POPULAR
immersive learning
500 Hours
NASSCOM CERTIFIED DATA SCIENCE PROGRAM
Data Science is a cross disciplinary blend of tools and technologies which work conjointly to understand business, clients, patterns and resolving inquisitions which we yet not perceived from the data stored from data warehouses and all possible web applications. This program is the perfect blend of Statistics, Programming, Machine learning, Deep Learning, Artificial Intelligence, Data Visualization and big data designed to give you a holistic view of Data Science.
OUR KNOWLEDGE PARTNERS
500 HOURS-NASSCOM CERTIFIED DATA SCIENCE PROGRAM
Our 500-hours NASSCOM Certified Data Science with AI program course encompasses a wide array of topics, including Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Artificial Intelligence (AI), Natural Language Processing (NLP), Computer Vision (CV), and Generative AI (Gen AI). This comprehensive curriculum ensures that participants gain in-depth knowledge and hands-on experience across various domains within the field of data science. The ML component delves into algorithms and techniques for pattern recognition and predictive modeling, while DL explores neural networks and advanced deep learning architectures. RL focuses on learning optimal decision-making strategies through interactions with an environment, enhancing participants’ skills in decision science. AI concepts cover a broad spectrum of topics, including problem-solving, intelligent agents, and ethical considerations in AI applications. NLP equips participants with the tools and techniques to analyze and understand human language, while CV enables them to work with visual data and image recognition systems. Lastly, the Gen AI segment introduces participants to generative models and creative AI applications, fostering innovation and creativity in their data science endeavors. By covering these diverse areas, our course ensures that participants develop a holistic understanding of full-stack data science and are well-equipped to tackle complex challenges in the field.
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Meet Your Mentors
Program Structure
- 50 hour Pre-Learning: Before you embark on the live academic session, get ready for the Program. You will get a series of online recorded tutorials to understand the structure of Data Science to know about the fundamentals which would enrich your future learning experience..
- 160 hours Program: Here, you will get execution-based learning experience on Advance Excel, SQL, R Programming, Python, Statistics, Machine Learning, Deep Learning, Artificial Intelligence, Tableau, Power BI, Big Data with Hadoop & Spark along with Advanced Gen AI.
- 160 hours Post Program:Learning does not stop here. After completing the modular training, you will work on Domain-specific Project, Assignments. Doubt clearing is also provided. You will be working on different capstone projects from a huge repository of data sets.
- 130 Hours of Electives:Grab an opportunity to add the advanced knowledge on data science, artificial intelligence, big data, java, non-relational data bases, business intelligence tool, natural language processing, object detection to the existing pool of knowledge by opting the electives. Here you will be working on advance concepts of statistics, machine learning algorithms, SQL and business intelligence tools like Tableau and Power BI.
LEARN WITH A WORLD CLASS CURRICULUM
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Lecture 1:Orientation (Introduction to Data Science, Scope of Data Science)
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📚No. of Lectures: 1
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⏳Duration of Lecture: 1.5 Hour
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📝Assessment: 0
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🌟Assignment: 0
Module 1. Data Science with Machine Learning
- Lecture 2: Introduction to Linux, Linux Distribution, Types of shell, Package Installation, Basic Linux Commands, Shell scripting
- Lecture 3: Sorting Data, Filtering Data, Charts, Column Chart, Pie Chart .
- Lecture 4: Pivot Tables, Lookup Function, Vlookup, Hlookup, Match Function .
- Lecture 5: VBA, Macros, Dashboards, Interview Questions.
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📚No. of Lectures: 4
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⏳Duration of Lecture: 12 Hour
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📝Assessment: 1
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🌟Assignment: 1
- Lecture 6: Introduction to Python, Why Python, Variables, Operators, Strings, Indexing .
- Lecture 7: Block Structure, Data Structures, Functions, Creating Function, Calling a function, Function Parameter.
- Lecture 8: Lambda Function, *args, **kwargs, Conditional Statement, Loops and it’s Control Statement.
- Lecture 9: Class, Creation, __init__(), Inheritance, Polymorphism .
- Lecture 10: Libraries and Packages (Numpy, Pandas, Matplotlib, Seaborn).
- Lecture 11: Libraries and Packages (Numpy, Pandas, Matplotlib, Seaborn) .
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📚No. of Lectures: 6
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⏳Duration of Lecture: 18 Hours
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📝Assessment: 1
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🌟Assignment: 1
- Lecture 12: Introduction to Statistics, Descriptive Statistics, Sample, Population, Major of Central Tendency, Standard Deviation, .
- Lecture 13: Variance, Range, IQR, Outliers, Correlation, Covariance Skewness, Kurtosis, Probability .
- Lecture 14: Probability distributions, Central Limit Theorem, Binomial and Poisson Distribution, Normal Distribution.
- Lecture 15: Type I & Type II Error, T-test, Z-test, Hypothesis Testing Interview Questions
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📚No. of Lectures: 4
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⏳Duration of Lecture: 12 Hours
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📝Assessment: 1
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🌟Assignment: 1
- 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.
- Lecture 21: Introduction to Neural Network, Foreward Propagation, Activation Function .
- Lecture 22: Activation Function(Linear, Sigmoid, Relu, Leaky Relu), Optimizers, Gradient Descent, Stochastics 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 and Multiplicative) ARIMA,.
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📚No. of Lectures: 10
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⏳Duration of Lecture: 30 Hours
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📝Assessment: 1
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🌟Assignment: 1
- Lecture 26: What is R Programming, Variables and Data Type in R .
- Lecture 27: Logical Operators,Vectors,List,Matrix,Data Frame,Flow Control, Functions in R.
- Lecture 28: Data Manipulation in R- dplyr, Data Manipulation in R- tidyr .
- Lecture 29: Data Visualization In R .
- Lecture 30: Project Discussion and Doubts Class.
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📚No. of Lectures: 5
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⏳Duration of Lecture: 13.5 Hours
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📝Assessment: 1
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🌟Assignment: 1
Module 2. Artificial Intelligence with CV, NLP and RL
- Lecture 31: Introduction to Image Processing, Feature Detection, OpenCV.
- Lecture 32: Convolution, Padding, Pooling & its Mechanisms.
- Lecture 33: Forward Propagation & Backward Propagation for CNN .
- Lecture 34: CNN Architectures like AlexNet, VGGNet, InseptionNet, ResNet,Transfer Learning.
- 📚No. of Lectures: 4
- ⏳Duration of Lecture: 12 Hour
- 📝Assessment: 1
- 🌟Assignment: 1
- Lecture 35: Introduction to Text Mining, Text Processing using Python and Introduction to NLTK
. - Lecture 36: Sentiment Analysis, Topic Modeling (LDA) and Name- Entity Recognition
. - Lecture 37: BERT (Bidirectional Encoder Representations from Transformers), Text Segmentation, Text Mining, Text Classification.
- Lecture 38: Automatic Speech Recognition, Introduction to Web Scraping
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- 📚No. of Lectures: 4
- ⏳Duration of Lecture: 12 Hour
- 📝Assessment: 1
- 🌟Assignment: 1
- Lecture 39: RL Framework, Component of RL Framework, Exampes of
Systems. - Lecture 40: Types of RL Systems, Q-Learning.
- 📚No. of Lectures: 2
- ⏳Duration of Lecture: 6 Hour
- 📝Assessment: 1
- 🌟Assignment: 1
- Lecture 41: Introducing container technology, Creating containerized services, Managing containers
- 📚No. of Lectures: 1
- ⏳Duration of Lecture: 1.5 Hour
- 📝Assessment: 1
- 🌟Assignment: 1
Module 3. Generative AI
- Lecture 42: Introduction to AI, Hype vs. Reality, Business Applications, Ethical Considerations, Introduction to Generative AI, From Text Generation to Multimodal Models, Potential and Challenges.
- 📚No. of Lectures: 1
- ⏳Duration of Lecture: 3 Hours
- 📝Assessment: 1
- 🌟Assignment: 1
- Lecture 43: 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 44: Feature Engineering: Normalization, Stemming, Lemmatization, Stop Word Removal, Understanding key NLP Applications using Huggingface platform.
- Lecture 45: Sentiment analysis, Sentence classification, Generating text, Extracting an answer from text.
- 📚No. of Lectures: 3
- ⏳Duration of Lecture: 9 Hours
- 📝Assessment: 1
- 🌟Assignment: 1
- Lecture 46: Understanding language models, Probability-based language models, Unsupervised learning language representations, Introduction to transformer models, What are transformer models.
- Lecture 47: 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.
- 📚No. of Lectures: 2
- ⏳Duration of Lecture: 6 Hour
- 📝Assessment: 1
- 🌟Assignment: 1
- Lecture 48: Introduction to Large Language Models (LLMs)
– Structure of popular models.
– Types of Models: text to text, text to image, text to video, multimodal. - Lecture 49: 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 50: 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 51: – Customer Insights & Sentiment Analysis
– Personalized Marketing & Content Creation
– Chatbots: Automating Customer Service and Support
– Document Processing Automation .
- 📚No. of Lectures: 4
- ⏳Duration of Lecture: 12 Hours
- 📝Assessment: 1
- 🌟Assignment: 1
- Lecture 52: 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.
- 📚No. of Lectures: 1
- ⏳Duration of Lecture: 3 Hours
- 📝Assessment: 1
- 🌟Assignment: 1
- Lecture 53: Langchain, Applied use case for Gen AI
– hands on exercise
– Designing a custom chatbot
– Data analytics using Gen AI model such as OpenAI API
- 📚No. of Lectures: 1
- ⏳Duration of Lecture: 3 Hour
- 📝Assessment: 1
- 🌟Assignment: 1
- Lecture 54: 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
- 📚No. of Lectures: 1
- ⏳Duration of Lecture: 3 Hours
- 📝Assessment: 1
- 🌟Assignment: 1
- Doubt Session and Project Class .
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📚No. of Lectures: 1
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⏳Duration of Lecture: 1.5 Hours
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📝Assessment: 1
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🌟Assignment: 1
SKILLS YOU WILL POSSESS
✔️ Data Wrangling
✔️ Data Cleaning
✔️ Data Visualization
✔️ Big Data Architecture/Engineering
✔️ Data Analysis
✔️ Descriptive Analytics
✔️ Machine learning Modelling
✔️ Predictive Analytics
✔️ Text Processing
✔️ Image Processing
✔️ Sentiment Analysis
✔️ Video Analytics
✔️ Emotion Analysis
✔️ Face Recognition/Detection
✔️ Optical Character Recognition
PROGRAM BENEFITS
✔️ Cutting Edge Curriculum: Hand crafted Course content made by Experts from various Industries. Learn through Practical case studies and multiple projects.
✔️ On the Go Learning: Online accessible E-learning Material, recorded lectures, case studies and Research Paper through our system.
✔️ Build Solid Foundation: 230 hours focused course on Data Science.
✔️ Industry Mentorship: Get 1 to 1 guidance from Industry experts and start your career in Data Science.
✔️ Earn a Government of India approved & globally recognized certificate by NASSCOM IT- ITes SSC by clearing NASSCOM assessment examination.
Course Certificates
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Placement Assistance
Exclusive access
Mock Interview Preparation
1 on 1 Career Mentoring Sessions
Career Oriented Sessions
Resume & LinkedIn Profile Building
Real World Projects
Projects will be a part of Our 500 Hours NASSCOM Certified Data Science with AI Certification Program to solidify your learning. They ensure you have real-world experience in Development and Operations.
- Practice 25+ Essential Tools
- Designed by Industry Experts
- Get Real-world Experience
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Admission Details
Submit Application
Tell us a bit about yourself and why you want to join this program
Application Review
An admission panel will shortlist candidates based on their application
Admission
Selected candidates will be notified within 1week.
Program Fees
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Starting from ₹4,999*
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Total Admission Fees
₹94,399*(Including GST)
USD $1350
FAQS
The program spans 500 hours of immersive learning, covering a wide range of topics in data science and AI.