Data Science & AI Advance Bootcamp

Learn data analysis, dashboards, SQL, Excel, Python, Power BI, and AI tools to turn data into business insights.

New

Created byJayTech AI Academy

Last updated 7/2026

English

What you'll learn

Learn the foundations of data analysis and how businesses use data to make better decisions.
Understand how to collect, clean, organize, and prepare data for analysis.
Learn how to identify patterns, trends, and insights from business data.
Use AI to generate insights, explain data, summarize reports, and support business recommendations.
Work with real-world datasets from areas such as sales, marketing, operations, finance, customer service, and HR.

Course content

7 sections • 35 lectures • 12h 53m total length

Lesson 1: Advanced Data Preprocessing & Feature Engineering15:34
Lesson 2: Handling-Imbalanced-and-Noisy-Data.11:18
Lesson 3: Feature Selection & Dimensionality Reduction16:00
Lesson 4: Statistical-Testing-and-Experiment-Design18:36
Lesson 5: Pipelines-and-Workflow-Automation21:51

Lesson 1:Ensemble-Methods21:13
Explore ensemble methods: bagging, boosting, and stacking, to boost accuracy and robustness by combining diverse models and reducing variance and bias in real-world finance, healthcare, and e-commerce applications.
Lesson 2: Gradient-Boosting25:18
Lesson 3: Hyperparameter-Optimization26:46
Lesson 4:Model-Interpretability25:15
Explore model interpretability with SHAP and LIME, understand, explain, and deploy transparent AI, combining local and global explanations for trusted, compliant production systems.
Lesson 5: Model-Evaluation-and-Validation-Strategies24:36
Master model evaluation and validation strategies for reliable, production-ready AI. Learn key metrics like accuracy, precision, recall, F1, and AUC ROC, plus train-test split, cross-validation, stratified k-fold, and time-series validation.

Lesson 1: Neural-Network-Architecture-Design20:22
Explore neural network architecture design and how depth versus width shapes learning and performance. Apply practical strategies with activation functions, forward propagation, regularization, and standard baselines to build efficient models.
Lesson 2: Backpropagation-and-Optimization-Techniques20:32
Explore how backpropagation and optimization techniques enable neural networks to learn from errors and update weights. Discover gradient flow, loss functions, and modern optimizers that improve training stability and performance.
Lesson 3: Regularization-Methods15:06
Lesson 4: Image-Processing23:03
Explore how convolutional neural networks transform raw pixel data into meaningful predictions through automatic feature extraction. Learn about pooling, end-to-end training, and real-world applications in healthcare, autonomous driving, and security.
Lesson 5: RNN-LSTM-and-GRU-for-Sequential-Data18:50

Lesson 1:Computer-Vision-Systems27:09
Lesson 2: CNN-Architectures21:42
Lesson 3:Object Detection20:23
Lesson 4:Image-Segmentation-Techniques19:06
Lesson 5:Real-World-Computer-Vision-Applications22:56
Explore real-world computer vision applications across autonomous vehicles, healthcare, retail, manufacturing, and security, and learn how visual intelligence enables real-time decision making and scalable impact.

Lesson 1: Time-Series-Decomposition25:56
Master time series decomposition to break data into trend, seasonality, cyclical, and residual components, enabling clearer forecasts and better decision-making across finance, retail, healthcare, and IoT.
Lesson 2: ARIMA-and-SARIMA-Models22:11
Lesson 3: Prophet-Model-for-Forecasting26:15
Lesson 4: LSTM-for-Time-Series-Prediction20:52
Learn LSTM for time series, exploring gates and memory mechanisms for forecasting and anomaly detection. Build and prepare data as 3D input tensors to enable accurate predictions.
Lesson 5:Anomaly-Detection-in-Time-Series22:57

Requirements

No prior data analysis or AI experience is required. This course is designed to guide students step by step.
Basic computer skills are recommended, including using a browser, managing files, working with online tools, and installing software when needed.
A laptop or desktop computer with a reliable internet connection is required.
Basic knowledge of Microsoft Excel or Google Sheets will be helpful, but it is not mandatory.

Description

“This course contains the use of artificial intelligence.”

Data is one of the most valuable assets in today’s business world. Organizations need professionals who can collect data, clean it, analyze it, visualize it, and use it to make better decisions. At the same time, artificial intelligence is changing how data analysis is done by making it faster, smarter, and more practical.

This Data Analysis and AI Program is designed to help students learn how to analyze data and use AI tools to turn raw information into meaningful business insights. The course is practical, beginner-friendly, and focused on real-world business use cases.

Students will learn the foundations of data analysis, including data collection, data cleaning, data organization, data visualization, reporting, dashboards, and business intelligence. They will also learn how to use popular tools such as Excel, Google Sheets, SQL, Python, Power BI, and modern AI platforms to analyze and present data effectively.

The course will also show students how to use AI to improve the data analysis process. Students will learn how to use AI tools to clean data, summarize information, generate insights, explain trends, create reports, support decision-making, and improve productivity.

Throughout the program, students will work with real-world datasets from areas such as sales, marketing, finance, operations, customer service, healthcare, education, and human resources. They will learn how to find patterns, identify problems, communicate insights, and make data-driven recommendations.

By the end of this course, students will have the practical skills needed to work with data confidently. They will also build portfolio-ready projects that can support job applications, freelance work, consulting services, or business growth.

This course is ideal for beginners, career changers, business professionals, entrepreneurs, consultants, students, and anyone who wants to learn data analysis with AI and use data to solve real business problems.

Who this course is for:

This course is for beginners who want to learn data analysis and AI from the ground up.
This course is for people who want to become Data Analysts, AI Data Analysts, Business Analysts, or AI Consultants.
This course is for career changers who want to enter the data and AI field without needing advanced math or programming experience.
This course is for entrepreneurs and small business owners who want to understand their business data, find insights, and improve performance.
This course is for anyone who wants to build portfolio-ready data analysis projects and gain the confidence to work with real-world business data.

Data Science & AI Advance Bootcamp

What you'll learn

Explore related topics

Course content

Module 1: Data Engineering For Machine Learning5 lectures • 1hr 23min

Module 2: Advance Machine Learning Modeling & Optimization5 lectures • 2hr 3min

Module 3: Deep Learning and Neural Network5 lectures • 1hr 38min

Module 4: NLP Transformer & Large Language Models5 lectures • 2hr 7min

Module 5: Computer Vision & Image Intelligence5 lectures • 1hr 51min

Module 6: Time Series Analysis & Forecasting5 lectures • 1hr 58min

Module 7: MLOps & AI Model Deployment5 lectures • 1hr 53min

Requirements

Description

Who this course is for: