    Professional

    Children

    Pós-Graduação

    Doctorate

    ACCA

    Première

    Terminale

  • French
  • English

All languages in which the lesson is available :

French

English

Data analysis is not simply about using Excel, writing SQL queries, or building dashboards. It is a complete reasoning process:

**Define the question → understand the data → prepare it → analyze it → verify the results → visualize the evidence → communicate the conclusion.**

My lessons help you understand this complete workflow so that you can work accurately, independently, and confidently with real academic, research, business, or organizational data.

I teach beginners, college and university students, researchers, analysts, managers, engineers, and professionals. Each lesson is adapted to your current level, software environment, dataset, assignment, examination, workplace task, or professional objective.

At the beginning, I identify your existing knowledge, the structure and quality of your data, the expected output, the software requirements, and the main technical or conceptual difficulties. We then establish a clear and practical learning plan.

Depending on your objectives, lessons may cover:

A- Data understanding, preparation, and quality
• Understanding datasets, observations, variables, fields, records, tables, data types, measurement levels, and analytical units
• Translating academic, organizational, or business questions into clearly defined analytical objectives, variables, measures, key performance indicators, and reporting requirements
• Data cleaning and quality control, including missing values, duplicates, inconsistent formats, invalid entries, outliers, coding errors, and logical validation
• Importing, restructuring, filtering, sorting, grouping, reshaping, aggregating, merging, and combining datasets
• Data governance and quality practices, including validation rules, metadata, data dictionaries, lineage, ownership, access permissions, privacy awareness, documentation, and auditable analytical decisions

B- Microsoft Excel and spreadsheet analysis
• Microsoft Excel from foundational to advanced analytical use
• Formulas and functions, including logical, text, date, lookup, statistical, financial, and conditional functions
• XLOOKUP, INDEX–MATCH, dynamic arrays, structured references, named ranges, conditional logic, and error handling
• PivotTables, PivotCharts, slicers, data validation, conditional formatting, scenario analysis, Goal Seek, and what-if analysis
• Power Query for importing, cleaning, transforming, reshaping, appending, and merging data through repeatable workflows
• Power Pivot and the Excel Data Model, including relationships, calculated columns, measures, and analytical modelling
• Professional dashboards, reusable templates, automated reports, and management summaries
• Excel macros and VBA when automation is relevant to the learner’s course or workplace objective

C- Python for data analysis
• Python for data analysis using Jupyter Notebook, Anaconda, or Visual Studio Code
• pandas for importing, cleaning, filtering, grouping, aggregating, reshaping, merging, and analyzing data
• NumPy for numerical arrays and computational foundations
• Matplotlib and Seaborn for exploratory and professional data visualization
• Working with CSV, Excel, text, and database data
• Writing clear, reusable, and well-documented analytical scripts
• Understanding errors, debugging code, validating results, and organizing reproducible notebooks and projects
Python is taught here specifically as a data-analysis tool. General programming, software development, artificial intelligence, and advanced machine learning remain separate learning pathways.

D- SQL and database querying
• SQL from beginner to advanced analytical querying
• SELECT statements, filtering, sorting, grouping, aggregation, aliases, and conditional expressions
• INNER, LEFT, RIGHT, FULL, and self-joins
• Subqueries, correlated subqueries, set operations, and common table expressions
• CASE expressions, date functions, text functions, conversion functions, and handling NULL values
• Window functions, ranking, running totals, moving calculations, partitioning, and analytical comparisons
• Views, temporary tables, reusable queries, and multi-table analysis
• Query troubleshooting and optimization, including indexes, execution logic, efficient joins, duplicate prevention, validation, and performance-aware query design

E- Relational database design and management
• Database requirements analysis and relational modelling
• Entities, attributes, tables, records, fields, relationships, and business rules
• Entity–relationship diagrams
• Primary keys, foreign keys, candidate keys, composite keys, and surrogate keys
• Cardinality, referential integrity, constraints, and validation rules
• Normalization, functional dependencies, schema design, and data dictionaries
• Views, transactions, stored procedures, triggers, indexing, user permissions, security, backup principles, and concurrency foundations
• Understanding the difference between operational databases, analytical databases, spreadsheets, and business-intelligence models
Practical work may involve MySQL, Microsoft Access, Oracle, SQL Server, or PostgreSQL, depending on the learner’s course, project, or professional environment.

F- Data integration, ETL, and warehousing
• Extracting data from spreadsheets, text files, databases, and other agreed data sources
• Transforming formats and structures, standardizing values, merging sources, validating outputs, and loading prepared data into analytical systems
• ETL workflow foundations: extraction, transformation, validation, and loading
• Staging data, operational versus analytical systems, data marts, dimensional models, and the movement of data from source systems to reports and dashboards
• Introductory data warehousing concepts, including fact tables, dimension tables, star schemas, snowflake schemas, data granularity, and reporting structures

G- Power BI and business intelligence
• Power BI Desktop from data import to interactive reporting
• Power Query for cleaning and transforming data
• Relationships, cardinality, filter direction, data granularity, and dimensional modelling
• Fact and dimension tables, star schemas, and business-intelligence data models
• Calculated columns, measures, and DAX foundations
• Row context, filter context, CALCULATE, time-intelligence foundations, and KPI development
• Interactive reports using filters, slicers, drill-through, tooltips, bookmarks, hierarchies, and navigation
• Dashboard layout, visual consistency, accessibility, usability, and audience-focused presentation
• Publishing, refreshing, sharing, workspaces, and Power BI Service concepts when appropriate

H- Tableau
• Connecting and preparing data in Tableau
• Dimensions, measures, discrete and continuous fields
• Calculated fields, table calculations, parameters, filters, sets, groups, and hierarchies
• Level-of-detail calculation foundations
• Interactive charts, dashboards, actions, stories, and presentation workflows
• Designing visualizations that support clear comparisons, exploration, and decision-making

I- Data visualization and storytelling
• Selecting the correct chart for the analytical question
• Visualizing comparisons, trends, distributions, relationships, composition, ranking, geography, and uncertainty
• Using position, length, size, colour, labels, annotations, and hierarchy effectively
• Designing clear and accessible charts and dashboards
• Avoiding misleading scales, unnecessary decoration, visual clutter, and incorrect comparisons
• Matching the level of detail and presentation style to technical, managerial, academic, or non-technical audiences
• Data storytelling: moving from raw results to a coherent narrative, explaining KPIs, identifying patterns and exceptions, highlighting uncertainty, and presenting evidence-based conclusions and recommendations

K- Reproducibility and professional workflow
• Reproducible and maintainable workflows using documented formulas, SQL queries, scripts, notebooks, naming conventions, structured folders, and versioned files
• Git and version-control foundations when appropriate
• Checking calculations, reconciling results across tools, validating joins and relationships, and identifying the source of inconsistencies
• Distinguishing among data-quality problems, incorrect formulas, wrong joins, misunderstood data types, faulty relationships, weak analytical questions, and interpretation errors
• Producing clear technical documentation so that the analysis can be reviewed, repeated, updated, and transferred to another user

L- End-to-end analytical projects
Lessons may follow a complete real-world project such as:
**Question definition → data collection or import → cleaning → database design → SQL extraction → Excel or Python analysis → BI modelling → dashboard development → interpretation → final report or presentation**

Projects can be based on your own academic, research, organizational, or workplace data, provided that confidential information is removed or handled appropriately.

Professionals and career-transition learners may also build a carefully documented portfolio project demonstrating:
• Data preparation
• SQL analysis
• Excel or Python workflows
• Database design
• Power BI or Tableau dashboards
• Interpretation and communication of findings

M- Lesson structure and deliverables

A lesson may include:
• A brief diagnostic review
• Explanation of the relevant concepts
• Live software demonstration
• Guided practice
• Independent application
• Error analysis and troubleshooting
• Validation of the final result
• A concise summary and recommended next steps

When useful, you may receive annotated spreadsheets, SQL scripts, Python notebooks, database diagrams, Power BI or Tableau files, reusable templates, concise lesson notes, targeted exercises, or a documented workflow that you can continue independently.

For ongoing instruction, we can follow a structured pathway, monitor your mastery of tools and concepts, maintain a project portfolio, and progressively move from guided examples to independent analysis.

Preparation may also be aligned with Excel, SQL, Tableau, or Microsoft Power BI PL-300 objectives when the relevant curriculum and requirements are agreed in advance.

My objective is not merely to fix a formula, complete one query, or build one chart. It is to help you understand the complete data workflow, recognize why errors occur, select the correct tool, verify your results, and communicate reliable conclusions.

Whether you need to learn from the beginning, complete an assignment, prepare for an examination, improve a workplace report, design a database, analyze a real dataset, build an interactive dashboard, or develop professional data-analysis skills, each lesson will be built around your actual objective.

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Rates

Rate

  • £31,047

Pack prices

  • 5h: £155,237
  • 10h: £310,475

online

  • £31,047/h

Travel

  • + £10

free lessons

The first free lesson with Ammar will allow you to get to know each other and clearly specify your needs for your next lessons.

  • 1hr

Details

Getting started: You may begin directly with a paid tutoring session when the topic and objective are already clear. If you prefer to discuss your needs first, we can have a brief free Zoom meeting - together with a parent or guardian when relevant - to clarify the level, goals, and best learning plan. No booking or payment is required for this introductory meeting;

Ammar's Video

Find out more about Ammar

Find out more about Ammar

  • When and why I developed an interest in my chosen fields?

    I developed an interest in my various professional fields because they would allow me to put into practice
    my skills and my great rigor. I think that I am able to thrive in my domain thanks to my inclination from an
    early age. In addition, my deep experience allowed me to provide quality assistance to the people who
    need support.
    Technology and IT are areas that have always fascinated me: it was to feed my passion that I decided to
    choose this profession. In addition, this domain corresponds perfectly to my expectations and aligns very
    well with my objectives.
  • What makes me passionate about your subject?

    My biggest passion is learning about software and technology. I was fascinated by computers from an
    early age, so I decided to study Science and Technology and have continued in that direction since
    graduating. It was a great choice for me!
    I have always been passionate about all my professions based on creativity. This passion still allowed me
    to acquire the professional expertise necessary to oversee much larger projects.
    In addition, one area I am passionate about is self-development and self-improvement in my personal life.
    I am always looking to challenge myself and learn new things. I have acquired excellent communication,
    listening and problem-solving skills which help me in my daily work but also in my personal life in terms
    of confidence, communication, etc. “
  • What do I like about tutoring? What makes me a "Superprof"?

    After more than 30 years of tutoring experience in various fields, I drew my philosophy of tutoring, one
    that gives the concept a special meaning with six dimensions:
    a- It is giving. It should be a will before being a career. I do own a very different concept that I cherished
    and became my slogan ever since for all my academic career life and through all the different types
    of businesses that I established. “I am your teacher for Life” is the departing teaching story with all
    my students who, and after 30 years still ask me for an advice, a lesson, a session, a consultation.
    b- Tutoring is a friendship. Unless you have the ability to build tight friendship links with your students,
    you will be considered just as a knowledge reciter. Teaching skills depend enormously on listening to
    students’ questions, paying attention to their real presence in class, adapting to their changing
    moods, following their personal goals, and drawing with them their prosperous future.
    c- Tutoring is a human art. No matter how arid the material you will be giving today, it should be taught
    with a human touch. Students need a humanistic approach for everything they learn. They want to
    feel and see every abstract and this could not be realized unless you sculpt the lesson in real life.
    d- Tutoring is expertise. It is an ongoing process where both knowledge and skills ought to stay together
    all the time to nurture students’ minds and practices and prepare them well for the job they seek.
    Teachers must be well equipped with the “know what, why, and how”. They ought to be well inclined
    to research in their field, pursuing development, and continuously updating their knowledge.
    Teachers must be proficient practitioners of their knowledge in the market field, able to draw a
    complete picture for their students and keep track on their career development to draw from their
    experience for their successors.
    e- Tutoring is creativity. Happiness is not math or physics; neither is it play and music. It is all together!
    Here comes the most delicate role of the teacher: how to create happiness out of everything and
    make students always satisfied and willing to learn more and more? Creativity is essential in the
    teaching process through linking the bits and cells of the body of knowledge differently to come out
    with new ideas. It is like joining the same puzzle pieces and coming always up with awesome pictures.
    This is what we call creative teaching.

    f-Tutoring is learning. An intelligent teacher is the one who learns through teaching others. It is a
    dynamic interactive relationship environment that the teacher tries to create with his/her students
    transforming the knowledge coming out to practices flowing in. Teaching must be a continuous action
    research process that helps teachers measure and discover.
  • Do I have any role models; a teacher that inspired you? Please explain.

    Either you choose a great role model that you can learn from and use in making decisions in your life, or
    you make the wrong decision when choosing a role model and end up on the wrong path.
    I don't have a single role model because no one is perfect. But one of them is my dad because he is smart,
    analytical and willing to give everything in the world for the people he loves, especially me and my sister
    and two brothers.
    The other was originally my classmate and then became my mentor. He opened several businesses and
    is ready to help his loved ones. He is straightforward, intelligent and prudent. The downside is that he can
    be very honest and has no problem criticizing others. As a mentor, he gave me his fair share of constructive
    criticism, but some people don't do that well.
  • What are my keys to success?

    The spirit for teaching as for leadership, from a personal point of view, is innate. If you are not born a
    teacher, you cannot be the perfect one. I remember my school days when I loved sharing knowledge
    with my schoolmates who became more and more attached to me, and still do after decades. I also
    remember working with my friend on our graduation project at the Faculty of Engineering at Ottawa
    University and providing him with most of the project's theoretical and CAD-based principles and details.
    All of the above conceptual meanings for teaching and many others have accompanied my teaching life
    and in many fields: science, engineering, management, and computing throughout which, I found that the
    the typical student is the one who is always looking for answers to questions specific to his/her own life: past,
    present, and future in order to achieve the main goal, that is, happiness. Students expect us to teach
    them with no retention, to be their friends, not competitors, to teach them life, not books, to make them
    brilliant, creative and self-learners and not reliant on others.
    So, my actual keys to success are my students.
--
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