DSAI
Ranked #1 International Training Institute Rated 4.9/5 by 15k+ Students Learn Advance Machine Learning and GenerativeAI

Data Science and Artificial Intelligence

Embrace the future of Data Science and AI with this comprehensive data science course program, combining the best of both worlds: essential Data Science knowledge and in-demand Generative AI (Gen AI) skills. This data science course features special capstone projects, guest sessions from industry experts, masterclasses, and live BIA® DoubtBuster sessions.

Get hands-on experience with data science course including Python, Machine Learning, Deep Learning, Natural Language Processing (NLP), MLOps, Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Attention, Transformers, BERT, and Business Intelligence Tools.
BIA® is highly rated at 4.9-star and is ranked #1 International Data Science Training Institute by British Columbia Times, Business World, Avalon Global and several recognized forums, making it a one-stop shop for mastering most sought-after skills and the latest AI technologies.

ENQUIRE NOW DOWNLOAD BROCHURE

    Talk to Our Expert

    Please share your details and we will reach out to you soon..

    Dual Certification: Data Science & Artificial Intelligence

    The Two Most In-Demand and Highly Paid Skills

    Ranked #1 International Training Institute

    In 2023, by British Columbia Times, Business World & Others

    3 Learning Paths

    Data Science Certification (4 Months) | Data Science Diploma (6 Months) | Data Science Master Diploma (10 Months)

    15000+ Learners

    Data Science & AI Trained Students Across 105+ Campus in 7+ Countries

    360° Career Support

    Resume Building, Interview Prep, and Access to Partner Companies

    Data Science and Artificial Intelligence Course Overview

    Key Highlights

    Immersive Data Science Course Training Classroom Experience

    Hands-on Training By Industry Experts

    15+ Industry Case Studies and Assignments

    Data Science Course Modules Integrated with Generative AI

    Exclusive Data Science Job Opportunities Portal

    BIA® Alumni Status

    Globally Recognized Dual Certification

    Real World Projects and Case Studies

    In-person Career Mentorship Sessions (1:1)

    200+ Hours of Learning & Practical Exercises

    360 Degree Career Support

    Live Data Science Course BIA® DoubtBuster Sessions

    350+ Corporate Partners

    Immersive Plus Online Blended Learning

    Practical Hands-on Capstone Projects

    In-person Job Interview Preparation (1:1)

    30+ Programming Tools & Technologies

    Access to Top MNC Companies

    No Cost EMI Options Available

    BIA® Alumni Status

    Syllabus for Data Science Course and Artificial Intelligence

    Delve into 200+ hours of learning and hands-on exercises with our expertly crafted content. Our data science course modules are always up-to-date and seamlessly integrated with Generative AI. Developed by top industry experts, this data science course ensures a cutting-edge learning experience.

    DOWNLOAD BROCHURE

    Topics That Will Keep You Engaged and Curious

    Python, Machine Learning, Deep Learning, Natural Language Processing (NLP), MLOps, Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Attention, Transformers, BERT, and Business Intelligence Tools

    Your Roadmap to Learning

    Start with the basics, then move to advanced topics step by step. This prevents feeling overwhelmed and helps you remember and easily grasp new concepts. Each topic builds on what you’ve learned, forming a solid foundation for understanding more in data science course.

    Ideal Candidates for Data Science Course

    In the era of cutting-edge tools and AI, BIA’s data science course is open to everyone. Whether you’re a student pursuing Commerce, Arts, Banking, Engineering, or Science, you can dive into the world of data science. For professionals eyeing a shift into this field, this data science course is a great stepping stone. Business and IT professionals eager to boost their data skills will find valuable insights on this learning path.

    Minimum Eligibility for Data Science Course

    In today’s data science skill-based job market, degrees matter less than a passion for learning. Even a high school diploma is enough to kickstart this journey. Your love for data and enthusiasm for leveraging new-age technology will propel your career forward. Gain data science skills that not only open doors but also create new professional horizons.

    Job Opportunities After Data Science Course

    Data Scientist, Machine Learning Engineer, Deep Learning Engineer, Data Mining Specialist, AI/ML Model Validator, AI Research Scientist, Data Analyst, Data Visualization Specialist, AI Product Manager, AI Chatbot Designer, Big Data Engineer, AI Consultant, Quantitative Analyst, AIOPS Specialist, NLP Engineer, Computer Vision Engineer, Business Intelligence Analyst, Conversational AI Developer, Algorithm Developer, AI Solution Architect

    Industries That Are Hiring Data Scientists and AI Specialists

    The demand for Data Scientists and AI specialists spans across diverse sectors, including Technology, Finance, Banking, Healthcare, E-commerce, Retail, Telecom, Aerospace, Marketing, Entertainment, Sports and more, reflecting the broad applicability and impact of these technologies.

    Globally Accredited Data Science Course in India

    Unlock the realm of Data Science with Boston Institute of Analytics’ premier Data Science course in India, renowned as the most comprehensive data science training institute in India. Delve into the entire Data Science lifecycle, encompassing Data Collection, Extraction, Cleansing, Exploration, Transformation, and beyond. Explore a vast array of skills and tools, from Statistical Analysis to Text Mining, Regression Modeling to Deep Learning, all meticulously covered in our data science course curriculum in India.
    At Boston Institute of Analytics, we go beyond mere data science training – we offer a pathway to success. Join us in India, and embark on a journey to become a sought-after Data Science professional in India. Discover BIA is regarded as the best Data Science training institute in India and beyond.

    Why Should You Choose Boston Institute of Analytics For Data Science Course in India?

    Embark on your journey towards a rewarding career in Data Science with Boston Institute of Analytics, recognized as one of India‘s premier data science training institutes. With a proven track record of shaping the careers of numerous Data Science professionals, both locally and internationally, we stand as a beacon of excellence in the field. Benefit from our expert trainers, each possessing over 15 years of professional experience in the industry. Our dual data science certification in Data Science & Data Analytics, widely regarded as the best in the industry, is tailored to equip you with the skills and knowledge needed to excel in the competitive landscape of Data Science in India. At Boston Institute of Analytics, we offer a blended learning model that combines data science classroom sessions, instructor-led online sessions, and e-learning modules. Our dedicated data science placement cell and extensive network of 350+ corporate partners ensure that you receive ample opportunities for interviews and data science placement assistance. Whether you’re a seasoned professional looking to upskill or a fresh graduate aspiring to kickstart your data science career in India, our comprehensive Data Science course in India is designed to meet your needs and exceed your expectations. Join us today and take the first step towards unlocking your full potential in the dynamic field of Data Science in India.

    20+ Programming Tools, Libraries & Technologies Covered

    10+ Generative AI Tools, Libraries & Technologies Covered

    BIA® Dual Certification In Two Most In-Demand And Highly Paid Skills

    Data Science

    Data Science Training Course Certification
    Data Science Training Course Certification – BIA

    +

    Artificial Intelligence

    Explore our AI Powered Centralized Learning Hub

    Our AI powered Learning Management System (LMS) is a centralized hub designed to enhance your data science training course experience. It provides seamless access to study materials, lecture recordings, personalized progress tracking, assignment submission, and automated grading. Additionally, it offers access to BIA® DoubtBuster to help clear any doubts you may have.

    ENQUIRE NOW
    Data Science Training Course LMS

      Talk to Our Expert

      Please share your details and we will reach out to you soon..

      By submitting the form, you agree to our Terms and Conditions and our Privacy Policy.

      Comprehensive Curriculum

      Our data science course curriculum, meticulously crafted and delivered by Industry-Expert Trainers, offers a dynamic fusion of academic depth and real-world know-how.

      Know Your Trainer

      A seasoned pro in the very field you’re passionate about. Beyond learning, this is your golden ticket to tap into their years of hands-on expertise. See your trainers as more than a guide; envision them as a gateway to the industry you’re eager to join.

      200+ Hours

      Learning & Practicals

      15+

      Projects & Case Studies

      30+

      Tools & Technologies

      DOWNLOAD BROCHURE

      Data Science Course and AI Foundation: Orientation

      • Introduction to the Data Science Course
      • Importance of Data Science and AI
      • Fundamentals of Data Science 
      • Introduction to Artificial Intelligence
      • Installing Anaconda
      • Setting up Jupyter Notebooks
      • Setting-up Power BI and Tableau Account
      • Introduction to Excel Environment
      • Overview of the Course Modules 
      • Brief on Assignments and Assessments
      • Digital Platforms and Resources
      • Communication Channels

      Mastering MS Excel

      •  2 Quizzes
      • 1 Project
      • Overview of Excel Interface 
      • Key Formulas and Functions
      • Ranges and Tables 
      • Data Cleaning – Text Functions, Dates and Times 
      • Conditional Formatting
      • Sorting and Filtering
      •  2 Quizzes
      • 1 Project
      • Pivots
      • Data Analysis in Excel – Trends and Patterns
      • Data Visualization in Excel – Charts and Plots
      • Working With Multiple Worksheets
      • Linking and Referencing the Data Between Worksheets

      Python for Data Science

      •  2 Quizzes
      • 1 Project
      • Overview of Python
      • Understanding Statements, Expressions and Indentation
      • Overview of Identifiers, Keywords and Comments
      • Variables: Declaration, Assignment and Naming Conventions
      • Common Data Types: Integers, Floats and Strings
      • Type Casting and Conversion
      • Operators in Python
      • Hands-on Activity
      •  2 Quizzes
      • 1 Project
      • Loop Control Statements: Break, Continue andPass
      • Defining and Calling Functions
      • Function Parameters and Return Values
      • Scope of Variables (Global and Local)
      • Advanced Functions
      • Default Values and Variable-Length Arguments
      • Recursive Functions
      • Map, Reduce and Filter
      • Introduction to Exceptions
      • Try, Except and Finally Blocks
      • Handling Common Errors
      • Hands-on Activity
      •  2 Quizzes
      • 1 Project
      • Basic Operations on Lists
      • Demonstration of List Manipulation Techniques
      • Slicing and Indexing in Lists
      • List Comprehension for Concise and Readable Code
      • Tuples Creation
      • Basic Operations on Tuples
      • Slicing And Indexing in Tuples
      • Common Operations on Both Lists and Tuples
      • Hands-on Activity
      •  2 Quizzes
      • 1 Project
      • Basic Operations on Dictionaries
      • Manipulating Dictionaries
      • Dictionary Comprehension for Concise Creation
      • Creation of Sets
      • Manipulating Sets
      • Common Operations on Both Dictionaries and Sets
      • Hands-on Activity
      •  2 Quizzes
      • 1 Project
      • Intro To Numpy and Creating Numpy Array
      • Basic Operations on Arrays
      • Indexing and Slicing
      • Reshaping, Stacking and Splitting
      • Iteration, Filtering and Boolean Indexing
      • Image Processing Using Numpy and Matplotlib
      • Hands-on Activity
      •  2 Quizzes
      • 1 Project
      • Data Structure in Pandas
      • Creating Dataframe and Loading Files
      • Data Exploration (EDA)
      • Creating and Saving Basic Plots Using Matplotlib
      • Creating Statistical Plots Using Seaborn
      • Exploring Relationships in Data: Pair Plot and Heat Map
      • Hands-on Activity

      SQL for Data Science

      •  2 Quizzes
      • 1 Project
      • SQL and Its Significance
      • SQL’S Role in Data Retrieval and Manipulation
      • Select Statement for Data Retrieval
      • Retrieving Specific Columns and All Columns
      • Using Distinct to Remove Duplicates
      • Data Models & ER Diagrams
      • Relational Vs. Transactional Models
      • Organizing Data in Tables
      • Filtering Data with Where Clause
      • Sorting Data with Order By
      • Limiting Results with Limit
      • Using Aliases for Column Names
      • Hands-on Activity
      •  2 Quizzes
      • 1 Project
      • Creating and Using Temporary Tables
      • Adding Comments to SQL Code for Documentation
      • Introduction to Data Modeling
      • Designing A Database Schema
      • Sorting Data with Order By (Advanced)
      • Advanced Filtering (With In, Or, And, Not)
      • Performing Mathematical Operations on Data
      • Introduction to Aggregate Functions (Count, Sum, Avg, Max, Min)
      • Grouping Data with Group By
      • Filtering Grouped Data with Having
      • Understanding Subqueries and Their Types
      • Performing Join Operations (Inner Join, Left Join, Right Join, Full Outer Join)
      • Updating and Deleting Data with SQL
      • Analyzing Data with Statistics
      • Hands-on Activity

      Application of Statistics and Probability

      •  2 Quizzes
      • 1 Project
      • Define Statistics and Its Importance
      • Explain The Types of Data: Categorical and Numerical
      • Inferential and Descriptive Statistics​
      • Measure Of Central Tendency: Mean, Median, Mode
      • Measure Of Dispersion: Variance and Standard Deviation
      • Probability Basics, It’s Rules and Notation
      • Probability Distribution – Discrete and Continuous
      • Normal Distribution and Properties
      • Central Limit Theorem and Its Importance
      • Skewness and T-Distributions
      •  2 Quizzes
      • 1 Project
      • Hypothesis Testing – Null and Alternative
      • Significance Level (Alpha) and P-Value
      • One-Sample and Two-Sample T-Test
      • Visualization Plots for Data Exploration
      • Interpretation of Visualization
      • Correlation and Regression
      • Confidence Interval
      • Hypothesis Testing With Z-Test
      • Chi-Square Test for Categorical Data
      • One-Way and Two-Way Anova

      Explore Supervised Machine Learning

      •  2 Quizzes
      • 1 Project
      • Intro to ML & Its Role in Data Analysis
      • Types of Machine Learning – Supervised, Unsupervised and Reinforcement 
      • Data Pre-processing Methods
      • Feature Scaling
      • Linear Regression as Regression Technique
      • Simple Linear Regression
      • Hands-on Activity
      •  2 Quizzes
      • 1 Project
      • Model Evaluation Metrics for Regression
      • Mean Absolute Error (MAE)
      • Mean Squared Error (MSE)
      • Root Mean Squared Error (RMSE)
      • R-Squared (Coefficient of Determination)
      • Multiple Linear Regression
      • California Housing Dataset – Model Evaluation
      • Hands-on Activity
      •  2 Quizzes
      • 1 Project
      • Overview of Logistic Regression
      • Binary Classification Problem and Logit Function and Odds Ratio
      • Binary & Multi-class LR
      • Classification Matrix: Accuracy, Precision, Recall and F1-Score
      • Confusion Matrix Interpretation
      • ROC Curves & AUC
      • Hands-on Activity
      •  2 Quizzes
      • 1 Project
      • Decision Tree and Its Structure
      • Decision Nodes and Leaf Nodes, Parent/Child Node
      • Splitting Criteria – Gini Impurity and Entropy
      • Tree Pruning and Overfitting
      • Techniques to Prevent Overfitting
      • Random Forest – Ensemble Learning and Bagging
      • Gradient Boosting And AdaBoost Ensemble Method
      • Hands-on Activity
      •  2 Quizzes
      • 1 Project
      • K-Fold Cross-Validation for Model Evaluation
      • Hyper-parameter Tuning Using Grid Search
      • Detailed Coverage of Classification Metrics
      • Precision, Recall, F1-Score, ROC Curves, AUC
      • Interpretation and Practical Usage
      • Hands-on Activity

      Explore Unsupervised Machine Learning

      •  2 Quizzes
      • 1 Project
      • K-Means Clustering and Its Applications
      • K-Means Algorithm
      • Choosing the Number of Clusters (K)
      • Introduction to Hierarchical Clustering
      • Agglomerative Hierarchical Clustering
      • Hands-on Activity
      •  2 Quizzes
      • 1 Project
      • Classification and Regression with SVM
      • The Concept of Margin and Support Vectors
      • Kernel Trick for Non-Linear Data
      • Introduction to KNN
      • Predictions of KNN Based on Nearest Neighbors
      • Euclidean Distance, Manhattan Distance and Other Distance Metrics
      • Choosing the Value of K
      • Hands-on Activity
      •  2 Quizzes
      • 1 Project
      • Understanding Time Series Data
      • ARIMA Model and Its Components
      • Building ARIMA Models
      • Forecasting with ARIMA
      • Seasonal ARIMA (SARIMA) Model and Its Components
      • Building and Forecasting with SARIMA
      • Model Evaluation and Tuning
      • Hands-on Activity

      Explore Deep Learning

      •  2 Quizzes
      • 1 Project
      • Overview of Artificial Neural Networks (ANNs)​
      • Neural Network Basics​
      • Model Representation in Deep Learning​
      • Deep Learning Applications​
      • Training Deep Learning Models​
      • Building A Simple Artificial Neural Network​
      • Hands-on Activity: ANN
      • Convolutional Neural Networks (CNNs)​
      • Hands-on Activity: CNN
      •  2 Quizzes
      • 1 Project
      • Recurrent Neural Networks (RNNs)
      • Recurrent Neurons​
      • Vanishing Gradient Problem​
      • LSTM and GRU​
      • Building and Training RNN
      • Overfitting and Regularization Techniques​
      • Dropout and Normalization​
      • Model Evaluation, Metrics and Hyper-parameter Techniques​
      • Hands-on Activity: RNN, LSTM, GRU

      Discover Natural Language Processing (NLP)

      •  2 Quizzes
      • 1 Project
      • Overview of NLP
      • Challenges in NLP
      • Key NLP Tasks
      • Text Preprocessing in NLP
      • NLP Libraries and Frameworks
      • Feature Extraction and Representation
      • Building A Text Classification Model
      • Hands-on Activity
      •  2 Quizzes
      • 1 Project
      • Advanced Word Embeddings
      • GLOVE (Global Vectors for Word Representation)
      • N-Grams
      • Recurrent Neural Networks (RNN)
      • Long Short-Term Memory (LSTM)
      • GRU
      • Hands-on Activity

      Class Project: Application of ML, Deep Learning and NLP

      •  2 Quizzes
      • 1 Project
      • Introduction​ – Data Science Workflow
      • Data Collection​
      • Exploratory Data Analysis (EDA) and Visualization​
      • Data Preprocessing​
      • Machine Learning Model Development​
      • Introduction to Model Deployment
      • Model Deployment​ Using Streamlit
      •  2 Quizzes
      • 1 Project
      • Introduction to Problem Statement
      • Dataset Overview​
      • NLP Model Development​
      • Deep Learning Model Development​
      • Model Evaluation​
      • Model Deployment​ Using Streamlit

      Mastering Data Visualization

      •  2 Quizzes
      • 1 Project
      • Introduction to Power BI, Key Features, Installation and Setup
      • Understanding the Power BI Desktop Interface
      • Exploring the Workspace: Ribbons, Panes and Menus
      • Data Transformation
      • Data Modeling: Relationships, Keys and Hierarchies
      • Data Analysis Expressions (DAX), DAX Functions and Calculations
      • Advanced DAX Calculations: Time Intelligence, Filters and Measures
      • Charts and Page Layouts
      • Creating A Power BI Dashboard
      • Publishing and Sharing Reports and Dashboards
      • Hands-on Activity
      •  2 Quizzes
      • 1 Project
      • Overview of Tableau Prep
      • Data Connections, Cleaning and Transformation
      • Introduction to Tableau Desktop
      • Data Source Connection and Navigation
      • Visual Analytics – Sorting and Filtering Data Interactivity
      • Working with Calculated Fields
      • Aggregations and Level of Detail (LOD) Expressions
      • Creating Charts and Dashboards in Tableau
      • Hands-on Activity

      Mastery in Generative AI (Gen AI)

      •  2 Quizzes
      • 1 Project
      • Overview of Generative AI
      • Definition and Key Features of Generative Models
      • Applications of Generative AI Across Various Industries
      • Ethical Considerations and Potential Biases in Generative AI
      • Architecture Overview: Transformers and Their Key Components
      • Pre-Training and Fine-Tuning of LLMs
      • Comparison of Different LLM Models (GPT-3, T5, Jurassic-1 Jumbo)
      • Introduction to Hugging Face and Text Generation/Summarization
      • Setting Up the Environment and Accessing Hugging Face
      • Exploring Pre-Trained LLM Models and Functionalities
      • Implementing Text Generation Tasks Using Transformers and LLMs
      • Experimenting With Text Summarization Techniques with LLMs
      • Analyzing the Strengths and Limitations of Different Approaches
      • Hands-on Activity
      •  2 Quizzes
      • 1 Project
      • Fine-Tuning LLMs for Specific Tasks
      • Dataset Preparation and Pre-Processing Techniques
      • Fine-Tuning Hyper-parameter Optimization
      • Evaluating the Performance of Fine-Tuned Models (Bleu and Rouge)
      • Introduction to Retrieve, Augment and Generate (RAG) for Fine-Tuning
      • Hands-On: Fine-Tuning A LLM with Custom Data
      • Selection of LLM Models and Dataset
      • Fine-Tuning with Hugging Face Libraries
      • Evaluating and Analyzing the Fine-Tuned Model’s Performance
      • Comparison of Results with The Pre-Trained Model
      • Hands-on Activity
      •  2 Quizzes
      • 1 Project
      • Advanced Fine-Tuning Techniques
      • Prompt Engineering and Its Impact on Generated Text
      • Exploring Techniques Like Beam Search and Nucleus Sampling
      • Conditional Text Generation Based on Specific Contexts
      • Text-To-Speech and Speech-To-Text Integration with Hugging Face
      • Model Evaluation Techniques
      • Going Beyond Bleu and Rouge: Exploring Advanced Metrics for Different Tasks
      • Qualitative Analysis of Generated Text and Summarization Outputs
      • Importance of Human Evaluation in Generative Models
      • Hands-on: Fine-Tuning with Advanced Techniques and Text-To-Speech/Speech-To-Text
      • Experimenting with Prompt Engineering and Advanced Generation Techniques
      • Implementing Conditional Text Generation Based on Specific Contexts
      • Integrating Text-To-Speech and Speech-To-Text Functionalities
      • Evaluating the Performance of Fine-Tuned Models Using Advanced Metrics
      •  2 Quizzes
      • 1 Project
      • Real-World Applications of Generative AI
      • Case Studies of Successful LLM Applications in Various Industries
      • Identifying New Opportunities for Generative AI Solutions
      • Ethical Considerations and Responsible Deployment Practices
      • Designing and Developing a Chatbot
      • Defining the Chatbot’s Functionalities and Target Audience
      • Integrating Fine-Tuned LLM Models for Text Generation, Dialogue, and Text-To-Speech/Speech-To-Text
      • Building the Chatbot Interface and User Interaction Flow
      • Implementing and Deploying the Chatbot With Gradio
      • Testing and Evaluating the Chatbot

      Capstone Project

      •  2 Quizzes
      • 2 Project
      • Project and Dataset Assignment by Capstone Mentor
      • Orientation Session by Capstone Mentor – Project Expectations
      • Mentorship Session by Capstone Mentor – Doubt Resolutions
      • Project Presentation

      Career Enhancement

      • Presentation Skills
      • Email Etiquettes
      • LinkedIn Profile Building
      • Personality Development and Grooming
      • Interview Do’s and Don’ts
      • Mock Interviews
      • HR And Technical Interview Prep
      • One-On-One Feedback

      Learn Through Industry Projects in Data Science Course and AI

      Highlighting a selection of real-world projects and case studies supported by leading companies across industries

      CO2 Emission Prediction in Automobiles

      Building a model to predict Toyota vehicle CO2 emissions using fuel data for sustainable innovation.

      Predictive Analysis for E-Commerce Success

      Crafting a predictive system to forecast customer behavior using diverse ML algorithms for strategic insights.

      Advanced Phishing Detection

      Developing a system to detect phishing attempts with advanced ML algorithms for enhanced cybersecurity measures.

      Decoding Public Sentiment

      Crafting sentiment analysis, leveraging advanced ML and NLP for deep insights into user sentiments and behaviors.

      Predicting Amazon Book Genres

      Forecasting book genres from text data using advance NLP techniques and Machine Learning algorithms.

      Visualizing Entertainment in Tableau

      Explore Netflix’s global content with Tableau: map, top genres, titles added yearly, and interactive details.

      Forecasting Trends in Banking and Finance

      Predict future stock prices with precision using ARIMA, analyzing history to unveil strategic financial patterns.

      Precise Pneumonia Detection

      Automate pneumonia detection in chest X-rays using CNN, optimizing healthcare workflows, and aiding diagnostics.

      Developing a GenAI Chatbot

      Embark on a culinary adventure with a GPT-powered chatbot, guiding users to master delightful recipes.

      The BIA® Advantage

      Blended Learning Opportunities

      • BIA®’s blended learning experience enables students to choose between lively data science course IN-CLASSROOM sessions or seamlessly attend the same session ONLINE.
      • Our In-Classroom data science course sessions are Live Streamed, allowing students to join in ONLINE mode with real-time options to interact with the trainer and fellow classmates.

      Flipped Classroom Learning

      • Our flipped classroom learning model empowers students to review instructional materials online before class, fostering active discussions, problem-solving, and hands-on activities during face-to-face sessions.

      Asynchronous Learning Modules

      • Elevate in-person data science course learning with our asynchronous modules, featuring quizzes, assignments, lecture recordings, and extra resources. This allows students to revise learning materials at their own pace and according to their preferences.

      Industry Experience