OUR WORK
Become a Certified
AI Data Scientist
Comprehensive Online Certification Course
4.5 out of 5 rating
Fee
INR 49500
USD 850
Duration
Bootcamp (3 months) or
Part-time (9 months)
Batches
Weekdays (Evening)
Weekends (Morning & Evening)
Join our comprehensive online certification course to become a certified AI data scientist. Gain essential skills in AI data analysis, machine learning, data visualization, and more. Enroll now and start your career in data science!
17+ Years legacy 5.4K+ International Students
Why Choose AI Data Scientist Course?
57% Avg Salary Hike:
Earning a data science certification can significantly boost your earnings. On average, certified professionals experience a 57% increase in salary. This hike is attributed to the high demand for certified data scientists who bring specialized knowledge and skills to their roles, enhancing their value to employers.
Job Opportunities:
The demand for data science professionals is robust and growing. According to the U.S. Bureau of Labor Statistics, data science jobs are expected to increase by 28% through 2026, adding more than 11.5 million positions. This surge is driven by the expanding use of data-driven decision-making in sectors such as technology, finance, healthcare.
Future Analytics:
The field of analytics is set for substantial growth. With enterprises increasingly adopting AI, there is a projected 75% rise in operationalizing AI solutions. This expansion is expected to result in a 5x growth in analytics infrastructure, underscoring the critical role of data science in enhancing business operations and competitiveness.
₹25 LPA to ₹40 LPA:
Professionals with a data science certification and over 5 years of experience can expect competitive salaries ranging from ₹25 LPA to ₹40 LPA. This reflects their advanced skills in data analysis, machine learning, and AI, making them valuable assets to employers across various industries.
Propel Your Career in AI Data Science
Develop your proficiency in:
- Essential Skills: Acquire the theoretical knowledge, methodologies, tools, and programming languages necessary for various data scientist roles.
- Interdisciplinary Communication: Hone your ability to communicate and collaborate effectively across different functions and teams.
- Core Disciplines: Gain expertise in statistics, applied mathematics, data science, computer science, business analytics, and more.
Elevate Your Career in AI Data Science
- $100,910: The average salary of a AI Data Scientist in the United States.
- 113,300: The number of job postings in AI Data Science across the USA.
- 36% Growth by 2031: The field is experiencing rapid expansion, with an anticipated 36% growth from 2021 to 2031, significantly outpacing other sectors.
Qualify for In-Demand Roles
- Data Scientist
- Decision Scientist
- Statistician
The U.S. Bureau of Labor Statistics anticipates a 19% growth in data scientist positions over the next two decades, reflecting the increasing demand for expertise in data-driven decision-making.
Core Programs for AI Data Scientist
- Introduction to Python for AI:
- Fundamentals of Python in AI, setting up the environment, and basic Python programming.
- Data Handling and Manipulation:
- Using NumPy and Pandas for data analysis, and visualizing data with Matplotlib and Seaborn.
- Introduction to Machine Learning with Python:
- Basics of machine learning, using Scikit-learn for implementing ML algorithms, and evaluating models.
- Deep Learning with Python:
- Fundamentals of neural networks, using TensorFlow and Keras for building and training models.
- Natural Language Processing (NLP):
- Basics of NLP, utilizing libraries like NLTK and spaCy
- Basics of NLP, utilizing libraries like NLTK and spaCy
- Foundations of Data Analysis for AI: Introduction to data analysis, statistical concepts, and the essential tools for data analysis.
- Data Management for AI: Techniques for data collection, cleaning, preparation, and ethical data handling.
- Exploratory Data Analysis (EDA): Deep dive into EDA techniques and implementing EDA using Python.
- Predictive Analytics and Machine Learning: Basics of machine learning, building predictive models, and exploring advanced AI techniques like neural networks.
- Foundations of Data Visualization: Understanding the basics and importance of visualizing data, and preparing data for visualization.
- Visualization Tools and Technologies: Detailed coverage of tools ranging from simple to advanced, integrating AI with visualization tools for automated insights.
- Designing Effective Visualizations: Principles for designing visuals for AI data, including interactive elements for enhanced user engagement.
- Advanced Visualization Techniques: Exploring dynamic real-time visualizations and predictive analytics to foresee future trends.
Module 1: SQL Fundamentals and Database Management
Session 1: SQL Fundamentals and Database Introduction
- Introduction to SQL and Databases
- DBMS vs. RDBMS Understanding
- SQL Standards Overview
- SQL Sub-languages
- Oracle Versions Comparison (10g, 11g, 12c)
- Oracle 12c Installation
- SQL*Plus and Developer Tool Usage
- Datatypes in Oracle
- Operators in Oracle
- Schema Design and Objects Overview
Session 2: Data Retrieval Techniques
- SELECT Statement for Data Retrieval
- Column and Table Aliases
- Data Filtering and Sorting within a Single Table
- Clauses in Oracle: WHERE, HAVING, FROM, GROUP BY, ORDER BY, USING, CONSTRAINT, FOR UPDATE
Session 3: Working with DDL Commands
- CREATE Statement for Table Creation
- Creating Tables from Existing Tables
- Table Deletion with DROP Command
- Altering Table Columns
- Modifying Column Data Types
- Renaming Table Columns and Entire Tables
- Using TRUNCATE Command
- Difference Between DELETE and TRUNCATE
Session 4: Working with DML Commands
- Copying Data between Tables
- Copying Table Structure
- Various Methods of Inserting Rows
- Updating Record Values with UPDATE Command
- Deleting Specific Records
- Utilizing MERGE and INSERT ALL Commands
Module 2: Advanced SQL Topics and Data Aggregation
Session 1: Integrity Constraints
- Declaring Column-Level Constraints
- Declaring Row-Level Constraints
- Adding Constraints to Existing Tables
- Types of Integrity Constraints: NOT NULL, UNIQUE Key, PRIMARY Key, Referential Integrity, Check Integrity
- Enabling and Disabling Constraints
- Retrieving Information About Constraints
Session 2: Built-In Functions
- Understanding Single Row Functions
- Using Single Row Functions with Dummy Tables
- Types of Single Row Functions: String Functions, Date Functions, Mathematical Functions, Conversion Functions, Special Functions, Analytical Functions
- Working with Multi-Row Functions
Session 3: Data Aggregation
- Working with Aggregate Functions: COUNT(), SUM(), MAX(), MIN(), AVG()
- Utilizing GROUP BY Clause
- Working with HAVING Clause
- Distinguishing Between WHERE and HAVING Clauses
Module 3: Advanced SQL Techniques, Subqueries, and Database Security
Session 1: Understanding Joins
- Importance and Uses of Joins
- Types of Joins: Equi Join, Non-Equi Join, Self Join, Outer Join, Left and Right Outer Join, Full Outer Join, Cross Join
Session 2: Set Operators and Pseudo Columns
- Utilizing Set Operators within a Single Table
- Working with Set Operator Types: UNION, UNION ALL, INTERSECT, MINUS
- Exploring Pseudo Columns: ROWID, ROWNUM
Session 3: Subqueries and Views
- Significance of Subqueries
- Different Types of Subqueries: Single-Row Subqueries, Multi-Row Subqueries, Nested Queries, Multi-Column Subqueries, Correlated Subqueries
- Using Inline Views and Scalar Queries
Session 4: Database Transactions and Security
- Working with Transaction Control Language (TCL)
- Managing Data Control Language (DCL) Commands
- Using COMMIT and ROLLBACK
- Leveraging SAVEPOINT and SET TRANSACTION
- Granting System Privileges to Users
- Invoking and Revoking Object Privileges
- Creating Users and Roles
Module 4: SQL Advanced Topics and Database Expertise
Session 1: SQL Functions
- Explanation of Scalar Functions
- Overview of Aggregate Functions
- Introduction to Date and Time Functions
Session 2: SQL Procedures and Optimization
- Creating and Using Stored Procedures
- Basic Introduction to Triggers
Session 3: Database Design and Performance
- Brief Overview of Normalization and Denormalization
- Discuss the Importance of Indexing for Performance
- Introduce Constraints and Data Integrity
Session 4: Query Optimization
- High-Level Discussion of Query Optimization Techniques
- Quick Overview of Analyzing Query Execution Plans
- Mention the Importance of Profiling and Monitoring Database Performance
Module 5: Advanced SQL Techniques and Database Administration
Session 1: Advanced SQL Techniques
- Brief Overview of Window Functions
- Introduction to Common Table Expressions (CTEs)
- High-Level Understanding of Advanced Joins and Subqueries
Session 2: SQL Reporting and Analysis
- Creating Complex Reports with SQL
- Discuss the Role of SQL in Data Analysis
- Touch on Data Export and Integration
Session 3: Database Administration
- Briefly Explain Backup and Recovery Strategies
- Mention User and Permissions Management
- Introduce Monitoring and Troubleshooting
Session 4: High Availability and Security
- Quick Overview of High Availability Solutions (Replication, Clustering)
- Discuss Security Best Practices and Compliance at a High Level
- Mention Encryption and Data Protection
Module 6: Power BI Program Integrating Advanced SQL
Session 1: Power BI Data Modeling
- Overview of Advanced Data Modeling Techniques
- Introduce Working with Complex Data Structures
- Discuss Hierarchies and Time Intelligence
Session 2: Advanced DAX and Power Query
- Introduction to Advanced DAX Functions and Formulas
- Briefly Cover Advanced Power Query Transformations
- Mention Optimizing Data Loading
Session 3: Advanced Power BI Visualization and Reporting
- Discuss Advanced Visualizations and Custom Visuals
- Introduction to Drillthrough and Bookmarks
- Mention Storytelling and Reporting Best Practices
Module 1: Introduction to Deep Learning
- Overview of Deep Learning
- Definition and Scope
- History and Evolution of Deep Learning
- Milestones and Key Figures
- Key Applications of Deep Learning
- Real-World Use Cases
- Basic Concepts and Terminology
- Fundamental Terms and Definitions
Module 2: Neural Networks and Deep Learning
- Introduction to Neural Networks
- Basic Structure and Function
- Perceptrons and Multilayer Perceptrons
- Single-Layer vs. Multi-Layer Perceptrons
- Activation Functions
- Common Activation Functions and Their Roles
- Training Neural Networks
- Process and Techniques
- Backpropagation Algorithm
- Detailed Explanation
- Loss Functions and Optimization
- Types and Applications
Module 3: Improving Deep Neural Networks: Hyperparameter Tuning, Regularization, and Optimization
- Hyperparameter Tuning
- Methods and Strategies
- Regularization Techniques
- L1 and L2 Regularization
- Dropout
- Data Augmentation
- Optimization Algorithms
- Gradient Descent Variants
- Adam, RMSprop, and Other Optimizers
- Batch Normalization
- Concepts and Benefits
- Early Stopping and Model Checkpointing
- Implementation and Advantages
Module 4: Structuring Machine Learning Projects
- Project Workflow and Best Practices
- End-to-End Process
- Data Preparation and Preprocessing
- Techniques and Tools
- Training, Validation, and Test Sets
- Splitting and Management
- Model Selection and Evaluation Metrics
- Criteria and Methods
- Debugging and Error Analysis
- Strategies and Techniques
- Deployment and Monitoring
- Best Practices and Tools
Module 5: Convolutional Neural Networks (CNNs)
- Introduction to CNNs
- Basic Concepts and Architecture
- Convolutional Layers
- Function and Implementation
- Pooling Layers
- Types and Applications
- Fully Connected Layers
- Role in CNNs
- Transfer Learning and Pre-trained Models
- Techniques and Benefits
- Advanced CNN Architectures
- AlexNet
- VGGNet
- ResNet
- InceptionNet
Module 6: Sequence Models
- Introduction to Sequence Models
- Overview and Applications
- Recurrent Neural Networks (RNNs)
- Basic Concepts and Uses
- Long Short-Term Memory (LSTM) Networks
- Structure and Function
- Gated Recurrent Units (GRUs)
- Comparison with LSTMs
- Sequence to Sequence Models
- Applications and Examples
- Attention Mechanisms
- Theory and Implementation
- Transformer Models
- Detailed Overview
Module 7: Advanced Topics in Deep Learning
- Generative Adversarial Networks (GANs)
- Concepts and Applications
- Autoencoders and Variational Autoencoders (VAEs)
- Theory and Use Cases
- Reinforcement Learning
- Basics and Applications
- Deep Reinforcement Learning
- Advanced Techniques
- Meta-Learning and Few-Shot Learning
- Concepts and Examples
- Neural Architecture Search (NAS)
- Methods and Benefits
- Explainable AI and Interpretability
- Importance and Techniques
Module 8: Practical Implementations and Case Studies
- Image Classification
- Methods and Applications
- Object Detection and Segmentation
- Techniques and Tools
- Natural Language Processing (NLP) Applications
- Key Applications and Models
- Speech Recognition
- Techniques and Use Cases
- Time Series Forecasting
- Methods and Applications
- Recommender Systems
- Theory and Implementation
- Case Studies from Industry
- Real-World Examples
Introduction to Machine Learning and AI
- Overview of Machine Learning and AI.
- Historical Context and Evolution.
- Key Terminologies and Concepts.
Supervised Learning
- Linear Regression and Classification.
- Decision Trees and Random Forests.
- Support Vector Machines (SVM).
- Model Evaluation and Performance Metrics.
Unsupervised Learning
- Clustering Algorithms (K-means, Hierarchical).
- Dimensionality Reduction Techniques (PCA, LDA).
- Anomaly Detection.
Neural Networks and Deep Learning
- Introduction to Neural Networks.
- Deep Learning Fundamentals.
- Convolutional Neural Networks (CNN).
- Recurrent Neural Networks (RNN).
- Transfer Learning.
Natural Language Processing (NLP)
- Text Preprocessing and Tokenization.
- Sentiment Analysis.
- Topic Modeling.
- Sequence Models and LSTM.
- Transformer Models and BERT.
Practical Machine Learning
- Working with Python and Jupyter Notebooks.
- Using TensorFlow and Keras for Model Building.
- Implementing PyTorch for Advanced Deep Learning.
- Utilizing scikit-learn for Machine Learning Algorithms.
- Introduction to Pandas:
- Essentials of Pandas and setting up the environment.
- Understanding Series and DataFrame structures.
- Data Manipulation Techniques:
- Comprehensive data importing/exporting, cleaning, and transformation.
- Advanced Data Analysis:
- Techniques in grouping, aggregation, and time series analysis.
- Visualization with Pandas:
- Basics of plotting with Pandas and creating interactive visualizations.
- Integrating Pandas with AI Models:
- Data preprocessing for machine learning and feature engineering.
- Introduction to NumPy:
- Basics of NumPy and setting up the environment.
- Understanding NumPy arrays, data types, and array manipulations.
- NumPy in Data Handling:
- Advanced techniques in indexing, slicing, reshaping, and transposing arrays.
- Statistical analysis capabilities of NumPy for AI applications.
- NumPy for Machine Learning:
- Utilization of NumPy in feature engineering for AI models.
- Techniques to improve computation efficiency through vectorization and broadcasting.
- NumPy with Other Libraries:
- Integrating NumPy with Pandas for data analysis and Matplotlib for data visualization.
- Practical applications in building neural networks and processing image and audio data.
- Introduction to Scikit-learn:
- Understanding Scikit-learn’s framework and its capabilities in AI.
- Setup and basic machine learning concepts relevant to Scikit-learn.
- Data Handling:
- Techniques for preprocessing data and managing model validation effectively.
- Supervised Learning:
- In-depth coverage of regression and classification models, including model tuning and evaluation.
- Unsupervised Learning:
- Exploration of clustering algorithms and dimensionality reduction techniques.
- Model Selection and Boosting:
- Advanced techniques for enhancing model performance using ensemble methods and feature selection.
- Advanced Applications:
- Application of Scikit-learn in text mining, natural language processing, and neural network integrations.
- Introduction to TensorFlow:
- Understanding TensorFlow’s role in AI, setting up the environment, and basic programming.
- Basic Concepts in TensorFlow:
- Exploring core components like tensors and data types, and building simple neural networks.
- Advanced TensorFlow:
- Advanced model architectures like CNNs and RNNs, debugging, and optimization techniques.
- TensorFlow in Production:
- Strategies for deploying TensorFlow models in cloud and edge environments, monitoring, and maintenance.
- Real-World Applications:
- Exploring TensorFlow’s applications in various industries like healthcare and retail, and innovative uses in fields like art and music.
- Introduction to Matplotlib:
- Basics of data visualization and setting up Matplotlib.
- Fundamental plotting techniques and customization options.
- Advanced Plotting Techniques:
- Variety of plot types like line plots, scatter plots, bar charts, and histograms.
- Data visualization best practices and aesthetic enhancements.
- Matplotlib in Machine Learning:
- Visualization strategies for exploratory data analysis and machine learning data.
- Techniques for comparing model performance and visualizing model tuning.
- Interactive Plots and Real-time Data:
- Creating interactive and dynamic visualizations.
- Handling and visualizing real-time data streams.
- Introduction to Matplotlib:
Introduction to AWS and AI Services:
- Basics of AWS’s architecture and its comprehensive AI services.
AWS Machine Learning Services:
- Detailed insights into using SageMaker, Comprehend, and Rekognition for practical AI applications.
AWS for Deep Learning:
- Utilization of AWS Deep Learning AMIs and containers, TensorFlow, and PyTorch integration.
Data Management and Processing in AWS:
- Building AWS data lakes, using AWS Kinesis for real-time data streaming and analytics.
Scalability and Cost Management:
- Strategies for managing scalability and optimizing costs in AWS.
Security and Compliance:
- Ensuring robust security measures and understanding AWS’s compliance frameworks.
- Introduction to Microsoft Azure:
Overview of Azure services, setting up an account, navigating the portal, and understanding pricing models. - Azure Computing Services:
Deep dive into Azure Virtual Machines, Kubernetes Service, serverless computing with Azure Functions, and resource management. - Azure AI Services:
Utilization of Azure Cognitive Services, Machine Learning Service, and Bot Services, including real-world AI implementations. - Data Management in Azure:
Managing data using Azure SQL Database, Cosmos DB, Data Factory, and integrating big data with AI solutions.
- Introduction to OpenCV: Understanding the basics, installation, and setup.
- Image Manipulation and Processing: Techniques for color transformations, thresholding, and advanced image manipulation.
- Feature Detection and Description: Methods for identifying and matching features in images for detailed analysis.
- Video Analysis: Techniques for video data manipulation, motion detection, and object tracking.
- Applications of OpenCV in AI: Implementing real-time face recognition and augmented reality, integrating with AI frameworks like TensorFlow and PyTorch.
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It really helped me for my project.
Shreenidhee Nilesh Teli
Student, MIT ADT University
“
Nicely planned with relevant topics and experts. Nicely organized
Dr Gopal Pundlikrao Bharkad
Academicians, Mumbai Veterinary College
“
Joined for the further training program, Excellent and Helpful, thanks.
Dr. Dharmendra Chandraprakash Kothari
Professor, Shri Shivaji Education Society Amravati's College of Engineering & Technology
FAQs for AI Data Scientist Certification Program
An AI Data Scientist specializes in using artificial intelligence and machine learning techniques to analyze and interpret complex data sets, develop predictive models, and create data-driven solutions to business problems.
You will learn:
- Data analysis and visualization
- Machine learning and AI fundamentals
- Programming languages such as Python and R
- Use of tools like TensorFlow, PyTorch, Scikit-learn, and more
- Cross-functional communication and teamwork
- Applied mathematics and statistics
The duration of the certification program varies, but most participants complete it within 6 to 12 months, depending on their pace and schedule.
Basic knowledge of programming and mathematics is recommended. However, the program is designed to cater to both beginners and those with some experience in data science or AI.
This certification provides you with the necessary skills and knowledge to excel in high-demand roles in AI and data science. It also offers a significant salary boost, with certified professionals seeing an average salary hike of 57%.
Yes, the AI Data Scientist Certification Program is available online, offering flexibility to learn at your own pace.
Yes, you will receive a recognized certificate upon completing the program, which can be showcased to potential employers.
Workshops
