Advanced Certification in Data Science & Gen AI with Visualizations Tools
August 29, 2024 2025-03-22 9:07Advanced Certification in Data Science & Gen AI with Visualizations Tools
Advanced Certification in Data Science & Gen AI with Visualizations Tools
Comprehensive curriculum covering data science, AI, Big Data, and visualization tools.
Hands-on projects with real-world datasets for practical experience.
Training in the latest technologies, including Generative AI.
Expertise in Big Data tools like Hadoop, Spark, and NoSQL databases.
In-depth learning of visualization tools such as Tableau and Power BI.
Taught by industry professionals with extensive experience.
Prepares for a wide range of high-demand roles in data science and AI.
Flexible for both beginners and experienced professionals.
Key Highlights of Advanced Certification in Data Science & Gen AI with Visualizations Tools
Weekly sessions with industry professionals
Dedicated Learning Management Team
640 hours of hands-on learning experience
Over 240 hours live sessions spread across 11 months
240 hours of self-paced Learning
Learn from IIT Faculty & Industry experts
More than 60+ industry-related projects and case studies
Personalised mentorship sessions with industry experts
24*7 Support
Dedicated Learning Management Team
1:1 Mock Interview
No-Cost EMI Option
Designed for both working professionals and fresh graduates
High Demand and Career Opportunities
Competitive Edge and Innovation
Problem-Solving and Critical Thinking
Weekly sessions with industry professionals
Dedicated Learning Management Team
640 hours of hands-on learning experience
Over 240 hours live sessions spread across 11 months
240 hours of self-paced Learning
Learn from IIT Faculty & Industry experts
More than 60+ industry-related projects and case studies
Personalised mentorship sessions with industry experts
24*7 Support
Dedicated Learning Management Team
1:1 Mock Interview
No-Cost EMI Option
Designed for both working professionals and fresh graduates
High Demand and Career Opportunities
Competitive Edge and Innovation
Problem-Solving and Critical Thinking
Learn from Industry Experts.
Why Join Advanced Certification in Data Science & Gen AI with Visualizations Tools ?
Upcoming Batch:-
19th January 2025 (10pm to 1 am )
1st of February 2025 (10 pm to 1 am)
Advanced Certification in Data Science & Gen AI with Visualizations Tools Overview
This Program offers a blend of theory and practice for the upcoming data scientists or AI professionals. This book covers strong basics to advance topics of Machine Learning, Deep Learning, Computer Vision and Natural Language Processing. Those participants will also gain additional expertise in data processing, visualization and Big Data technologies of all forms including an emphasis on Generative AI. This program is modeled to teach the professionals skills for utilizing modern technologies resulting in useful data-driven insights.
Advanced Certification in Data Science & Gen AI with Visualizations Tools Objectives
This course will enable participants to have an in-depth understanding of advanced data science techniques and tools. The course aims to develop proficiency in critical areas such as Machine Learning, Deep learning, Computer Vision and NLP. They get to work on the most practical aspects of data management, more detailed and dynamic visualization as well Big Data technologies so that participants will be able to try some Generative AI solution on Advanced Problems. The goal of the course is to equip attendees with the knowledge and understanding necessary for transforming data into evidence-based insights, facilitate decision making under uncertainty; while providing key contributions towards innovative solutions across multiple domains.
Why Learn Advanced Certification in Data Science & Gen AI with Visualizations Tools ?
Program Advantages
Learn a diverse range of tools and technologies, from data science fundamentals to advanced AI, for a well-rounded education.
Gain hands-on experience with real-world datasets and projects, ensuring job readiness with practical skills in each tool.
Benefit from guidance by industry professionals, learning best practices and the latest industry standards.
Understand how tools like Machine Learning, Big Data, and Generative AI integrate to solve complex problems.
Master visualization tools like Tableau and Power BI to effectively communicate data insights.
Develop expertise in handling large-scale data with technologies like Hadoop, Spark, and NoSQL databases.
Equip yourself with in-demand skills that are valuable across various industries.
Enhance your career prospects by mastering the most sought-after tools and technologies in data science and AI.
Advanced Certification in Data Science & Gen AI with Visualizations Tools Certification
Advanced Certification in Data Science & Gen AI with Visualizations Tools Learning Path/Curriculum
Module 01 - Python
Lecture 00: Orientation (Introduction to Data Science, Scope of Data Science)
Lecture 01: Introduction to Python, Why Python, Variables, Data Types, Type castings, Strings, Indexing
Lecture 02: Operators and Conditional Statements, Looping Statements and its Control Statement
Lecture 03: Lambda Functions, *args, **kwargs, Functions
Lecture 04: Data Structures - List, Tuple and List Comprehensions
Lecture 05: Data Structures - Set and Dictionaries
Lecture 06: Classes, Objects and Constructors, Inheritance
Lecture 07: Polymorphism, Abstraction and Encapsulation
Lecture 08: Connecting to Databases, Establishing connections to databases, Executing SQL Queries, ORM (Object-Relational Mapping), Working with NoSQL Databases
Lecture 09: Introduction of Numpy and Pandas
Lecture 10: Introduction of Seaborn and Matplotlib
Module 02 - Statistics
Lecture 11: Introduction to Statistics, Descriptive Statistics, Sample, Population, Major of Central Tendency, Standard Deviation
Lecture 12: Variance, Range, IQR, Outliers, Correlation, Covariance Skewness, Kurtosis, Probability
Lecture 13: Probability, Probability distributions, Central Limit Theorem, Binomial and Poisson Distribution
Lecture 14: Normal Distribution, Type I & Type II Error
Lecture 15: T-test, Z-test, Hypothesis Testing Interview Questions
Module 03 - Machine Learning
Lecture 16: Introduction to ML, Types of variables, Encoding, Normalization, Standardization, Types of ML, Linear Regression
Lecture 17: Linear Regression, Logistic Regression, SVM, KNN, Naïve Bayes, Decision Tree, Random Forest
Lecture 18: Mean Absolute Error, Mean and Root Mean Square Error, Confusion Matrix, R2 Score, Adjusted R2 Score, F1 Score
Lecture 19: Classification Report, AUC ROC, Accuracy, Ensemble Techniques, Random Forest, Xgboost
Lecture 20: Unsupervised Machine Learning, PCA, Clustering, k-Means Clustering and Hierarchical clustering
Module 04 - Deep Learning
Lecture 21: Introduction to Neural Network, Forward Propagation, Activation Function
Lecture 22: Activation Function (Linear, Sigmoid, Relu, Leaky Relu), Optimizers, Gradient Descent, Stochastic Gradient Descent
Lecture 23: Mini Batch Gradient Descent, Adagrad, Padding, Pooling, Convolution
Lecture 24: Checkpoints and Neural Networks Implementation and Introduction to Time Series Analysis
Lecture 25: Various components of the TSA, Decomposition Method (Additive Method and Multiplicative)
Lecture 26: ARMA and ARIMA
Module 05 - SQL
Lecture 27: Basic of Database, Types of Database, Data Types, SQL Operators, Expression, Create, Insert
Lecture 28: Drop, Truncate, Delete, Alter, Update, Select, Range, Operator, IN, Wildcard, Like, Clause
Lecture 29: Constraint, Aggregation Function, Group by, Order by, Having
Lecture 30: Joins, Case, Complex Queries, Doubt Clearing
Module 06 - Tableau
Lecture 31: Tableau Desktop, Tableau products
Lecture 32: Data import, Measures, Filters
Lecture 33: Data transformation, Marks, Dual Axis
Lecture 34: Manage worksheets, Data visualization, Dashboarding, Project
Module 07 - Power BI
Lecture 35: Power BI Platform, Process Flow
Lecture 36: Features, Dataset, Bins
Lecture 37: Pivoting, Query Group, DAX Function
Lecture 38: Formula, Charts, Reports, Dashboards
Lecture 39: Bookmarks and Buttons, Conditional Formatting and Sorting, and Report Layout and Interaction
Lecture 40: Tabular Visuals, Modelling and Calculations, Advanced Data Modelling Scenarios and DAX in Power BI
Module 08 - No-SQL
Lecture 41: Introduction, SQL vs NoSQL, Data Model, Data types, Object ID, Data type, Binary Data, Date, Null, Boolean, Integer, String
Lecture 42: Collection method, queries, CRUD Operation, Insert, Find, Update, Delete, Validate, Bulk write, Delete one
Module 09 - Java
Lecture 43: Introduction to Java, Installation, Syntax main()/printIn()/print()/ Variable [String, Int, Boolean, float, char], Datatypes, Operators
Lecture 44: Conditions, Loop, Methods, Class, File Handling
Module 10 - Introduction to Big Data & Hadoop
Lecture 45: Types of Data, Introduction to Big Data (History, V's of Big Data, Advantages & Disadvantages of Big Data), Big Data Applications in Various Sectors, Introduction to Hadoop, Scaling (Horizontal and Vertical), Challenges in Scaling, Parallel Computing, Distributed Computing and Hadoop, Hadoop Tools Overview, Big Data Analytics Lifecycle
Lecture 46: On-Premises Installation Oracle Virtual Box and setup of VM & Ubuntu, Basic Linux command, Download and Installation of Hadoop, Introduction to Hadoop, Core components of Hadoop, Hadoop working, Principle
Lecture 47: VM creation on Cloud (AZURE), Configuration & Insight to Single Node Hadoop Deployment (bsshrc, hadoop-env, core-site, hdfs-site, mapred-site, yarn-site), Format HDFS Namenode.
Lecture 48: HDFS Architecture, Hadoop Commands and Implementation
Lecture 49: MapReduce, MapReduce Implementation
Lecture 50: Introduction to Hive, Hive Installation, Hive Implementation
Lecture 51: Hive Query Language, SQL Operations
Lecture 52: HIVE_SQL Operations
Module 11 - SPARK
Lecture 53: Installation of Spark, PySpark, Introduction to Sqoop, Installation of Sqoop
Lecture 54: PySpark Query, Installation of HBase, HBase Query
Lecture 55: PIG Installation and Query
Lecture 56: PIG Query, Oozie
Lecture 57: Flume and Doubt Clear
Module 12 - Computer Vision
Lecture 58: Introduction to Image Processing, Feature Detection, OpenCV
Lecture 59: Convolution, Padding, Pooling & its Mechanisms
Lecture 60: Forward Propagation & Backward Propagation for CNN
Lecture 61: CNN Architectures like AlexNet, VGGNet, InceptionNet, ResNet, Transfer Learning
Module 13 - NLP
Lecture 62: Introduction to Text Mining, Text Processing using Python and Introduction to NLTK
Lecture 63: Sentiment Analysis, Topic Modeling (LDA) and Named Entity Recognition
Lecture 64: BERT (Bidirectional Encoder Representations from Transformers), Text Segmentation, Text Mining, Text Classification
Lecture 65: Automatic Speech Recognition, Introduction to Web Scraping
Module 14 - RL
Lecture 66: RL Framework, Component of RL Framework, Examples of Systems
Lecture 67: Types of RL Systems, Q-Learning
Module 15 - Introduction to Gen AI & Huggingface Transformers Platform
Lecture 68: Introduction to AI, Hype vs. Reality, Business Applications, Ethical Considerations, Introduction to Generative AI, From Text Generation to Multimodal Models, Potential and Challenges
Lecture 69: Introduction to Open Source Huggingface Transformers Platform, Review of NLP Basics & Text Pre-processing, Introduction to NLP Concepts: Language Representations, Tokenization, Part-of-Speech Tagging, Text Preprocessing
Lecture 70: Feature Engineering: Normalization, Stemming, Lemmatization, Stop Word Removal, Understanding Key NLP Applications Using Huggingface Platform
Lecture 71: Sentiment Analysis, Sentence Classification, Generating Text, Extracting an Answer from Text
Module 16 - Language Models and Transformer Models
Lecture 72: Understanding language models, Probability-based language models, Unsupervised learning language representations, Introduction to transformer models, What are transformer models
Lecture 73: Types of models: encoder –decoder, decoder only, Attention mechanism, Tasks that transformer models can do: translation, text summarization, Q&A, text generation, Zero shot, few shot text classification
Module 17 Large Language Models (LLMs)
Lecture 74: Other types of Generative AI algorithms, - GANs ( Generative Adverserial Networks), Variational Autoencoders (VAEs), Diffusion Models, Mixture of Experts, - Diffferent models available currently for image ( DALLE-2, Midjourney)
Lecture 75: Other types of Generative AI algorithms, - GANs ( Generative Adverserial Networks), Variational Autoencoders (VAEs), Diffusion Models, Mixture of Experts, - Diffferent models available currently for image ( DALLE-2, Midjourney)
Lecture 76: Hands on practice of NLP tasks using Huggingface library and opensource language models such as Bloom for finetuning a LLM, zero and few shot classification,-Applications of Generative AI in business
Lecture 77: Customer Insights & Sentiment Analysis- Personalized Marketing & Content Creation- Chatbots: Automating Customer Service and Support- Document Processing Automation
Module 18 - Langchain, AI Application Stack and Ethical Considerations
Lecture 78:Langchain, Applied use case for Gen AI – hands on exercise- Designing a custom chatbot- Data analytics using Gen AI model such as OpenAI API
Lecture 79: AI Application Stack: Infrastructure & foundation layer- Overview of AI infrastructure: cloud platforms, GPU, and distributed computing, Setting up an AI environment for generative models- Infrastructure considerations for scalable AI applications- Retrieval augmentation generation or RAG
Lecture 80: Hallucination, Data Privacy, Ethics, and Environmental Impact of AI & future of Work- Importance of data privacy in AI applications- Ethical considerations in AI development and Deployment- Environmental Impact and Sustainability in AI- The Future of Work: How AI Will Reshape Roles and Responsibilities
Lecture 81: Project Discussion session
Advanced Certification in Data Science & Gen AI with Visualizations Tools Skills Covered
Proficiency in Programming
Statistical Analysis
Advanced Machine Learning Techniques
Deep Learning and AI Techniques
Computer Vision Techniques
Natural Language Processing Techniques
Reinforcement Learning Techniques
Advanced Data Analysis in Excel
Data Analysis and Visualization
SQL querying
NoSQL Database Management
Big Data Management
Hadoop ecosystem tools
Spark Data Processing and Analytics
Transformer architectures Language Modeling
Generative Pre-trained Transformers (GPT)
Image Generation
Large-scale image synthesis
Generative Adversarial Networks (GANs)
Retrieval-Augmented Generation (RAG)
Advanced Certification in Data Science & Gen AI with Visualizations Tools Covered
Advanced Certification in Data Science & Gen AI with Visualizations Tools Program Benefits
Projects that you will Work On
Machine Learning
No. of Projects:10
Deep Learning
No. of Projects:10
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Job Obligation after this course
We can apply for jobs in
Analyze large datasets to identify trends, patterns, and insights to inform business decisions.
Design, develop, and deploy machine learning and AI models to solve specific problems.
Manage and maintain data pipelines, ensuring data quality and integrity across systems.
Develop and present data visualizations and reports to communicate findings effectively.
Implement and optimize algorithms for machine learning, deep learning, and AI applications.
Work with other departments to understand data needs and provide analytical support.
Integrate various data science and AI tools to create comprehensive solutions for complex problems.
Address and resolve data-related challenges and issues that arise in projects or operations.
Stay updated with the latest advancements in data science, AI, and related technologies.
Document processes, models, and findings to ensure reproducibility and clarity in your work.
Companies Hiring for this course
Batch Professional Profiles
Program Advisors
IITs
IIMs
NITs
Experts from the IT Industries.
Admission Details
The application process consists of three simple steps. An offer of admission will be made to selected candidates based on the feedback from the interview panel. The selected candidates will be notified over email and phone, and they can block their seats through the payment of the admission fee.
Course Fees & Financing
Course Fees
(50% OFF upto 31ˢᵗ March)
(Inclusive Of All Taxes)
Payment Partners
We partnered with financing companies to provide competitive finance option at 0% interest rate with no hidden costs
Upcoming Batches/Program Cohorts
| Batch | Date | Time | Batch Type |
|---|---|---|---|
| Online Live Instructor Led Session | 5th April 2025 | 10:00 AM | Full-Time |
| Online Live Instructor Led Session | 29th March 2025 | 02:00 PM | Part-Time |
Comparison with Others
| Aspect | Our Program | Other Programs |
|---|---|---|
| Comprehensive Coverage | Extensive range including data science, AI, Big Data, and visualization tools | May focus on narrower set of tools or specific domains |
| Hands-On Experience | Emphasis on practical projects and real-world datasets | May offer limited practical experience or fewer projects |
| Cutting-Edge Technologies | Includes the latest advancements like Generative AI | May not cover the latest technologies or focus on older tools |
| Big Data Tools | Covers Hadoop, Spark, NoSQL, MongoDB | May not include all Big Data tools or have limited coverage |
| Visualization Tools | Extensive training in Tableau and Power BI | May focus on one visualization tool or offer less depth |
| Integration of Tools | Integrated approach to using various tools together | Might not emphasize tool integration or offer siloed learning |
| Expert Instruction | Led by industry professionals with deep experience | May vary in instructor experience and industry relevance |
| Career Advancement | Prepares for a wide range of high-demand roles | May have a narrower focus or fewer career opportunities |
| Program Flexibility | Suitable for beginners to experienced professionals | May be tailored to specific experience levels or needs |
Self Assessments
Advanced Certification in Data Science & Gen AI with Visualizations Tools Training Faqs
A specialized curriculum that gives professionals additional abilities in data analysis, generative AI model creation, and data visualization is the additional Certification in Data Science and Generative AI with Visualization Tools. Statistical analysis, machine learning, deep learning (including GANs and NLP), and technologies like as Tableau and Power BI are all covered in the curriculum.
Python programming, R programming, statistical analysis, machine learning, deep learning, computer vision techniques, natural language processing, reinforcement learning, SQL querying, data visualization (Tableau, Power BI), Java programming, Hadoop ecosystem tools, Spark data processing and analytics, NoSQL database managementNatural Language Processing (NLP), Transformer architectures Language Modeling, Generative Pre-trained Transformers (GPT) Image Generation, Large-scale image synthesis Generative Adversarial Networks (GANs), Retrieval-Augmented Generation (RAG)
Yes Online cources are available.
60+ Projects are covered during this cources.
duration of each lecture is 3 houre.
yes Available in online mode.
Algorithms that can produce new data that is comparable to the data they were trained on are referred to as generative AI. This comprises models that can produce realistic text, music, graphics, and other types of data, such as GANs and transformers.
50+ Projects are done during the course on the topics machine learning, Natural language processing data analysis and generative ai.
duration of each lecture of 3 houre.
For learn Master advanced data science and AI techniques, enhance data visualization skills
No work experience is required basic knowledge of data science is required.
10 to 11 months.
Introduction to Data Science- 03 Hrs, Python /R Programming-30 hrs, Statistics-15 hrs, Machine Learning-15 Hrs, Deep Learning-15 Hrs, Computer Vision-12 Hrs, Natural Language Processing-12 Hrs, Reinforcement Learning -06 Hrs, SQL,-12 Hrs Tableau-12 Hrs, Power BI- 18 Hrs, Java-06 Hrs, Hadoop-24 Hrs, Spark-15 Hrs, NoSql & Mongo DB-5 Hrs, Generative AI-40 Hrs
There are career opportunity after the course completed: AI Specialist, Data Scientist, Machine Learning Engineer.
Entry-level: 8-15 lakhs per annum
Mid-level: 15-30 lakhs per annum
Senior-level: 30+ lakhs per annum
Mid-level: 15-30 lakhs per annum
Senior-level: 30+ lakhs per annum
A degree in Engineering, Mathematics, Science, or any equivalent field is ideal for pursuing a career in Artificial Intelligence and Data Science.
INR Fees(Exclusive of GST) : INR 289,999.00
INR Fee(Inclusive of GST) : INR 342,199.00
USD Fee: 3475(subject to be change)
INR Fee(Inclusive of GST) : INR 342,199.00
USD Fee: 3475(subject to be change)
