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Data Science and Artificial Intelligence

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Data Science and Artificial Intelligence

Take part in the future of data science and AI with this complete data science course program. It gives you the best of both worlds: the information you need in data science and the skills you need in generative AI (Gen AI). There are capstone projects, masterclasses, guest sessions with experts in the field, and live RIA® DoubtBuster sessions in this data science study.

Gain practical experience with a data science course that covers a variety of topics, such as 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.

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Data Science and Artificial Intelligence Course Overview

  • Key Highlights
  • Extensive Classroom Training for Data Science Course
  • Practical Instruction by Industry Professionals
  • Over 15 industry case studies and assignments
  • Data Science Course Modules Incorporating Generative AI
  • Exclusive Portal for Data Science Employment Opportunities
  • Alumni Status of RIA®
  • Practical Projects and Case Analyses
  • Internationally Acclaimed Dual Certification
  • Individual In-Person Career Mentorship Sessions
  • Comprehensive Career Assistance
  • Live Data Science Program RIA® DoubtBuster Sessions
  • Over 350 Corporate Partners
  • Capstone projects that are practical and hands-on
  • Preparation for in-person job interviews (1:1)
  • Thirty-plus Programming Resources
  • Access to the Most Prominent Multinational Corporations
  • Zero-Cost EMI Available

Syllabus for Data Science & AI

Use our carefully created content to immerse yourself in more than 200 hours of learning and practical tasks. Our generative AI-integrated data science education modules are consistently current. Leading industry professionals created this data science course, guaranteeing a state-of-the-art educational experience.

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Topics That Will Keep You Engaged and Curious

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

Your Roadmap to Learning

Start with the basics, then graduate to advanced subjects step by step. This eliminates feeling overwhelmed and helps you recall and quickly comprehend new concepts. Each topic builds on what you’ve learned, building a solid foundation for learning more in a data science course.

Ideal Candidates for Data Science Course

Everyone can enroll in RIA's data science course in this age of advanced tools and artificial intelligence. The field of data science is open to students studying banking, science, engineering, commerce, or the arts. This data science course is an excellent first step for professionals considering a change into this industry. This study route offers significant insights for IT and business professionals who want to improve their data skills.

Minimum Eligibility for Data Science Course

Degrees are less important in today's data science skill-based employment market than a love of learning. To begin this path, even a high school graduation is sufficient. Your passion for using cutting-edge technology and your love of data will advance your career. Learn data science techniques that will expand your professional horizons and open doors.

Job Opportunities After Data Science Course

AI Product Manager, AI Chatbot Designer, AI Research Scientist, AI Consultant, Quantitative Analyst, AIOPS Specialist, NLP Engineer, Computer Vision Engineer, Business Intelligence Analyst, Conversational AI Developer, Algorithm Developer, AI Solution Architect, 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

Industries That Are Hiring Data Scientists and AI Specialists

Data scientists and AI specialists are in high demand across a wide range of industries, including technology, finance, banking, healthcare, e-commerce, retail, telecommunications, aerospace, marketing, entertainment, sports, and others, demonstrating the technologies' widespread applicability and influence.

Globally Accredited Data Science Course in India

Discover the world of data science with the top-ranked data science education in India offered by RASA Institute of Analytics, which is known as the most extensive data science training center in India. Explore the whole lifecycle of data science, including data collection, extraction, cleaning, exploration, transformation, and more. Discover a wide range of abilities and resources that are carefully addressed in our data science course curriculum in India, including statistical analysis, text mining, regression modeling, and deep learning.

At RASA Institute of Analytics, we provide a route to success rather than just data science instruction. Come along with us in India and start your adventure to becoming a highly sought-after Data Science specialist there. In India and abroad, Discover RIA is recognized as the top data science training center.

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

RASA Institute of Analytics, one of India's top data science training institutions, can help you start your path to a fulfilling career in data science. We are a shining example of excellence in the industry, having shaped the careers of many Data Science professionals both domestically and abroad.

Take advantage of our knowledgeable instructors, all of whom have over 15 years of industry experience. Widely recognized as the best in the field, our dual data science certification in data science and data analytics is designed to provide you the abilities and information required to succeed in India's fiercely competitive data science market. Our blended learning approach at RASA Institute of Analytics blends instructor-led online sessions, e-learning modules, and data science classroom sessions.

There are plenty of interview chances and data science placement support available to you thanks to our specialized data science placement cell and wide network of more than 350 corporate partners. Our all-inclusive Data Science course in India is made to meet and beyond your expectations, regardless of whether you're an experienced professional seeking to advance your skills or a recent graduate hoping to launch your data science career in India. Take the first step toward realizing your full potential in India's rapidly evolving data science industry by joining us today.

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Course Sylabus

  • Data Science Course and AI Foundation: Orientation

    Quickly grasp the data science training course and fundamental concepts, all while setting up essential software. This introductory session sets the stage for a seamless learning experience, reducing potential challenges along the way.

    Welcome and Course Overview
    • Overview of the Data Science Course
    • Why Data Science and AI are important
    Key Concepts Overview
    • Data Science Foundations
    • An Overview of Artificial Intelligence
    Software Installation Guidance
    • Setting up Anaconda
    • Configuring Jupyter Notebooks
    • Installing Power BI and Tableau Account
    • Overview of the Excel Interface
    Course Expectations and Structure
    • Summary of the Course Modules
    • Overview of Tasks and Evaluations
    Introduction to the Learning Environment
    • Digital Tools & Platforms
    • Channels of Communication
  • Mastering MS Excel

    Harness the potential of data. Acquire fundamental Excel abilities to effectively analyze data, create visual representations, and make informed decisions across different professional fields.

    Fundamentals of Excel
    • Introduction to Excel Interface
    • Functions & Key Formaulas
    • Tables & Ranges
    • Data Cleaning: Dates, Times & Text Functions
    • Conditional Formatting
    • Filtering & Sorting
    Advance Excel
    • Pivots
    • Excel Data Analysis: Patterns and Trends
    • Excel Data Visualization: Plots and Charts
    • Working with many worksheets
    • Linking and referencing data between worksheets
  • Python for Data Science

    Discover Python's full potential for data analysis. Learn how to handle, analyze, and visualize data effectively.

    Fundamentals of Python
    • Introduction to Python Programming Language
    • Comprehending Statements, Expressions, and Formatting
    • Summary of Identifiers, Keywords, and Comments
    • Variables: Naming Convention, Assignment & Declaration
    • Common Data Types: Strings, Floats and Integers
    • Conversion & Type Casting
    • Operators in Python
    • Interactive Learning Experience
    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 Tupls
    • 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

    Become proficient in database terminology. Develop necessary abilities in SQL to retrieve, control, and assess information to support informed decision-making.

    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

    Discover the valuable insights derived from data analysis. Delve into how statistics and probability can be used practically to improve decision-making within the realm of data.

    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

    Explore the basics of supervised learning, in which algorithms are trained using labeled data to make predictions and support well-informed choices.

    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

    Discover the realm of unsupervised learning, where algorithms reveal important findings from data that is not labeled, sparking creativity and exploration.

    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

    Explore the immersive realm of deep learning, a technology that utilizes neural networks to mimic the functions of the human brain in order to process and make sense of intricate information.

    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)

    Discover the field of natural language processing (NLP), in which machines are able to understand, interpret, and produce human language, leading to improved communication and comprehension.

    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

    Explore the capabilities of machine learning, deep learning, and natural language processing in practical situations. Address real-life problems and demonstrate your abilities by completing interactive projects.

    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

    Discover how to create engaging visual narratives by utilizing Power BI and Tableau, the top Business Intelligence software in the industry, to transform unprocessed data into practical findings.

    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)

    Discover the innovative realm of Generative AI, in which machines are taught to independently produce fresh content, art, and concepts.

    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 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
    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

    Demonstrate your abilities by putting them into practice. Engage in a practical, real-life project to exhibit your understanding of the course material.

    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

    Enhance your career path by acquiring knowledge, expertise, and approaches to progress in the evolving industries of Data Science and Artificial Intelligence.

    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

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