Applied Data Science Lab: From Raw Data to Business Impact

From 65 C$ /h

Overview

Transitioning from learning data science theory to solving actual business problems is the hardest step for any aspiring data professional. [Insert Chosen Course Title] is an intensive, mentor-led program designed to simulate a real-world data team environment. Instead of working through synthetic, pre-cleaned textbook datasets, you will take on messy, complex industry scenarios and turn them into end-to-end data products.

What You’ll Experience

End-to-End Execution: Walk through the full data lifecycle—from problem scoping and data extraction to exploratory analysis, modeling, and executive stakeholder presentation.

Industry-Standard Workflows: Work with messy real-world datasets, practice Git-based version control, write production-ready code, and structure reports that business leaders actually care about.

1-on-1 & Group Mentorship: Receive continuous code reviews, architectural feedback, and project guidance mirroring the experience of working under a Senior Data Scientist or Analytics Lead.

Portfolio-Ready Deliverables: Graduate with 2–3 complete, polished projects that demonstrate actual business value to hiring managers—not just another churn prediction copy-pasted from Kaggle.

Who This Is For
Aspiring Data Analysts, Data Scientists, and recent graduates who know Python, but want the practical experience, confidence, and portfolio needed to land high-impact roles in the industry.

Extra information

Prerequisites & Tech Requirements
To fully participate in hands-on sessions, you will need a personal laptop with internet access. Exercises and project work can be completed either by installing Python locally or directly in your browser using Google Colab—no high-performance hardware required.

Location

Online from India

About Me

26 Years of Industry Experience: A seasoned tech professional and leader from India, with over two decades of experience building and delivering data solutions within a world-leading financial institution.

Friendly & Approachable Mentor: Passionate about demystifying complex concepts through a supportive, encouraging teaching style that builds student confidence from day one.

Avid Pythonista: Deeply passionate about Python programming, clean code, and leveraging open-source tools to solve complex, real-world data problems.

Dedicated to Career Growth: Enthusiastic about guiding students and young professionals as they navigate the transition from academic learning to corporate environments.

Mock Interview Specialist: Conducts structured, realistic mock technical and analytical interviews to help students sharpen their problem-solving skills and land job placements.

Education

1996 - 2000
Bachelor of Engineering, Instrumentation and Control
Bharath Institute of Science and Technology
University of Madras, Chennai, India
I am a University Rank Holder

Experience / Qualifications

26 Years at BNY

Data Scientist | Senior Vice President (2017 – Jun 2026): Led AI/ML, simulation modeling, and custom ranking engine initiatives.

Application Architect | Vice President (2013 – 2017): Architected enterprise search engines and site-wide frameworks.

Java Developer | Assistant Vice President (2000 – 2013): Led core software design and workflow development.

Age

Teenagers (13-17 years old)

Adults (18-64 years old)

Student level

Beginner

Intermediate

Advanced

Duration

60 minutes

The class is taught in

English

Skills

Python

Computer modeling

Numerical analysis

Availability of a typical week

(GMT -04:00)

New York

Online via webcam

Mon

Tue

Wed

Thu

Fri

Sat

Sun

00-04

04-08

08-12

12-16

16-20

20-24

Matías

4.88

(85)

Python is one of the best, if not the best, language to start learning programming. It is also one of the most widely used languages today, especially in cutting-edge areas such as machine learning.

This popularity means that Python is constantly evolving. It offers a wide range of tools and libraries, which are free and very varied.

As an aeronautical engineer, I like to share my knowledge and derive satisfaction from it by teaching and motivating others.

I'm used to working with people of different ages. I believe in the importance of segmenting learning, visualizing progress, setting concrete goals and practicing regularly.

Beyond these general principles, there is no magic rule or method. Some approaches work with some students but not with others. Adaptation to individual needs is therefore the main objective of private lessons. So I will do my best to find what motivates and helps my student.

Dimitri

4.88

(85)

Microsoft Excel is very powerful data analysis software. It is a practical solution in the short to medium and long term to automate your calculations, to have a global and detailed overview on your activities, and to analyze your data.

As an accountant, marketer, commercial agent, secretary, merchant, salesperson or company manager, a good mastery of this software will improve your efficiency, your competitiveness, and will save you a lot of time and money. Whatever your field of activity, this software is designed to help you.

During this training you will learn:
- best practices, functionalities and tools;
- functions and their use;
- handling of Dynamic Cross Tables, dynamic graphics,
- the design of dashboards,
- and you will acquire reflexes that will be useful for your entire career.

Duration of training: 1 month
Number of hours: 24 hours

I am expecting many of you because we have a lot to share.

Mohamed

4.88

(85)

An exam is coming up and your child doesn't know where to start? Is the Baccalaureate or Brevet exam approaching and they lack method or confidence?
He knows the material but loses his composure when faced with an exercise?
He makes repetitive mistakes despite his revisions?
He doesn't know how to organize his revision?
There isn't enough time to review all the chapters?
Or does he simply want to prepare himself seriously to achieve better results?
I can help him organize his work, target his difficulties and train effectively.
I am a mathematics teacher with 15 years of teaching experience and hold a Master's degree in mathematics. I tutor middle and high school students, particularly in the French and Moroccan curricula.
🎯 Preparation tailored to the student's objective
Good preparation is not just about doing a lot of exercises.
First, we need to identify:
• concepts that are not mastered;
• errors that recur regularly;
• methods that the student does not yet know how to use;
• the priority chapters;
• the actual level of preparation.
Next, we implement a targeted and progressive approach.
📝 Preparation for tests and exams
Depending on the level and objective, we can work on:
• review of essential concepts;
• classic exercises and more complex exercises;
• the exam topics;
• problems requiring multiple steps of reasoning;
• the problem-solving methodology;
• drafting the solutions;
• time management;
• the analysis and correction of errors;
• the strategies to use when faced with different types of exercises.
For the Brevet or the Baccalaureate, we can also work from past exam papers to gradually familiarize the student with the exam format.
🔎 A method based on errors
An error is not simply something to be corrected.
It allows us to understand what is not yet mastered.
During the sessions, we therefore analyze the errors to determine their origin, then we repeat the method until the student is able to solve a similar exercise independently.
Understand → Practice → Identify errors → Correct → Repeat → Master
💻 Interactive online courses
The classes are held remotely using Google Meet and an interactive whiteboard.
The student works directly with me during the session. It's not simply a matter of looking at a solution: they must search, explain their reasoning and participate actively.
I can also send him screenshots of the work done during the session to facilitate revisions.
🏆 15 years of experience
I have worked with many students of varying levels and difficulties.
For my final year students who were tutored in mathematics, I achieved a 100% success rate in the Baccalaureate, with 75% obtaining a distinction of "Bien" or "Très Bien".
This experience allows me to adapt the work to the available time and the actual level of the student, especially when an exam is approaching.
👨‍🎓 For which students?
• College
• High school
• Second
• First
• Final year
• Preparation for the Brevet exam
• Preparation for the Baccalaureate
• Preparation for inspections
• Students needing a refresher course before an exam
The French and Moroccan programs are taken into account.
📩 An exam is coming up?
In your message, please tell me:
• the student's level;
• the type of examination or test;
• the date;
• the relevant chapters;
• the main difficulties encountered.
This will allow me to determine priorities and propose work that is adapted to the time available.
The goal is to prepare the student effectively, to correct their priority difficulties and to enable them to approach their exam with more method and confidence.

Giuliano

4.88

(85)

Python is a powerful and versatile programming language with countless possibilities. You can use it for data analysis, image processing, automation, software development, hardware control, and much more.

Do you want to create your own software?
Work with data or images?
Automate repetitive tasks?
Control or manage your own hardware?

Whether you are just starting to learn Python or already have a specific project and need some guidance, I would be happy to help you.

My goal is to explain things clearly, adapt to your level, and help you understand not only how to make something work, but also why it works.

Let's turn your ideas into working Python projects!

Salah

4.88

(85)

Master Python with Personalized Courses

Discover the art of programming with Python courses tailor-made to meet your specific needs. Whether you are a beginner, intermediate or professional, my lessons are suitable for all levels.

Why Choose My Courses?

Personalized Teaching Approach: Each course is tailored to your skill level and individual goals.

Practical Experience: Learn by doing with real-world projects that build your understanding and skills.

Ongoing Support: Get unlimited email support for any questions you have between sessions.

As a Python expert, I have a passion for teaching and sharing my knowledge. My goal is to guide you effectively in your learning journey.

Book Your First Lesson:

Start your journey to Python mastery now by booking your first lesson. Whether you aspire to enter the development field or hone your existing skills, these courses are designed for you.

Medo

4.88

(85)

With 14 years of experience teaching mathematics in middle and high school, I offer you tailor-made support to help you progress in math. Whether you want to fill in your gaps, prepare for an exam (brevet, baccalaureate) or simply deepen your knowledge, I am here for you. My private lessons, given online or at home, are adapted to your pace and your specific needs. I guarantee you a clear and effective pedagogy, as well as personalized follow-up to help you succeed. Specializing in 3rd, Brevet and Terminale levels, I help you acquire the methods and tools necessary to master mathematics and enjoy learning them.
My goal is to help you improve your math skills quickly and prepare effectively for the bac/brevet. I am also attentive to students experiencing academic difficulties and I can offer you personalized support to overcome your difficulties.
I am considered one of the best online math tutors. I also offer math support for French mission students and math help for international students wishing to follow the French curriculum.
I offer a full range of services in mathematics:

Private lessons: at home or online
Personalized academic support: for all levels and all programs
Preparation for exams: brevet, baccalaureate, competitive examinations
Homework help: for regular monitoring
Intensive courses: for rapid progress
Specific support: for students in difficulty, students on French missions, international students
Thanks to my personalized math lessons, you will be able to:

Understand the concepts in a clear and simple way
Solve the exercises with ease
Gaining self-confidence
Pass your exams with flying colors
I am here to help you discover the fun of mathematics and reveal your potential.

Feel free to contact me for more information.

Hassane

4.88

(85)

Description:
This course is ideal for beginners or intermediate learners who want to learn programming using languages like C#, Java, or Python. With a step-by-step approach, you'll be guided from basic algorithms to object-oriented programming.

Goals :

Introduction to algorithms and their implementation.
Master the basics of C#, Java, and Python languages.
Understand the concepts of classes, objects, and error management.
Course methods and format:

Video lessons: Clear explanations and practical exercises.
Flexibility: Personalized support to meet your expectations.
For who ?
Students or professionals starting out in programming, or preparing for exams.

Baia

4.88

(85)

I teach Python specifically for finance and data applications - the kind used in economics, business analytics, and quantitative programs. This isn't a general "learn to code" course; it's built around real financial data, benchmarking, and the workflows you'll actually use in coursework or early career work.

Topics include:
Python fundamentals through a finance lens (data structures, functions, control flow).
Working with financial data and datasets.
Performance benchmarking and writing efficient code.
Applying concepts from Hilpisch's Python for Finance.

Kevin

4.88

(85)

► EXPERT STATISTICS, DATA ANALYTICS, MACHINE LEARNING & AI TUTOR FROM SWITZERLAND
► UNIVERSITY, FOUNDATION, IB, A-LEVEL & INTERNATIONAL SCHOOL SUPPORT

I completed my Master’s degree in Business Information Systems at a Swiss University of Applied Sciences, where my academic background strongly combined mathematics, statistics, data analysis, analytical thinking and problem-solving. This technical and data-oriented foundation shaped the way I teach today: clearly, logically and with a strong focus on real understanding.

For many years, I have successfully supported students in Statistics, Data Analytics, Machine Learning and AI. My main focus is especially on Statistics — from basic descriptive statistics to advanced statistical methods, hypothesis testing, regression, probability distributions and interpretation of results.

I mainly use R for statistical analysis, data handling, visualisation and practical exercises. My goal is not only to help students calculate results, but to make sure they understand what the results mean and how to explain them correctly.

► STATISTICS, DATA ANALYTICS & AI SUPPORT

► STATISTICS & PROBABILITY
I help students understand descriptive statistics, probability, random variables, distributions, sampling, confidence intervals, hypothesis testing, p-values, correlation, regression and statistical interpretation. My lessons focus on explaining the logic behind each method, not just applying formulas.

► APPLIED STATISTICS WITH R
I support students in using R for statistical analysis, data cleaning, visualisation, hypothesis testing, regression models and interpretation of outputs. Students learn how to connect theory, calculation, code and real meaning step by step.

► QUANTITATIVE METHODS & RESEARCH STATISTICS
I help students with statistical methods used in business, economics, psychology, social sciences, science and university research. This includes choosing the correct test, understanding assumptions, interpreting results and presenting findings clearly.

► DATA ANALYTICS & DATA SCIENCE
I support students with data preparation, exploratory data analysis, visualisation, dashboards, summary statistics and practical interpretation. The focus is always on understanding the data and drawing meaningful conclusions.

► MACHINE LEARNING & AI FOUNDATIONS
For students working with modern data topics, I also provide support in the foundations of Machine Learning and AI, including regression, classification, clustering, model evaluation and practical applications. These topics are explained from a statistical point of view, so students understand the logic behind the models.

► UNIVERSITY, FOUNDATION & INTERNATIONAL COURSES
I support students in Statistics, Data Analytics, Business Analytics, Quantitative Methods, Econometrics, Research Methods and technical modules. I help with exam preparation, assignments, projects and practical data analysis tasks.

► HOW I TEACH

► I FOCUS ON REAL STATISTICAL UNDERSTANDING.
Statistics becomes much easier when students understand why a method is used, what the result means and how to interpret it correctly.

► I EXPLAIN FORMULAS STEP BY STEP.
Difficult formulas, tests and models are broken down into simple, logical parts so students can follow the reasoning clearly.

► I CONNECT THEORY WITH R PRACTICE.
Students learn not only the statistical theory, but also how to apply it in R, read the output and explain the result in proper academic language.

► I HELP STUDENTS CHOOSE THE RIGHT METHOD.
Many students struggle with deciding whether to use a t-test, chi-square test, ANOVA, regression or another method. I teach students how to recognise the correct approach from the question or dataset.

► I TRAIN INTERPRETATION AND EXAM TECHNIQUE.
Students learn how to structure statistical answers, write clear conclusions, explain p-values, interpret confidence intervals and present results professionally.

► I ADAPT EVERY LESSON TO THE STUDENT.
Some students need help with theory, others with R coding, assignments, research projects or exam preparation. I adjust every lesson to the student’s exact course, level and goals.

► YEARS OF EXPERIENCE WITH STATISTICS, DATA & UNIVERSITY STUDENTS

Over the years, I have successfully supported students from demanding academic programmes, helping them strengthen their statistical understanding, improve their analytical thinking and achieve excellent progress in Statistics, Data Analytics, Machine Learning and AI.

► ONLINE LESSONS

► Interactive whiteboard
► Clear digital notes
► Step-by-step statistical explanations
► R support for data analysis
► Exam preparation
► Assignment and project guidance
► Practical examples with real datasets
► Focused one-to-one support from Switzerland

► MY GOAL

My goal is not only to help students pass exams or complete assignments, but to help them truly understand Statistics. With the right guidance, statistical methods become logical, practical and much easier to apply.

► SUBJECTS: Statistics, Probability, Data Analytics, Data Science, Machine Learning, AI, Quantitative Methods, Research Methods, Econometrics
► MAIN TOOL: R
► LEVELS: International School, IB, A-Level, Foundation Courses, University Modules, Professional Training
► FORMAT: Online tutoring from Switzerland
► FOCUS: Statistical understanding, R practice, interpretation, exam preparation, assignments, projects and long-term analytical confidence.

Yomna

4.88

(85)

This course is designed to introduce students aged 7 to 16 to the world of programming through two of the most widely used and industry-relevant languages: C++ and Python.

The class provides a structured, age-appropriate pathway into programming, whether the student is a complete beginner or already exploring coding through platforms like Scratch or Code.org. Emphasis is placed on understanding logic, building problem-solving skills, and writing real code in a supportive, project-based environment.

Taught by an engineering student with hands-on experience in both C++ and Python, this course empowers students to explore the power of code and build a strong foundation in computational thinking — essential for future studies in engineering, robotics, AI, or game development.

Aafaq

4.88

(85)

Calculus I, the first course in this extensive mathematics curriculum, teaches students the foundational ideas of limits, derivatives, and how to apply them to real-world issues including rates of change and optimization. Calculus III, which builds on this basis, introduces partial derivatives, multiple integrals, and vector calculus, extending these concepts into several dimensions. When taken as a whole, these calculus courses build the solid analytical foundation and spatial thinking abilities needed for further study in applied mathematics, science, and engineering.

Students study Number Theory concurrently, exploring the complex patterns and characteristics of integers, such as primes, modular arithmetic, divisibility, and the classical theorems that form the basis of much of contemporary computer science and encryption. In addition to this theoretical emphasis, the Numerical Methods course gives students useful computational tools to help them approximate solutions to challenging mathematical problems that are impossible to solve analytically. Students are prepared for a variety of jobs in mathematics, engineering, technology, and other fields by this program, which blends strong theoretical knowledge with algorithmic problem-solving abilities.

Ali

4.88

(85)

I offer one-to-one Programming tuition in Python, C, and C++, for GCSE Computer Science, A-Level Computing, and university students studying engineering, computer science, or related subjects. Lessons are available online or in person around Birmingham.

What I cover:

Python for beginners and intermediate learners
C and C++ programming
GCSE and A-Level Computer Science (all exam boards)
University coursework support, debugging help, and project guidance
Core concepts: variables, loops, functions, data structures, object-oriented programming, file handling, basic algorithms

How I teach:
I start by understanding exactly where you are — whether that's "I've never coded before" or "I'm stuck on a specific assignment." Then I build lessons around small, practical examples you can actually run and modify yourself. I'm patient with errors (everyone gets them), and I make sure you understand the why behind the code, not just how to copy it. For university students, I can also help with debugging, code reviews, and explaining tricky concepts in plain English.
If you or your child is preparing for exams, working on coursework, or just wants to finally feel comfortable with coding, I'd love to help.

Ammar

4.88

(85)

A- TOPICS YOU CAN EXPLORE AND MASTER:
1- PYTHON FOUNDATIONS
• Variables, data types, operators, conditional structures, loops, functions, modules, files, exceptions, and object-oriented programming
• Lists, tuples, dictionaries, sets, comprehensions, debugging, and writing clear, reusable, well-structured code
• Jupyter Notebook, Anaconda, Visual Studio Code, virtual environments, and package management

2 — PROGRAMMING, ALGORITHMS, AND COMPUTER SCIENCE FOUNDATIONS
• Algorithmic thinking, problem decomposition, pseudocode, flowcharts, procedural programming, object-oriented programming, recursion, and modular program design
• Fundamental data structures including arrays, lists, stacks, queues, dictionaries/hash tables, sets, trees, graphs, and their appropriate use
• Searching, sorting, traversal, algorithm efficiency, computational complexity, Big-O notation, debugging, testing, code organization, and problem-solving strategies
• Practical programming exercises ranging from beginner problems to university-level algorithmic and computational challenges

3- DATA PREPARATION AND EXPLORATION
• NumPy and pandas for importing, cleaning, transforming, filtering, grouping, reshaping, and merging data
• Missing values, duplicates, outliers, inconsistent formats, data leakage, and data-quality validation
• Exploratory data analysis using descriptive statistics, Matplotlib, Seaborn, and graphical interpretation

4- MATHEMATICAL FOUNDATIONS
• Linear algebra, vectors, matrices, derivatives, optimization, probability, and statistics
• Loss functions, gradients, distance measures, regularization, likelihood, and model complexity
• Mathematical concepts are explained according to the learner’s level and the requirements of the selected algorithms

5- SUPERVISED MACHINE LEARNING
• Linear and polynomial regression, logistic regression, and regularized models
• k-nearest neighbours, decision trees, random forests, gradient boosting, support vector machines, and Naive Bayes classifiers
• Classification, regression, model assumptions, decision boundaries, feature importance, and interpretation of results

6- UNSUPERVISED LEARNING
• Clustering using k-means, hierarchical clustering, and density-based methods
• Principal component analysis, dimensionality reduction, anomaly detection, and pattern or structure discovery
• Method selection, evaluation of data structure, and interpretation of results without predefined labels

7- MODEL EVALUATION AND IMPROVEMENT
• Training, validation, and test sets; cross-validation; hyperparameter optimization
• Accuracy, precision, recall, specificity, F1 score, ROC–AUC, confusion matrices, MAE, MSE, RMSE, and R2
• Underfitting, overfitting, bias–variance trade-off, class imbalance, feature engineering, feature selection, scaling, and regularization

8- DEEP LEARNING
• Neural-network foundations, activation functions, forward propagation, backpropagation, and gradient descent
• Multilayer perceptrons, convolutional neural networks, recurrent neural networks, and Transformer foundations
• TensorFlow, Keras, or PyTorch depending on the learner’s project and working environment

9- ARTIFICIAL INTELLIGENCE APPLICATIONS
• Natural language processing, text classification, embeddings, sentiment analysis, and foundations of language models
• Computer vision, image classification, fundamental principles of object detection, and image preprocessing
• Recommendation systems, forecasting, anomaly detection, intelligent automation, and decision-support applications

10- GENERATIVE AI AND LARGE LANGUAGE MODELS
• Transformer architecture, tokens, embeddings, attention mechanisms, prompt engineering, Retrieval-Augmented Generation (RAG), and model evaluation
• Use of artificial-intelligence APIs, vector databases, document-retrieval systems, and structured AI-enabled workflows when relevant
• Reliability, hallucinations, bias, privacy, responsible use, and appropriate human validation

11- TOOLS AND LIBRARIES
• Python, NumPy, pandas, Matplotlib, Seaborn, scikit-learn, SciPy, Statsmodels, TensorFlow, Keras, and PyTorch
• Jupyter Notebook, Anaconda, Visual Studio Code, Git, GitHub, SQL, Excel, and Power BI when useful to the project
• Additional libraries may be introduced depending on the selected specialization and dataset

12- PROJECTS, RESEARCH, AND INTERVIEW PREPARATION
• Complete projects covering data preparation, model development, evaluation, interpretation, and presentation of results
• University assignments, dissertations, theses, research projects, portfolio projects, technical interviews, and professional applications
• Code review, debugging, documentation, reproducibility, model comparison, and communication of results

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B- PERSONALIZED TUTORING: LEARNING HOW TO REASON
Machine learning and artificial intelligence become much more accessible when mathematics, algorithms, Python code, data, and real-world applications are clearly connected.

My lessons help you move beyond simply copying code or using models as “black boxes.” You will learn how to define the problem correctly, prepare the data, select an appropriate algorithm, understand how it works, train and evaluate the model, diagnose errors, improve performance, and interpret results rigorously and responsibly.

Each lesson is personalized according to your current level, mathematical background, programming experience, dataset, university work, research project, interview preparation, or professional objective. We begin by identifying your existing knowledge, software environment, expected outcomes, and main conceptual or technical difficulties. We then establish a structured learning plan.

The first lesson combines a discussion of your background, objectives, and tutoring needs; an initial assessment of your current knowledge; personalized planning and organization of future sessions; and a short trial lesson to determine the most effective learning approach.

A typical session may include conceptual explanation, development of mathematical intuition, live coding, guided implementation, model evaluation, technical problem solving, and a concise summary of the next steps.

You may work with your own dataset, university assignment, research project, or professional problem, provided that confidential information is handled appropriately. I can also provide structured examples and datasets suited to your level.

My goal is not simply to help you run an algorithm. It is to help you understand why it is appropriate, how it learns from data, how to evaluate it correctly, why it may fail, and how to build a reliable, interpretable, and scientifically rigorous solution.

Abdelmalek

4.88

(85)

This hands-on training pathway is designed to help students kickstart any project, specifically tailored for OT labs and industrial applications. Starting from absolute scratch, students will build a strong foundation in Python programming through practical, industry-relevant concepts.

Curriculum Outline: |
01 - Python Environment Setup & Basics |
02 - Python Variables, Numbers, Bytes & Hex |
03 - Control Flow Logic Functions |
04 - Data Structures (Lists, Tuples, Dictionaries & Sets) |
05 - String Formatting, Comprehensions & Exception Handling |
06 - File IO, Pathlib & Context Managers |
07 - Object-Oriented Programming (Classes & OOP) |
08 - Standard Library, Modules & Networking Basics |

Assessment & Evaluation:
Students will take a mini-test after the completion of each module. Additionally, an Audit & Performance Evaluation report will be sent following the tests.
Duration:
5 days to 15 days (depending on the pace of the cohort)

Arnaud

4.88

(85)

I work as a data consultant and hold a master's degree in Data Science and Statistics from UCLouvain. I use Python and data manipulation and analysis tools daily in a professional context.

I offer Python and data analysis courses for students, beginners and people wishing to develop practical skills in programming and data.

The lessons are tailored to your level and your goal. We can work on, among other things:

* the basics of Python and programming logic;
* variables, conditions, loops and functions;
* Data manipulation with Python and pandas;
* data cleaning and transformation;
* data exploration and visualization;
* applied statistics;
* solving practical exercises and projects;
* Understanding and debugging your code.

I prefer a practical approach: we start with a concrete problem or dataset, gradually build the solution, and analyze the errors encountered together.

The goal is to understand what you are doing rather than simply reproducing code, in order to gradually become self-reliant.

I can also support a project or student work by explaining the approach, the code and the methods used, without doing the work for you.