POPULAR
immersive learning
520 Hours
NASSCOM 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
520 HOURS NASSCOM DATA SCIENCE PROGRAM
Our 520-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..
- 177 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, Big Data with Hadoop & Spark.
- 177 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, 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.
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 Advanced Excel, Python, Statistics, Machine Learning, Deep Learning and R-Programming
- 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. Business Intelligence Using SQL, and Tableau
- Lecture 31: Orientation Session(Introduction to Business Intelligence).
- 📚No. of Lectures: 1
- ⏳Duration of Lecture: 1.5 Hour
- 📝Assessment: 0
- 🌟Assignment: 0
- 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: 4
- ⏳Duration of Lecture: 12 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. BIGDATA
- Lecture 41: Orientation- Introduction to Big Data Analytics.
- Lecture 42: Types of Data,Introduction to Bigdata(History,V’s of Bigdata,
Advantages & Disadvantages of BigData ), Use of Bigdata in different
sectors, Introduction to Hadoop, Scaling (Horizontal and Vertical),
Challenges in Scaling, Concept and challenges in parallel computing,
Distributed Computing and use in Hadoop,
Intro to Tools in Hadoop, Life cycle of Bigdata Analytics. - Lecture 43: 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,HDFS Architecture. - Lecture 44: 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 45: Hadoop Commands and implementation.
- Lecture 46: Mapreduce, Mapreduce Implementation.
- Lecture 47: Mapreduce Implementation, Introduction to Hive,
Hive Installation,Hive Implementaion. - Lecture 48: Hive Query Language, SQL Opeartions.
- Lecture 49: HIVE_SQL Opeartions.
- Lecture 50: Introduction to Sqoop, Hbase, Installation of Sqoop, Installation of Hbase, Installation of Spark, Hbase Query.
- Lecture 51: Hbase Query, PySpark Query.
- Lecture 52: PIG Installation and Query.
- Lecture 53: Oozie.
- Lecture 54: Flume.
- Lecture 55: Project Discussion and Doubt Clear.
- 📚No. of Lectures: 15
- ⏳Duration of Lecture: 43.5 Hour
- 📝Assessment: 1
- 🌟Assignment: 1
Module 4. Artificial Intelligence
- Lecture 56: Introduction to Image Processing, Feature Detection, OpenCV.
- Lecture 57: Convolution, Padding, Pooling & its Mechanisms.
- Lecture 58: Forward Propagation & Backward Propagation for CNN .
- Lecture 59: CNN Architectures like AlexNet, VGGNet, InseptionNet, ResNet,Transfer Learning.
- 📚No. of Lectures: 4
- ⏳Duration of Lecture: 12 Hour
- 📝Assessment: 1
- 🌟Assignment: 1
- Lecture 60: Introduction to Text Mining, Text Processing using Python and Introduction to NLTK.
- Lecture 61: Sentiment Analysis, Topic Modeling (LDA) and Name- Entity Recognition.
- Lecture 62: BERT (Bidirectional Encoder Representations from Transformers), Text Segmentation, Text Mining, Text Classification.
- Lecture 63: Automatic Speech Recognition, Introduction to Web Scraping.
- 📚No. of Lectures: 4
- ⏳Duration of Lecture: 12 Hour
- 📝Assessment: 1
- 🌟Assignment: 1
- Lecture 64: RL Framework, Component of RL Framework, Exampes of Systems.
- Lecture 65: Types of RL Systems, Q-Learning.
- 📚No. of Lectures: 2
- ⏳Duration of Lecture: 6 Hour
- 📝Assessment: 1
- 🌟Assignment: 1
- Lecture 55: Introducing container technology, Creating containerized services, Managing containers
- 📚No. of Lectures: 1
- ⏳Duration of Lecture: 1.5 Hour
- 📝Assessment: 1
- 🌟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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Total Admission Fees
₹1,00,299*(Including GST)
USD $1380
faQS
The program spans 520 hours of immersive learning, covering a wide range of topics in data science and AI.