Data Scientist Bootcamp with AI - A unique learning and certification program!

Data Scientist Bootcamp with AI

A unique learning and certification program!

This data science bootcamp is a comprehensive online training program designed to help aspiring professionals build job-ready skills in Data Science, Machine Learning, Generative AI, and analytics. The course combines live instructor-led sessions, hands-on labs, real-world projects, and capstone assignments to provide practical exposure to modern data science workflows.

Learners gain expertise in essential technologies such as Python, SQL, Machine Learning, Deep Learning, Power BI, TensorFlow, and Generative AI tools including LLMs and prompt engineering. The curriculum also covers advanced topics such as MLOps, Azure Machine Learning, Microsoft Fabric, and data visualization techniques to prepare participants for real-world business challenges.

With industry-aligned projects, expert mentorship, and flexible online learning, this program is ideal for professionals looking to transition into high-demand data science and AI careers.

Key Features

• Course and material in English
• Beginner - Advanced level for aspiring professional
• 11 months long live online bootcamp and eLearning (self-paced)
• Class is held mostly every weekend
• 200+ hours live instructor-led class
• 23 hours eLearning video content
• 1 year access to self-paced eLearning content & class recordings
• 240 hours study time recommendation
• Integrated labs for hands-on learning experience
• Bonus Microsoft courses
• 12+ real-world industry projects,
• 15+ tools, including Python, Scikit-learn, SciPy, NumPy, Pandas, Keras, PyTorch, TensorFlow, and Power BI
• Certification for each courses and Bootcamp certification upon completion

Outcomes of the Program

• Understand Generative AI concepts including neural networks, GANs, and transformers
• Learn data structures, data manipulation, and preprocessing techniques
• Build supervised and unsupervised machine learning models
• Apply regression, classification, clustering, and dimensionality reduction methods
• Visualize data and create interactive dashboards using Power BI
• Develop expertise in Deep Learning, MLOps, Azure ML, and Fabric ML
• Explore prompt engineering, LLMs, RAG, conversational AI, and chatbots
• Gain hands-on experience with leading AI and data science tools
• Use NumPy and Scikit-learn for mathematical and analytical computing
• Build and optimize deep learning models with Keras and TensorFlow
• Analyze real-world AI and MLOps use cases
• Learn Python, SQL, statistics, machine learning, and applied data science through practical projects

Tools covered

• Python
• Scikit Learn
• PowerBI
• Numpy
• PyTorch
• Pandas
• Seaborn
• SciPy
• MySQL
• Matplotlib
• TensorFlow
• Keras

Skills Covered

• SQL and Database Management
• Core Python Programming
• Data Analysis and Data Manipulation
• Exploratory Data Analysis (EDA)
• Descriptive and Inferential Statistics
• Explainable AI and Conversational AI
• Large Language Models (LLMs)
• Model Development and Fine-Tuning
• Ensemble Learning Techniques
• Data Visualization
• Deep Learning Frameworks
• Supervised and Unsupervised Learning
• Generative AI and LLM Applications
• MLOps and AI Deployment Practices
• Applied Data Science Concepts

Target Audience

• Aspiring Data Scientists and AI professionals
• Software Developers transitioning into Data Science
• Business Analysts and Data Analysts
• IT professionals looking to upskill in AI and Machine Learning
• Fresh graduates seeking a career in Data Science
• Engineers and technical professionals interested in analytics
• Professionals working with data who want advanced analytical skills
• Anyone interested in building practical expertise in Machine Learning and Generative AI

Prerequisites

There are no strict prerequisites for joining this course. However, the following knowledge can be beneficial:

• Basic understanding of mathematics and statistics
• Familiarity with programming concepts
• Interest in analytics, AI, or Machine Learning
• Prior exposure to Excel or databases is helpful but not mandatory
• The course includes foundational modules such as Python refresher and SQL basics, making it beginner-friendly.

Learning Path

1. Python Refresher With AI
2. SQL Certification Course
3. Advanced Statistics
4. Applied Data Science with Python
5. Machine Learning using Python
6. Deep Neural Networks (TensorFlow, Keras, Pytorch)
7. Data Science Capstone

Optional Courses - Bonus Material

• Master in Generative AI
• Microsoft Power BI With AI Assistance
• Applied MLOps
• End-to-End MLOps With Azure Machine Learning
• Machine learning for AI in Microsoft Fabric

Learning Path details

Course 1: Python Refresher (with AI)

Build a strong foundation in Python programming with the Python Refresher with AI course. Ideal for beginners, this course introduces essential concepts such as data types, loops, functions, and exception handling. Gain practical experience using Python, one of the most widely used languages in data science and improve your ability to manage and streamline analytics tasks confidently.

Key learning objectives

• Get an introduction to Python programming
• Learn Python data types and operators
• Explore conditional statements and loops
• Understand error and file handling
• Explore various Python functions
• Learn the practical use of AI code generation tools

Topics Covered

• Data structures, functions, OOP principles
• Python Fundamentals with AI

Course 2: SQL Certification Course

Join this course to build the essential skills needed to work confidently with SQL databases and integrate them into applications. You will learn key SQL concepts including queries, conditional statements, joins, subqueries, commands, and database functions, enabling you to efficiently manage and scale database systems.

Key learning objectives

• Master table creation, updates, and basic queries
• Apply joins (left, right, inner, full) and functions effectively
• Use subqueries, nested queries, and user-defined functions
• Optimize SQL queries for better performance

Topics Covered

• Creating and updating tables
• SQL joins (Left, Right, etc.)
• SQL functions and subqueries
• Nested queries and user-defined functions
• SQL optimization techniques

Lesson 3: Advanced Statistics

This module builds the mathematical and statistical foundation required for data science and machine learning. Topics include probability, linear algebra, hypothesis testing, and key statistical concepts such as measures of central tendency, dispersion, skewness, covariance, and correlation. Learners will also explore techniques like Z-tests, T-tests, ANOVA, and data manipulation using Pandas.

Key learning objectives

• Understand statistical modeling and distributions
• Apply hypothesis testing and linear algebra
• Prepare for machine learning math requirements

Topics Covered

• Probability distributions
• Hypothesis testing
• Linear algebra
• Statistical modeling

Lesson 4: Applied Data Science with Python

This course provides a comprehensive introduction to core data science concepts, including data preparation, data wrangling, model development, and evaluation techniques. Participants will also learn essential Python concepts such as strings, lists, and lambda functions while gaining hands-on experience with visualization libraries like Matplotlib and Seaborn to create meaningful data insights.

Key learning objectives

• Load, clean, and transform data
• Create visualizations and basic models
• Use Pandas, Matplotlib, Scikit-learn proficiently

Topics Covered

• Introduction to Data Science
• Essentials of Python Programming
• NumPy
• Working with pandas
• Data AnalysisData Wrangling
• Data Visualization
• End-to-End Statistics Applications in Python

Course 5 - Machine Learning using Python

This course provides a comprehensive overview of different machine learning approaches and their real-world applications. Participants will learn the complete machine learning pipeline, including supervised learning, regression techniques, and classification algorithms. The course also covers unsupervised learning methods, clustering techniques, and ensemble modeling concepts.

Key learning objectives

• Build and evaluate ML models
• Understand supervised/unsupervised techniques
• Apply Algorithm in TensorFlow,Keras, PyTorch for applications

Topics Covered

• Machine Learning
• Supervised Learning
• Regression and its Applications
• Classification and its Applications
• Unsupervised Learning
• Ensemble Learning
• Recommendation Systems

Course 6 - Deep Neural Networks (TensorFlow, Keras, Pytorch)

Learn how to build and deploy deep learning solutions using industry-leading AI and machine learning frameworks while exploring both foundational concepts and practical applications. Gain a clear understanding of the differences between machine learning and deep learning, study neural networks, backpropagation, TensorFlow 2, and Keras, and improve model accuracy and interpretability. The course also covers CNNs, transfer learning, RNNs, autoencoders, PyTorch neural networks, and techniques for building and optimizing models with Keras and TensorFlow.

Key learning objectives

• Deploy DL models with TensorFlow, Keras, PyTorch
• Apply CNNs, RNNs, transfer learning
• Improve model performance and interpretability

Topics Covered

• Deep Learning fundamentals
• Artificial Neural Networks
• TensorFlow and PyTorch Frameworks
• Model optimization and performance techniques
• CNNs, Transfer Learning, Object
• Detection, RNNs, Transformers, and Autoencoders

Course 7 - Data Scientist Capstone

The Data Science capstone project allows you to apply the knowledge and skills gained throughout the program in a practical, real-world scenario. The project covers key areas such as data processing, model development, and presenting business insights and results. As the final stage of the learning journey, it helps showcase your data science capabilities and strengthen your professional portfolio for future career opportunities.

Key learning objectives

• Data Processing: Utilizing various techniques to transform raw data into meaningful insights
• Model Building: Employing techniques such as regression and decision trees to create accurate and intelligent machine learning models capable of making predictions
• Python or SAS: Developing your model and conducting a complete model-building exercise, including data splitting, testing, and validating data using the k-fold cross-validation process
• Model Fine-Tuning: Applying various techniques to enhance the model’s accuracy and selecting the best performing champion model
• Dashboarding and Result Presentation: Using Power BI with AI to create a dashboard with meaningful insights to present your final results

Optional courses - Bonus material

1. Master in Generative AI

Develop a solid understanding of Machine Learning and Generative AI by exploring supervised, unsupervised, and reinforcement learning techniques. Learn the fundamentals of generative models such as neural networks, GANs, and transformers, and understand how LLMs and chatbots operate. The course also introduces image and video generation tools, the open-source AI ecosystem including Hugging Face, and practical prompt engineering techniques for chatbot and image generation applications.

Key learning objectives

• Machine Learning and Generative AI Basics
• Types of Machine Learning: Supervised, Unsupervised and Reinforcement Learning
• Generative AI Algorithms: Neural Networks, GANs, Transformers
• Large Language Models and Chatbots
• Image/Video Generation
• Open Source AI Models & Prompt Engineering

Topics Covered

• Introduction to GenAI and applications
• GenAI Algorithms
• Image/Video Generation
• Open Source Landscape
• GPTs
• GenAI in Businesses

2. Machine Learning for AI in Microsoft Fabric

The Machine Learning for AI in Microsoft Fabric course provides practical experience in creating complete machine learning workflows using Microsoft Fabric. Participants will learn how to preprocess data with Data Wrangler, train and monitor models using MLflow, and generate batch predictions from deployed models, building essential skills in data preparation, model tracking, and ML deployment.

Key learning objectives

• Preprocess data with Data Wrangler
• Train/track models using MLflow
• Generate batch predictions

Topics Covered

• Data wrangling in Fabric
• MLflow for models
• Deployed model predictions

3. Applied ML Ops

This elective introduces the MLOps lifecycle and teaches how to build scalable, automated, and collaborative machine learning workflows. The course focuses on CI/CD practices, Infrastructure as Code (IaC), and AWS deployment while helping learners design and manage pipelines for continuous training, testing, versioning, and deployment of ML models.

Key learning objectives

• Implement MLOps lifecycle
• Design CI/CD pipelines
• Monitor models with AWS services

Topics Covered

• MLOps lifecycle
• CI/CD, versioning
• IaC, experiment tracking
• AWS SageMaker, CloudWatch

4. End-to-End MLOps With Azure Machine Learning

The End-to-End MLOps with Azure Machine Learning course teaches learners how to build scalable and production-ready AI workflows using Azure. Participants will use Azure ML services to create MLOps pipelines, automate model training and deployment with GitHub Actions, and deploy AI models efficiently and reliably. By the end of the course, learners will be able to manage, automate, and scale machine learning solutions using industry-standard MLOps practices.

Key Learning Objectives

• Build scalable Azure pipelines
• Trigger jobs with GitHub Actions
• Deploy AI models robustly

Topics Covered

• Azure ML services
• MLOps pipelines
• GitHub Actions deployment

5. Microsoft Power BI With AI Assistance

The Power BI with AI course helps learners develop practical skills for transforming data into meaningful insights. Participants will learn how to connect, clean, and model data using Power Query and DAX, create interactive dashboards and reports, and implement data security best practices. The course also covers AI-powered features such as Quick Measures to improve efficiency, along with storytelling-based projects designed to present data effectively.

Key Learning Objectives

• Connect, clean, model data with DAX
• Build dashboards and reports
• Use AI features for performance

Topics Covered

• Power Query, DAX
• Visualizations, security
• AI tools like Quick Measures
• Storytelling projects

Projects

• Sales Analysis
• Employee Performance Mapping
• Air Cargo Analysis
• Classification of Songs
• Marketing Strategies with Exploratory Data Analysis
• Analyzing Customer Orders Using Python
• Building a Python Adventure Game With GitHub Copilot
• Data Manipulation and Reporting With Power BI
• Predicting Restaurant Tips
• Anomaly Detection in Credit Card Transactions
• Patient Readmission Prediction
• Predicting Customer Purchase Behavior

QUESTIONS AND ANSWERS

How long does it take to complete Bootcamp?

Thanks to the combination of e-learning and live online bootcamp, the program normally takes 11 months (5–10 hours/week).

However, you can complete it faster upon request. Don't hesitate to contact us for a better solution!

Some people can go through the program fairly quickly (about 3 months), while others need more time. Note: Some other programs take longer. This is an estimate.

You will have access to the program's e-learning videos and recorded lessons for 365 days.

What is the format of the Bootcamp?

The programs are entirely distance learning courses. The components are practical e-learning courses that you can complete at your own pace and at your convenience, and which you can also access from your mobile phone (our app).

There are also online classroom sessions via our advanced professional distance learning system. We have a range of time slots to choose from, and we always record the sessions so you can listen to them if you miss anything or want to review information. There is always someone on hand to help and support you if you have any questions about the skills you are learning.

When can I take Bootcamp live online courses?

The timing of each course varies for different groups. You will be given access to a dashboard with a number of different time slots for the same session/topic. You decide which date and time suits you best. Some are scheduled on weekday afternoons, while others are scheduled on weekend mornings or evenings. The schedule is based on factors such as the number of interested participants and the availability of instructors. If you miss a session, you can always watch recordings of that session.You will never miss anything!

When can I unlock my Master Certificate?

You must complete at least 85% of the course to unlock your certificate. This applies to all master programs. One of the criteria for obtaining the Master Certificate is to participate in the live courses. However, if you are unable to participate live but can watch the recordings, we can make an exception. It is important that you watch the recordings if you are unable to participate in the live sessions.

What is a Bootcamp for Data Scientists?

Data science courses are educational programs designed to provide students with the skills and information necessary to use programming, statistics, machine learning, and domain expertise methods to analyze, evaluate, and extract valuable insights from large and complex data sets. In this data science course, you will learn about many concepts of varying complexity—from beginner to intermediate and advanced levels.

What does a data scientist do?

A data scientist is someone who collects, cleans, analyzes, and visualizes large amounts of data to draw meaningful conclusions and communicate them to business leaders. This data is collected from various sources, processed into a format suitable for analysis, and fed into an analytics system where statistical analysis is performed to gain actionable insights.

Such actionable insights help solve complex business problems and make better decisions. Data scientists use data science techniques such as exploratory data analysis, statistical modeling, and machine learning to discover hidden patterns in data. If you want to pursue a career as a data scientist, this data science course can help you handle all of these responsibilities.

Is coding experience required?

No advanced coding experience is required. The program includes beginner-friendly programming modules to help learners build their coding skills gradually.

Does the course cover Generative AI?

Yes. The curriculum includes Generative AI concepts such as ChatGPT, LLMs, Retrieval-Augmented Generation (RAG), and prompt engineering.

What tools and technologies are covered?

The course covers Python, SQL, TensorFlow, Keras, PyTorch, Power BI, Tableau, Azure ML, Microsoft Fabric, and Generative AI tools including LLMs and prompt engineering.

Who are the instructors for this course? How are they selected?

Our highly qualified data science instructors are industry experts with years of relevant experience in machine learning, Python for data science, and applied data science.

All have gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before being certified to teach for us. We also ensure that only trainers with high alumni ratings remain on our faculty.

What will be the career path after completing the Data Scientist Bootcamp?

Organizations across industries rely heavily on data-driven decision-making for competitive growth. This shift has made the role of data scientist one of the most thriving career options in the current job market. Qualified data professionals who can analyze and interpret complex data are in high demand. You can expect competitive salaries, growth opportunities, and the chance to work with cutting-edge technology.

Completing the data science course from AVC opens up several promising career paths, including positions as a data scientist, data analyst, machine learning engineer, or business intelligence analyst.

Roles such as data engineer in specialized areas such as Natural Language Processing (NLP) or computer vision are also viable options. These careers span various industries such as IT, finance, healthcare, and retail.

Can recent graduates apply for jobs after completing this Data Scientist Bootcamp?

Data scientists are in high demand today, and companies are willing to pay higher salaries for entry-level positions. However, you need to demonstrate deep data science knowledge and gain industry exposure to become a data scientist. Our data science course equips recent graduates with all the necessary skills, making them industry-ready to become successful data scientists.

This online data science program includes applied data science assignments and real-world data science projects, making it an incredible option to start your journey in data science.

Which industries use data science the most?

Data science finds applications in major industry sectors, such as healthcare, banking and finance, retail, automotive, marketing, manufacturing, and government. Industries such as technology, advertising, energy, and utilities, among others, also employ many data scientists. This data science certification course is beneficial if you want to enter any of these sectors as a professional.