Master Diploma in Data Science and Artificial Intelligence

Boston Institute of Analytics

Learn about data science & artificial intelligence with Master Diploma in Data Science & Artificial Intelligence programme by the Boston Institute of Analytics.

Part time, Online

10 Months

Quick Facts

particular	details
Medium of instructions English	Mode of learning Virtual Classroom, Campus Based/Physical Classroom	Mode of Delivery Video and Text Based

Course overview

The Master Diploma in Data Science & Artificial Intelligence is a certification course designed to equip students with the skills and knowledge necessary to thrive in the dynamic fields of data science and artificial intelligence. This course explores the foundational concepts of data analysis, machine learning, and statistical modelling, providing a detailed understanding of the methodologies and tools involved in data science and artificial intelligence.

The Master Diploma in Data Science & Artificial Intelligence Certification by the Boston Institute of Analytics provides students with practical experience in data manipulation, feature engineering, and model deployment. The course also encompasses cutting-edge topics in artificial intelligence, data science exploring neural networks, deep learning, natural language processing, and computer vision.

Data Science Certification Courses
Artificial Intelligence Certification Courses

The highlights

10 months certification course
Certification from the Boston Institute of Analytics
Two-in-One Certification
Classroom + Online Mode
6 months of on Job Training
Interactive classes
Hands-on learning experience

Program offerings

Course readings
Practical learning

Course and certificate fees

certificate availability

Yes

certificate providing authority

Boston Institute of Analytics

Who it is for

Data analyst Data scientist Machine learning engineer

The Master Diploma in Data Science & Artificial Intelligence certification course is designed for Data Analysts, Machine Learning Engineers, and Data Scientists and facilitates networking opportunities with industry professionals, fostering connections that can enhance career prospects. Additionally, the course adds credibility to students' skill sets, opening doors to a wide range of career opportunities in data-driven and technology-centric sectors.

Eligibility criteria

Academic Qualifications

Candidates from all academic streams such as arts, science, commerce, and engineering can apply for this course.

Certification Qualifying Details

To qualify for the Master Diploma in Data Science & Artificial Intelligence training, candidates are required to complete the full course.

What you will learn

Natural language processing Knowledge of deep learning

The Master Diploma in Data Science & Artificial Intelligence certification course provides students with a profound skill set and a comprehensive understanding of key concepts in these dynamic fields. Students will master the art of data analysis, possessing the ability to manipulate and derive insights from complex datasets.

Upon completion of the Master Diploma in Data Science & Artificial Intelligence certification course, students will gain expertise in artificial intelligence, exploring advanced topics such as neural networks, deep learning, natural language processing, and computer vision. The course emphasis on hands-on projects ensures that students gain practical experience in data manipulation, feature engineering, and model deployment.

The syllabus

Data Science Course and AI Foundation: Orientation

Welcome and Course Overview

Introduction to the Data Science Course
Importance of Data Science and AI

Key Concepts Overview

Fundamentals of Data Science
Introduction to Artificial Intelligence

Software Installation Guidance

Installing Anaconda
Setting up Jupyter Notebooks
Setting-up Power BI and Tableau Account
Introduction to Excel Environment

Course Expectations and Structure

Overview of the Course Modules
Brief on Assignments and Assessments

Introduction to the Learning Environment

Digital Platforms and Resources
Communication Channels

Mastering MS Excel

Fundamentals of Excel

Overview of Excel Interface
Key Formulas and Functions
Ranges and Tables
Data Cleaning – Text Functions, Dates and Times
Conditional Formatting
Sorting and Filtering

Advance Excel

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

Fundamentals of Python

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

Loops, Functions & Error Handling

Loop Control Statements: Break, Continue and Pass
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

Data Structure: List and Tuple

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

Data Structure: Dictionary and Sets

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

Introduction to Numpy

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

Introduction to Pandas and Data Visualization

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

Introduction to SQL and Querying

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

Advanced SQL Concept and Data Manipulation

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

Fundamentals of Statistics and Probability

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

Advance Statistic and Hypothesis Testing

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

Introduction to Machine Learning (ML) and Regression

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

Multiple Linear Regression and Model Evaluation

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

Logistic Regression and Classification Metrics

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

Decision Trees and Ensemble Methods

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

Model Evaluation and Validation Techniques

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

Unsupervised Learning

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

Support Vector Machines (SVM) and K-Nearest Neighbors (KNN)

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

Time Series Modeling with ARIMA And SARIMA

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

Introduction to Deep Learning

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

Deep Learning Architectures and Training

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)

Introduction to Natural Language Processing (NLP)

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

Advanced NLP Techniques

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

Data Science Project – 1

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

Data Science Project – 2

Introduction to Problem Statement
Dataset Overview
NLP Model Development
Deep Learning Model Development
Model Evaluation
Model Deployment Using Streamlit

Mastering Data Visualization

Mastering Power BI

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

Mastering Tableau

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)

Introduction to Generative AI, Transformers and LLMs

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

Training and Fine-tuning LLMs

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 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

Advanced Fine-tuning and Model Evaluation

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

Building a Real-Life Chatbot with Gradio Deployment

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

Capstone Project Allocation, Mentorship and Presentation

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

Soft Skills Training

Presentation Skills
Email Etiquettes
LinkedIn Profile Building
Personality Development and Grooming

Interview Preparation

Interview Do’s and Don’ts
Mock Interviews
HR And Technical Interview Prep
One-On-One Feedback

Admission details

To join the Master Diploma in Data Science & Artificial Intelligence classes, candidates need to follow these steps:

Step 1: Browse the official URL: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

Step 2: Candidates are required to submit the online application by filling out all the necessary and relevant information such as primary email address, name, phone number, and motivation letter.

Step 3: Thereafter, they are required to pay the course fee in the payment gateway option.

Step 4: Candidates would then have to access the course and start the learning process.

Filling the form

To enrol for the Master Diploma in Data Science & Artificial Intelligence training, candidates are required to submit the online application which asks for details such as primary email address, name, phone number and a motivation letter.

How it helps

The Master Diploma in Data Science & Artificial Intelligence certification benefits the student by providing a deep and comprehensive understanding of both data science and artificial intelligence, ensuring that students are equipped with a holistic skill set that spans from data analysis to advanced machine learning and AI techniques. The hands-on course embedded in the curriculum fosters practical experience, enabling students to apply their knowledge to real-world scenarios.

FAQs

How can I apply for the Master Diploma in Data Science & Artificial Intelligence certification course?

Candidates need to submit an online application form by submitting their official details and pay the course fee in the payment gateway option.

What is the duration of the Master Diploma in Data Science & Artificial Intelligence by Boston Institute of Analytics?

The certification programme is 10 months in duration with access to course readings and hands-on learning experience.

Are there any scholarship options available for Master Diploma in Data Science & Artificial Intelligence training?

Candidates are not provided with any scholarship options for this certification programme.

Who can enrol for the Master Diploma in Data Science & Artificial Intelligence classes?

This course is designed for data scientists, data analysts and machine learning engineers to help students understand the fundamentals involved in data science and artificial intelligence.

Are there any prerequisites for the Master Diploma in Data Science & Artificial Intelligence training?

Candidates do not need any prerequisites for this certification programme.

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