POPULAR
immersive learning
440 Hours
NASSCOM DATA SCIENCE PROGRAM
Data Science combines cutting-edge tools and technologies to uncover insights from complex datasets, enabling a deep understanding of business dynamics, customer behavior, and underlying patterns. This program seamlessly integrates advanced Excel, Python, R, SQL, Tableau, and techniques from Computer Vision, NLP, Reinforcement Learning, Statistics, Machine Learning, and Deep Learning. It offers a comprehensive view of data science, equipping you with the skills to analyze, visualize, and make informed decisions based on data across various industries.
OUR KNOWLEDGE PARTNERS
440 HOURS-NASSCOM DATA SCIENCE PROGRAM
Our 440-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) and Computer Vision (CV). 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. 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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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..
- 137 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.
- 137 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.
- 100 Hours of Electives:Grab an opportunity to add the advanced knowledge on data science, artificial intelligence, 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.
LEARN WITH A WORLD CLASS CURRICULUM
Module 1. Data Science with Advanced Excel, Python, Statistics, Machine Learning, Deep Learning and R-Programming
- Lecture 2: Microsoft Excel Overview, Formatting Excel, Shortcuts and Basic Formulas.
- 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) .
- Lecture 11: LLibraries and Packages (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
- Lecture 30: Project Discussion and Doubts Class.
- 📚No. of Lectures: 1
- ⏳Duration of Lecture: 1.5 Hours
- 📝Assessment: 1
- 🌟Assignment: 1
Module 2. Business Intelligence Using SQL, and Tableau
- Lecture 31: Introduction to Business Intelligence. Role of BI Analyst, Market demand, Salary expectations, Interview Questions etc.
- Lecture 32: Basic of Database, Types of Database, Data Types, SQL Operators, Expression (Boolean, Date, Numeric), Create, Insert.
- Lecture 33: Drop, Truncate, Delete, Alter, Update, Select, Range, Operator, IN, Wildcard, Like Clause.
- Lecture 34: Constraint, Aggregation Function,Group by, Order by , Having.
- Lecture 35: Joins, Case, Complex Queries, Doubt Clearing
- 📚No. of Lectures: 5
- ⏳Duration of Lecture: 15 Hour
- 📝Assessment: 1
- 🌟Assignment: 1
- Lecture 36: Tableau Desktop, Tableau products.
- Lecture 37: Data import, Measures, Filters.
- Lecture 38: Data transformation, Marks, Dual Axis.
- Lecture 39: Manage worksheets, Data visualization, Dashboarding,Project.
- 📚No. of Lectures: 4
- ⏳Duration of Lecture: 12 Hour
- 📝Assessment: 1
- 🌟Assignment: 1
- Lecture 40: Doubts and Project Discussion.
- 📚No. of Lectures: 1
- ⏳Duration of Lecture: 1.5 Hour
- 📝Assessment: 1
- 🌟Assignment: 1
Module 3. Artificial Intelligence
- Lecture 41: Introduction to Image Processing, Feature Detection, OpenCV.
- Lecture 42: Convolution, Padding, Pooling & its Mechanisms..
- Lecture 43: Forward Propagation & Backward Propagation for CNN .
- Lecture 44: CNN Architectures like AlexNet, VGGNet, InseptionNet, ResNet,Transfer Learning.
- 📚No. of Lectures: 4
- ⏳Duration of Lecture: 12 Hours
- 📝Assessment: 1
- 🌟Assignment: 1
- Lecture 45: Introduction to Text Mining, Text Processing using Python and Introduction to NLTK..
- Lecture 46: Sentiment Analysis, Topic Modeling (LDA) and Name- Entity Recognition..
- Lecture 47: BERT (Bidirectional Encoder Representations from Transformers), Text Segmentation, Text Mining, Text Classification.
- Lecture 48: Automatic Speech Recognition, Introduction to Web Scraping..
- 📚No. of Lectures: 4
- ⏳Duration of Lecture: 12 Hours
- 📝Assessment: 1
- 🌟Assignment: 1
- Lecture 49: RL Framework, Component of RL Framework, Exampes of Systems.
- Lecture 50: Types of RL Systems, Q-Learning.
- 📚No. of Lectures: 2
- ⏳Duration of Lecture: 6 Hours
- 📝Assessment: 1
- 🌟Assignment: 1
- Lecture 51: Introducing container technology, Creating containerized services, Managing containers.
- 📚No. of Lectures: 1
- ⏳Duration of Lecture: 1.5 Hours
- 📝Assessment: 1
- 🌟Assignment: 1
SKILLS YOU WILL POSSESS
✔️ Data Manipulation
✔️ Data Wrangling
✔️ Data Cleaning
✔️ Data Visualization
✔️ Data Analysis
✔️ Descriptive Analytics
✔️ Machine learning Modelling
✔️ Predictive Analytics
✔️ Text Processing
✔️ Image Processing
✔️ Sentiment 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: 137 Hours focused course on Data Science and AI .
✔️ 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
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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 ₹2,999*
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Total Admission Fees
₹82,599*(Including GST)
USD $1130
FAQS
The program spans 440 hours of immersive learning, covering a wide range of topics in data science and AI.