Best Data Science Course in Noida with AI

Our data science course in Noida that covers AI, machine learning, and Python from scratch. Real projects, expert trainers, and 100% job assistance.

Certification Partners

Our Students Placed In These Companies

Data Science with AI Course Syllabus

ADVANCE PYTHON PROGRAMMING

1. Introduction to Python

Simple
Open Source
High Level Programming
Portable
Object and Procedure Oriented
Easy to Maintain

2. Input and Output Functions

The Input() function
The print() function

n use of % percent operator

n use of format()

3. Variable and Data Types

What is a Variable
Assign Values to variable
Typecasting
Data Types in Python

n Numeric

n String

n Boolean

n Componup

n List

n Tuple

n Set

n FrozenSet

n Dictionary

4. Operators in Python

Types of Operators

n Arithmetic Operators

n Comparison Operators

n Assignment Operators

n Logical Operators

n Bitwise Operators

n Identity Operators

n Membership Operators

n Operators Associativity

n Operator Precedence

n BEDMAS

5. Conditional Statements in Python

The if Statement
if- else statement
The elif Statement
Nested if-else ladder

6. Loops in Python

Use of while loop
use of for loop
range) function
arange) function
The break Statement
The continue Statement
The pass statement

7. User defined functions in python

Define a function
calling a function
Types of function
UDF
Function Arguments
Functions Parameters
Anonymous Function
Global and Local Variable
lambda
map
reduce
Filter
Mathematical Function
Trigonometric Function
Random Function

8. Strings in python

creating strings
difference between “” &”
creating multiline comment
basic string operations
creating slices in strings
String Built-in Functions
n capitalize()
n upper()
n lower()
n isalnum()
n isalpha()
n isnumeric()
n isdecimal()
n islower()
n isupper()

9. List

Access List items
Change List items
Add List Items
Remove List Items
Loop List
List Comprehension
Copy List
Join List
List built-in Method
n append()
n count()
n extend()
nreverse()
n sort()

10. Tuple

Access Tuples
Update Tuples
UnpackTuples
LoopTuple
JoinTuples
Tuplebuilt-in Methods
n index()
n count()

11. Sets

Create Sets
Access Set items
Add Set items
Remove Set items
Loop Sets
Join Sets

12. Dictionaries

Create Dictionaries
Access Dictionary items
Change Dictionary items
Add Dictionary items
Remove Dictionary items
Loop Dictionaries
Copy Dictionaries
Nested Dictionaries
Dictionary built-in Methods
n keys()
n values()
n items()
n get()

13. Python Modules

Date Module
Time Module
os Module
The import statement
The from…import Statement

14. File Handling in Python

Readfiles
Write/ Create files
Delete files
Rename files

15. Exception Handling

Error in Python Program
Syntax error
Exception
Types of Exceptionvelopment
Handling Exception in Python
Raising Exception
User Defined Exception

16. Regular Expression

Match function
Search function
Matching VS Searching
Modifiers
Patterns

ITERATORS- GENERATORS

STRUCTURE QUERY LANGUAGE (SQL)

PYTHON AND DATA SCIENCE LIBRARY

STATISTICAL METHODS FOR DECISION MAKING

MACHINE LEARNING

1. Introduction To Machine Learning

Importance Of Machine Learning
How did Machine Learning work?
Relationship Between Artificial Intelligence, Machine Learning, and Data Science
Different Categories of Machine Learning: Supervised, Unsupervised and Reinforcement
Applications of Machine Learning

2. Supervised Machine Learning

Linear Regression
Simple Linear Regression
Multiple Linear Regression
Performance Metrics: Accuracy Scores, R^2 And Adjusted RA2
To Compare the Model with Different Numbers of Independent Variables.
Approaches to Feature Selection: Univariate Selection, Feature Importance, RFE
Parameter Tuning and Model Evaluation
Data Transformation and Normalization
Ridge & Lasso Regression (L1 & L2)

3. Logistic Regression

Concept of Logistic Regression
Univariate Logistic Regression
Multivariate Logistic Regression: Model Building and Evaluation
Dealing with Categorical Independent Variable – Hot Encoding Vs Dummy Variable

4. Decision Trees

Concept of Decision Trees
Importance and Usage of Ginny And Entropy
Information Gain
Visualizing Decision Trees Nodes and Splits
Working of The Decision Tree Algorithm
Extending Decision Trees to Regressing Problem
Evaluating Decision Trees Models: Accuracy, Precision, Recall, Confusion Matrix

5. Classification – k Nearest Neighbor (KNN)

Classification and Regression
Application, Advantages and Disadvantages
Distance Metric – Euclidean, Manhattan, Chebyshev, Minkow
Measuring accuracy using Cross-Validation, Stratified k-fold, Confusion Matrix,Precision, Recall, Fl-score.

6. Naive Bayes Classifier

The Bayes Theorem
The Gaussian Naïve’s Bayes Classifier – Assumptions of The Naïve Bayes Classifier, Functioning of The Naïve’s Bayes Algorithm

7. Classification SVM(Support Vector Machine)

Classification and Regression
Separating line, Margin and Support Vectors
Linear SVC Classification
Polynomial Kernel – Kernel Trick
Gaussian Radial Basis Function (RBF)
Grid Search to tune hyper-parameters
Support Vector Regression

8. Unsupervised Learning – Clustering

Introduction to Clustering
K-Means Clustering: Linkage
Use of Elbow Curve & Silhouette Score
Hierarchical Clustering – Agglomerative & Divisive
Distance Matrix, Dendrogram

9. Density based clustering

DBSCAN (Density based clustering) Technique
Density based clustering
Eps: Density
Core, Border and Noise points
Density edge and Density connected points
DBSCAN Algorithm
Hyper Parameters: MinPts and EpsA
Advantages and Limitations of DBSCAN

10. Principal Component Analysis – PCА

Noise in The Data and Dimensionality Reduction
Capturing Variance – The Concept of Principal
Components
Assumptions in Using PCA
Eigenvectors and Orthogonality of Principal Components
What Is the Complexity Curve?
Advantages of Using PCA
The Working of The PCA Algorithm
Singular Value Decomposition

11. Ensemble Modelling

Popular Ensembles
Bagging
Bootstrap
Introduction to Random Forests
Feature Importance in Random Forests
Boosting
How Boosting Algorithm Works?
Adaboost
Gradient Boost
XGBoost

DEEP LEARNING

1. Introduction to Deep Learning

What is Deep Learning?
Why Deep Learning?
Relationship Between Al, Machine Learning, and Deep Learning
History and Evolution of Deep Learning
Challenges Motivating Deep Learning
Key Applications of Deep Learning

2. Artificial Neural Networks (ANN

Importance of ANN in Al & Machine Learning
Biological Inspiration: How ANN Mimics the Human Br
Input Layer: Receiving raw data
Hidden Layers: Processing and feature extraction
Output Layer: Generating predictions
Activation Functions: ReLU, Sigmoid, Tanh, Softmax
Training and Optimization in ANN
Forward and Backpropagation
Loss Functions in ANN (MSE, Cross-Entropy)
Gradient Descent & Optimization Algorithms (SGD, Adam,RMSprop
Overfitting & Regularization (Dropout, Batch Normalization)
Applications of ANN
Image & Speech Recognition
Natural Language Processing (NLP)
Financial Market Forecasting
Healthcare & Disease Prediction
Implementing ANN in Python
Using TensorFlow & Keras
Building & Training an ANN Model
Evaluating Model Performance

3. Convolutional Neural Networks (CNN)

What is a CNN?
Why CNNs for Image Processing?
How CNNs Differ from Traditional Neural Networks
Architecture of CNN
Convolutional Layer: Feature extraction
Pooling Layer: Dimensionality reduction (Max Pooling, Average Pooling)
Fully Connected Layer: Classification and output
Activation Functions: ReLU, Softmax
Types of CNN Architectures
LeNet-5 – Early CNN model
AlexNet – Breakthrough in deep learning
VGGNet – Deeper architectures
ResNet – Solving vanishing gradient problem
InceptionNet – Multi-scale feature learning
Training a CNN
Data Augmentation for Better Performance
Loss Functions for CNN (Categorical Cross-Entropy, MSE)
Optimization Techniques (Adam, SGD)
Applications of CNN
Image Classification: Face Recognition, Object Detection
Medical Image Analysis: Disease Detection (X-rays, MRIs)
Autonomous Vehicles: Lane detection, Pedestrian recognition
Augmented Reality & Deepfake Technology
Implementing CNN in Python
Using TensorFlow & Keras
Building a CNN Model for Image Classification

4. Recurrent Neural Networks (RNN)

What is an RNN?
Why RNNs for Sequential Data?
Difference Between RNN and Traditional Neural Networks
Architecture of RNN
Input, Hidden, and Output Layers
Recurrent Connections: Handling time-series & sequential data
Backpropagation Through Time (BPTT)
Types of RNN Architectures
Basic RNN – Simple recurrent structure
Long Short-Term Memory (LSTM) – Solving the vanishing gradient problem
Gated Recurrent Unit (GRU) – Efficient alternative to LSTM
Bidirectional RNN (Bi-RNN) – Learning from past and future context
Applications of RNN

5. Natural Language Processing (NLP) with Deep Learning

Introduction to NLP
What is Natural Language Processing?
Importance of NLP in Al
Key NLP Concepts
Tokenization, Stemming, and Lemmatization
Stopwords & Named Entity Recognition (NER)
POS Tagging & Chunking
Bag of Words (BoW) & TF-IDF
Deep Learning for NLP
Word Embeddings (Word2Vec, GloVe, FastText)
Recurrent Neural Networks (RNNs) for NLP
Transformers & BERT for NLP
Attention Mechanisms in NLP
Applications of NLP
Chatbots & Virtual Assistants
Text Classification & Sentiment Analysis
Speech-to-Text & Text-to-Speech Conversion
Implementing NLP Models
Using NLTK, Spacy, TensorFlow, and Transformers
Sentiment Analysis & Text Generation

6. Deep Learning with Autoencoders

What are Autoencoders?
Importance in Deep Learning
Applications of Autoencoders
Architecture of Autoencoders
Encoder: Extracting meaningful features
Latent Space: Compact representation
Decoder: Reconstructing the original data
Types of Autoencoders
Vanilla Autoencoder: Basic architecture
Denoising Autoencoder: Removing noise from data
Sparse Autoencoder: Learning useful representations
Variational Autoencoder (VAE): Generative modeling
Convolutional Autoencoder (CAE): Image processing applications
Applications of Autoencoders
Data Compression & Feature Extraction
Anomaly Detection
Image Denoising
Generative Modeling
Implementing Autoencoders in Python
Using TensorFlow & Keras

7. Computer Vision with Deep Learning

What is Computer Vision?
The Evolution of Computer Vision
Importance and Real-World Applications
Key Concepts in Computer Vision
Image Processing vs. Computer Vision
Understanding Pixels and Image Representation
Feature Extraction and Object Recognition
Techniques in Computer Vision
Image Processing: Filters, Edge Detection, Histogram Equalization
Feature Detection & Matching: SIFT, SURF, ORB
Object Detection: YOLO, SSD, Faster R-CNN
Image Segmentation: Semantic & Instance Segmentation
Role of Convolutional Neural Networks (CNNs)
Transfer Learning for Computer Vision
Generative Adversarial Networks (GANs) for Image Generation
Autoencoders for Image Compression
Applications of Computer Vision
Healthcare: Medical Image Analysis (X-rays, MRIs)
Autonomous Vehicles: Object Detection & Lane Detection
Security: Facial Recognition & Surveillance
Implementing Computer Vision in Python ES
OpenCV for Image Processing Con
Real-time Object Detection with YOLO

ARTIFICIAL INTELLIGENCE

PROPOSED PROJECTS

ADVANCE PYTHON PROGRAMMING

1. Introduction to Python

Simple
Open Source
High Level Programming
Portable
Object and Procedure Oriented
Easy to Maintain

2. Input and Output Functions

The Input() function
The print() function

n use of % percent operator

n use of format()

3. Variable and Data Types

What is a Variable
Assign Values to variable
Typecasting
Data Types in Python

n Numeric

n String

n Boolean

n Componup

n List

n Tuple

n Set

n FrozenSet

n Dictionary

4. Operators in Python

Types of Operators

n Arithmetic Operators

n Comparison Operators

n Assignment Operators

n Logical Operators

n Bitwise Operators

n Identity Operators

n Membership Operators

n Operators Associativity

n Operator Precedence

n BEDMAS

5. Conditional Statements in Python

The if Statement
if- else statement
The elif Statement
Nested if-else ladder

6. Loops in Python

Use of while loop
use of for loop
range) function
arange) function
The break Statement
The continue Statement
The pass statement

7. User defined functions in python

Define a function
calling a function
Types of function
UDF
Function Arguments
Functions Parameters
Anonymous Function
Global and Local Variable
lambda
map
reduce
Filter
Mathematical Function
Trigonometric Function
Random Function

8. Strings in python

creating strings
difference between “” &”
creating multiline comment
basic string operations
creating slices in strings
String Built-in Functions
n capitalize()
n upper()
n lower()
n isalnum()
n isalpha()
n isnumeric()
n isdecimal()
n islower()
n isupper()

9. List

Access List items
Change List items
Add List Items
Remove List Items
Loop List
List Comprehension
Copy List
Join List
List built-in Method
n append()
n count()
n extend()
nreverse()
n sort()

10. Tuple

Access Tuples
Update Tuples
UnpackTuples
LoopTuple
JoinTuples
Tuplebuilt-in Methods
n index()
n count()

11. Sets

Create Sets
Access Set items
Add Set items
Remove Set items
Loop Sets
Join Sets

12. Dictionaries

Create Dictionaries
Access Dictionary items
Change Dictionary items
Add Dictionary items
Remove Dictionary items
Loop Dictionaries
Copy Dictionaries
Nested Dictionaries
Dictionary built-in Methods
n keys()
n values()
n items()
n get()

13. Python Modules

Date Module
Time Module
os Module
The import statement
The from…import Statement

14. File Handling in Python

Readfiles
Write/ Create files
Delete files
Rename files

15. Exception Handling

Error in Python Program
Syntax error
Exception
Types of Exceptionvelopment
Handling Exception in Python
Raising Exception
User Defined Exception

16. Regular Expression

Match function
Search function
Matching VS Searching
Modifiers
Patterns

ITERATORS- GENERATORS

STRUCTURE QUERY LANGUAGE (SQL)

PYTHON AND DATA SCIENCE LIBRARY

STATISTICAL METHODS FOR DECISION MAKING

MACHINE LEARNING

1. Introduction To Machine Learning

Importance Of Machine Learning
How did Machine Learning work?
Relationship Between Artificial Intelligence, Machine Learning, and Data Science
Different Categories of Machine Learning: Supervised, Unsupervised and Reinforcement
Applications of Machine Learning

2. Supervised Machine Learning

Linear Regression
Simple Linear Regression
Multiple Linear Regression
Performance Metrics: Accuracy Scores, R^2 And Adjusted RA2
To Compare the Model with Different Numbers of Independent Variables.
Approaches to Feature Selection: Univariate Selection, Feature Importance, RFE
Parameter Tuning and Model Evaluation
Data Transformation and Normalization
Ridge & Lasso Regression (L1 & L2)

3. Logistic Regression

Concept of Logistic Regression
Univariate Logistic Regression
Multivariate Logistic Regression: Model Building and Evaluation
Dealing with Categorical Independent Variable – Hot Encoding Vs Dummy Variable

4. Decision Trees

Concept of Decision Trees
Importance and Usage of Ginny And Entropy
Information Gain
Visualizing Decision Trees Nodes and Splits
Working of The Decision Tree Algorithm
Extending Decision Trees to Regressing Problem
Evaluating Decision Trees Models: Accuracy, Precision, Recall, Confusion Matrix

5. Classification – k Nearest Neighbor (KNN)

Classification and Regression
Application, Advantages and Disadvantages
Distance Metric – Euclidean, Manhattan, Chebyshev, Minkow
Measuring accuracy using Cross-Validation, Stratified k-fold, Confusion Matrix,Precision, Recall, Fl-score.

6. Naive Bayes Classifier

The Bayes Theorem
The Gaussian Naïve’s Bayes Classifier – Assumptions of The Naïve Bayes Classifier, Functioning of The Naïve’s Bayes Algorithm

7. Classification SVM(Support Vector Machine)

Classification and Regression
Separating line, Margin and Support Vectors
Linear SVC Classification
Polynomial Kernel – Kernel Trick
Gaussian Radial Basis Function (RBF)
Grid Search to tune hyper-parameters
Support Vector Regression

8. Unsupervised Learning – Clustering

Introduction to Clustering
K-Means Clustering: Linkage
Use of Elbow Curve & Silhouette Score
Hierarchical Clustering – Agglomerative & Divisive
Distance Matrix, Dendrogram

9. Density based clustering

DBSCAN (Density based clustering) Technique
Density based clustering
Eps: Density
Core, Border and Noise points
Density edge and Density connected points
DBSCAN Algorithm
Hyper Parameters: MinPts and EpsA
Advantages and Limitations of DBSCAN

10. Principal Component Analysis – PCА

Noise in The Data and Dimensionality Reduction
Capturing Variance – The Concept of Principal
Components
Assumptions in Using PCA
Eigenvectors and Orthogonality of Principal Components
What Is the Complexity Curve?
Advantages of Using PCA
The Working of The PCA Algorithm
Singular Value Decomposition

11. Ensemble Modelling

Popular Ensembles
Bagging
Bootstrap
Introduction to Random Forests
Feature Importance in Random Forests
Boosting
How Boosting Algorithm Works?
Adaboost
Gradient Boost
XGBoost

DEEP LEARNING

1. Introduction to Deep Learning

What is Deep Learning?
Why Deep Learning?
Relationship Between Al, Machine Learning, and Deep Learning
History and Evolution of Deep Learning
Challenges Motivating Deep Learning
Key Applications of Deep Learning

2. Artificial Neural Networks (ANN

Importance of ANN in Al & Machine Learning
Biological Inspiration: How ANN Mimics the Human Br
Input Layer: Receiving raw data
Hidden Layers: Processing and feature extraction
Output Layer: Generating predictions
Activation Functions: ReLU, Sigmoid, Tanh, Softmax
Training and Optimization in ANN
Forward and Backpropagation
Loss Functions in ANN (MSE, Cross-Entropy)
Gradient Descent & Optimization Algorithms (SGD, Adam,RMSprop
Overfitting & Regularization (Dropout, Batch Normalization)
Applications of ANN
Image & Speech Recognition
Natural Language Processing (NLP)
Financial Market Forecasting
Healthcare & Disease Prediction
Implementing ANN in Python
Using TensorFlow & Keras
Building & Training an ANN Model
Evaluating Model Performance

3. Convolutional Neural Networks (CNN)

What is a CNN?
Why CNNs for Image Processing?
How CNNs Differ from Traditional Neural Networks
Architecture of CNN
Convolutional Layer: Feature extraction
Pooling Layer: Dimensionality reduction (Max Pooling, Average Pooling)
Fully Connected Layer: Classification and output
Activation Functions: ReLU, Softmax
Types of CNN Architectures
LeNet-5 – Early CNN model
AlexNet – Breakthrough in deep learning
VGGNet – Deeper architectures
ResNet – Solving vanishing gradient problem
InceptionNet – Multi-scale feature learning
Training a CNN
Data Augmentation for Better Performance
Loss Functions for CNN (Categorical Cross-Entropy, MSE)
Optimization Techniques (Adam, SGD)
Applications of CNN
Image Classification: Face Recognition, Object Detection
Medical Image Analysis: Disease Detection (X-rays, MRIs)
Autonomous Vehicles: Lane detection, Pedestrian recognition
Augmented Reality & Deepfake Technology
Implementing CNN in Python
Using TensorFlow & Keras
Building a CNN Model for Image Classification

4. Recurrent Neural Networks (RNN)

What is an RNN?
Why RNNs for Sequential Data?
Difference Between RNN and Traditional Neural Networks
Architecture of RNN
Input, Hidden, and Output Layers
Recurrent Connections: Handling time-series & sequential data
Backpropagation Through Time (BPTT)
Types of RNN Architectures
Basic RNN – Simple recurrent structure
Long Short-Term Memory (LSTM) – Solving the vanishing gradient problem
Gated Recurrent Unit (GRU) – Efficient alternative to LSTM
Bidirectional RNN (Bi-RNN) – Learning from past and future context
Applications of RNN

5. Natural Language Processing (NLP) with Deep Learning

Introduction to NLP
What is Natural Language Processing?
Importance of NLP in Al
Key NLP Concepts
Tokenization, Stemming, and Lemmatization
Stopwords & Named Entity Recognition (NER)
POS Tagging & Chunking
Bag of Words (BoW) & TF-IDF
Deep Learning for NLP
Word Embeddings (Word2Vec, GloVe, FastText)
Recurrent Neural Networks (RNNs) for NLP
Transformers & BERT for NLP
Attention Mechanisms in NLP
Applications of NLP
Chatbots & Virtual Assistants
Text Classification & Sentiment Analysis
Speech-to-Text & Text-to-Speech Conversion
Implementing NLP Models
Using NLTK, Spacy, TensorFlow, and Transformers
Sentiment Analysis & Text Generation

6. Deep Learning with Autoencoders

What are Autoencoders?
Importance in Deep Learning
Applications of Autoencoders
Architecture of Autoencoders
Encoder: Extracting meaningful features
Latent Space: Compact representation
Decoder: Reconstructing the original data
Types of Autoencoders
Vanilla Autoencoder: Basic architecture
Denoising Autoencoder: Removing noise from data
Sparse Autoencoder: Learning useful representations
Variational Autoencoder (VAE): Generative modeling
Convolutional Autoencoder (CAE): Image processing applications
Applications of Autoencoders
Data Compression & Feature Extraction
Anomaly Detection
Image Denoising
Generative Modeling
Implementing Autoencoders in Python
Using TensorFlow & Keras

7. Computer Vision with Deep Learning

What is Computer Vision?
The Evolution of Computer Vision
Importance and Real-World Applications
Key Concepts in Computer Vision
Image Processing vs. Computer Vision
Understanding Pixels and Image Representation
Feature Extraction and Object Recognition
Techniques in Computer Vision
Image Processing: Filters, Edge Detection, Histogram Equalization
Feature Detection & Matching: SIFT, SURF, ORB
Object Detection: YOLO, SSD, Faster R-CNN
Image Segmentation: Semantic & Instance Segmentation
Role of Convolutional Neural Networks (CNNs)
Transfer Learning for Computer Vision
Generative Adversarial Networks (GANs) for Image Generation
Autoencoders for Image Compression
Applications of Computer Vision
Healthcare: Medical Image Analysis (X-rays, MRIs)
Autonomous Vehicles: Object Detection & Lane Detection
Security: Facial Recognition & Surveillance
Implementing Computer Vision in Python ES
OpenCV for Image Processing Con
Real-time Object Detection with YOLO

ARTIFICIAL INTELLIGENCE

CURSOR AI + TRAE AI

Module 1: Introduction to Modern Al Tools

What is Al-first Development?
Evolution of Al Coding Assistants
Overview of CURSOR AI & TRAE AI
How developers use these tools in real-world projects

Module 2: Getting Started with CURSOR AI

Installation & Setup

Installing Cursor Editor
Integrating GitHub/GitLab
Setting environment, extensions & APIs

Understanding Key CURSOR Features

Smart Al Pair Programming
Code Autocomplete & Refactoring
Al Chat for Code Explanations
Multi-file Project Understanding
Cursor Composer Mode
Auto debugging assistance
Using Cursor for test-case generation

Hands-on with CURSOR AI

Building your first Al-assisted program
Real-time code optimization
Converting requirements into working code
Fixing bugs with Al suggestions
Al-generated documentation

Module 3: Introduction to TRAE AI

What is TRAE (Track-Reason-Execute) AI?

TRAE architecture overview
How TRAE agents understand and break tasks
Difference between standard LLM vs TRAE-based systems

TRAE AI Capabilities

Task tracking & workflow automation
Multi-step reasoning
Agent collaboration
Automated execution with human oversight

TRAE Al Tools & Platforms

TRAE SDK basics
Agent Studio Platforms
Integration with APIs & external tools

Module 4: Building Al Agents Using TRAE

Agent Fundamentals

Agent roles & responsibilities
Reasoning graph
Memory handling
Decision-making flow

Hands-On Agent Development

Creating your first TRAE-based agent
Connecting agent with external APIs
Automating data extraction & reporting
Building a task automation pipeline

Real-World Applications

Chatbots & virtual assistants
Automated customer service flows
Sales & lead management automation
Content creation & research automation

Module 5: CURSOR + TRAE Integration

How CURSOR helps build TRAE agents
Auto-code workflows for Al automation
Full-stack agent development
Automating testing & deployment using Al

Module 6: Advanced Al Development Workflow

Multi-agent systems
Prompt engineering for agents
Workflow design patterns
Long-term memory integration
Secure Al development principles

Module 7: Capstone Project

AI-powered chatbot
Automated data analytics agent
Code generation assistant
Workflow automation system

Module 8: Career Path & Industry Demand

Jobs in Al Agent Development
Using CURSOR & TRAE skills in MNC interviews
Portfolio building
Resume + GitHub project guidance

Enquiry Now

Our Placed Students

Khushi Shaho

Web Developer

Placed at

Jay Prakash

Cyber Security Consultant

Placed at

Shristi Kumari

Software Developer

Placed at

Mukul Chauhan

Java Developer

Placed at

Skill & Tools That You will Learn

Excel

Power BI

Numpy

Pandas

Matplotlib

Seaborn

Jupyter

Julius

Colab

Claude

ChatGPT

Tableau

Copilot

Scikit learn

Postgre SQL

My SQL

Pytorch

GitHub

Plotly

Tensor Flow

LangChain

LightGBM

Antigravity

Cursor AI

You Learn 30+ Data Science Skills

Data Collection
Data Cleaning
Data Wrangling
Data Preprocessing
Exploratory Data Analysis (EDA)
Statistical Analysis
Probability Analysis
Feature Engineering
Machine Learning
Supervised Learning
Unsupervised Learning
Deep Learning
Natural Language Processing (NLP)
Computer Vision
Predictive Modeling
Model Evaluation
Model Optimization
Time Series Analysis
Data Visualization
Data Interpretation
Data Mining
Pattern Recognition
Anomaly Detection
Predictive Analytics
Data Storytelling
Model Deployment
Problem Solving
Critical Thinking
Business Acumen
Data-Driven Decision Making

Why Choose us?

Hybrid Mode Class

Learn flexibly with combined online and offline classroom training sessions.

Internship Certificate

Receive an internship certificate for hands-on industry project experience.

100% Placement Assistance

Complete placement support provided until you secure a suitable job.

CV Building & ATS Score

Optimize your resume for ATS and improve shortlisting chances significantly.

Mock Interviews

Practice with mock interviews to boost real interview performance confidence.

15 Students Per Batch

Small batches ensure personalized attention and better student-teacher interaction.

Get Certified to boost your Professional Growth

Recognition Across the Globe

Globally accepted certification to showcase expertise and unlock career opportunities.

Guaranteed Validity for Life

Lifetime-valid certificate ensures lasting credibility and career-long professional recognition.

Special Opportunities for Networking

Join expert community, collaborate, and grow through valuable networking opportunities.

What is Data Science?

Data science is the exciting field of extracting meaningful insights from raw data. It combines mathematics, statistics, programming, and artificial intelligence to solve real-world business problems and predict future trends.

Who can join this data science course in noida?

Anyone with a passion to learn! This includes college students (B.Tech, BCA, BBA, B.Sc), IT professionals looking to upskill, and non-IT professionals wanting to transition into the tech industry. It is a highly beginner-friendly program.

Salary after Data Science training

Freshers who complete our data science course in Noida can expect starting salaries between ₹4 LPA to ₹8 LPA. Experienced professionals can easily command salaries ranging from ₹12 LPA to ₹25+ LPA, depending on their prior experience and skill level.

Why Learn Data Science

You might be wondering, “Why should I invest my time in a data science course in Noida right now?”Here is exactly why you need to jump into this field immediately:

1. The Massive AI Boom

We are living in the golden age of Artificial Intelligence. Tools like ChatGPT, Gemini, and Midjourney have shown the world the power of data. However, AI cannot exist without data. Data scientists are the architects behind these AI systems. By taking our data science training in Noida, you position yourself at the very center of the global AI revolution.

2. Unprecedented Demand for Data Scientists

Every industry from healthcare and finance to e-commerce and digital marketing is generating mountains of data. Companies desperately need experts who can clean, analyze, and interpret this data. Because of this massive supply-demand gap, graduates from a reputable data science institute in Noida are hired almost instantly.

3. High Salary Stats

Let’s talk numbers. Data science remains one of the highest-paying jobs globally and in India. The average salary for a mid-level data scientist is soaring. By enrolling in the best data science training institute in Noida, you are essentially securing a high ROI (Return on Investment) for your education. It is not uncommon for our alumni to double or triple their previous salaries.

4. Explosive Career Growth

A job in data is not a dead-end street. Once you complete a data science course in Noida with placement, you start as an analyst or junior scientist. Within 2 to 3 years, you can quickly move up to Senior Data Scientist, Lead AI Engineer, or Chief Data Officer. The ladder goes all the way to the executive suite.

5. Cross-Industry Demand

Unlike some niche IT skills, data science is universal. Do you love sports? Sports teams hire data analysts. Love fashion? Retail brands need predictive modeling. By completing your data science training in Noida, you get the freedom to choose the industry you are most passionate about.

Career Opportunities After Data Science in Noida

Completing our data science course in Noida opens up a massive world of job profiles. You don’t just have to be a “data scientist” Here are the top career outcomes and realistic salary ranges in India you can expect:

Data Scientist

What they do: Use advanced statistics and machine learning to predict the future and solve complex business puzzles.

Salary Range: ₹8,000,000 – ₹20,000,000+ per year.
How we help: Our data science institute in Noida covers advanced ML algorithms to make you perfect for this role.

Data Analyst

What they do: Clean data, create visual dashboards, and help companies make day-to-day decisions.

Salary Range: ₹4,000,000 – ₹10,000,000 per year.

How we help: You will master data analytics using Power BI certification and Tableau during the course.

Machine Learning (ML) Engineer

What they do: Build, test, and deploy AI models into production environments.

Salary Range: ₹10,000,000 – ₹25,000,000+ per year.

How we help: Our machine learning training modules in Noida are heavily integrated into the core curriculum.

Business Intelligence (BI) Analyst

What they do: Focus on business strategy, creating reports that executives use to steer the company.

Salary Range: ₹6,000,000 – ₹14,000,000 per year.

How we help: Our data science course in Noida heavily emphasizes business problem-solving frameworks.

AI Engineer

What they do: Work with deep learning, neural networks, and NLP (natural language processing) to create intelligent systems.

Salary Range: ₹12,000,000 – ₹30,000,000+ per year.

How we help: We introduce you to deep learning architectures, building a b foundation for an AI course specialization.

Data Engineer

What they do: Build the infrastructure and pipelines that gather and store data safely.

Salary Range: ₹9,000,000 – ₹22,000,000 per year.

How we help: We teach you SQL and cloud basics to understand how data flows.

Note: These salaries vary based on your background, but graduating from the best data science training institute in Noida gives you a massive advantage in negotiations.

Why Choose Appwars Technologies for Data Science Course?

You have many options, so why should you pick Appwars Technologies? Simply put, we care about your success. We are not just a data science institute in Noida; we are your career partners. We provide a completely hands-on learning environment. You will write code, build models, and deploy apps from day one.Here is a quick comparison table showing why we are considered the best data science training institute in Noida:

Feature	Appwars Technologies	Average Training Institutes
Practical Approach	80% Practical, 20% Theory	Mostly theoretical concepts
Beginner Friendly	Yes, absolutely! Basics to Advanced	Expects prior coding knowledge
Curriculum Updates	Updated for AI demands	Outdated syllabus
Placement Guarantee	Yes, dedicated placement cell	Only “assistance” or no support
Python Focus	Extensive Python for Data Science	Basic Python overview
Live Projects	6+ Industry-level real projects	1 or 2 basic textbook projects

When looking for a data science course in Noida with placement, this table clearly shows why students and working professionals trust Appwars Technologies.

Tools Covered in Our Data Science Training in Noida

To become a top-tier data professional, you need to master the right tools. At Appwars Technologies, our data science course in Noida ensures you are highly proficient in the modern tech stack.

Here is what you will master:

Python: The undisputed king of data. We dedicate extensive hours to Python for Data Science, ensuring you can write clean, efficient code. You can also explore our standalone Python course in Noida if you want to focus purely on programming first.
SQL: The language of databases. You will learn to extract and manipulate data from complex relational databases.
Excel: The classic tool that is still heavily used for quick analytics and data cleaning.
Power BI & Tableau: The best tools for data visualization. You will learn to create stunning dashboards. (Check out our Data Analytics Course in Noida and Tableau course in Noida for deeper insights).
Scikit-Learn, Pandas, NumPy: The holy trinity of Python for Data Science libraries.
TensorFlow & Keras: For deep learning and advanced neural networks.

By mastering these tools at the best data science training institute in Noida, your resume will be irresistible to tech recruiters.

Live Projects Included in Data Science Training

Theory is good, but practice is what gets you hired. Companies don’t want to know what you read; they want to know what you have built. This is why our data science course in Noida with placement includes intense, real-world projects.

These projects will go straight into your GitHub portfolio, proving your skills to future employers:

1. Sales Forecasting

The Problem: A major retail store wants to know how many products they will sell next month to manage their inventory.
What you will do: You will use Python for data science and time series analysis to predict future sales based on historical data, holidays, and seasonal trends. This is a highly demanded skill in the e-commerce sector.

2. Fake News Detection

The Problem: Social media platforms are flooded with misinformation.
What you will do: You will build a Natural Language Processing (NLP) model that scans news articles and classifies them as “Real” or “Fake”. You will use TF-IDF vectorizers and machine learning classifiers. This project showcases your ability to handle unstructured text data.

3. Customer Segmentation

The Problem: A marketing team wants to run targeted ad campaigns but doesn’t know how to group their diverse customers.
What you will do: Using Unsupervised Machine Learning (like K-Means Clustering), you will group customers based on their purchasing behavior, age, and income. This project is a staple in our data science training in Noida because it solves a core business marketing problem.

4. Recommendation System

The Problem: How does Netflix know what movie you want to watch next? How does Amazon suggest products?
What you will do: You will build your very own recommendation engine using collaborative filtering and content-based filtering. It is a fantastic project that recruiters absolutely love to see on a resume.

5. Stock Market Prediction

The Problem: Investors want to predict the stock price of a company for the next week.
What you will do: You will pull live financial data and use advanced regression algorithms and LSTM neural networks to predict stock price movements. This project proves your capability to handle volatile, real-world numerical data.

6. HR Analytics Dashboard

The Problem: A company is losing employees and wants to understand why people are resigning.
What you will do: You will step into the shoes of a Data Analyst. You will clean HR data and build a beautiful, interactive dashboard (using Power BI or Tableau) to visualize attrition rates, salary discrepancies, and employee satisfaction scores.

Completing these six projects at our data science institute in Noida guarantees that you have the practical experience required to pass any technical interview.

100% Placement Support: Your Dream Job Awaits

We understand that the ultimate goal of taking a data science course in Noida is to secure a high-paying job. That is why we are famous for our data science course in Noida with placement track record.

At Appwars Technologies, our dedicated placement cell works tirelessly to bridge the gap between our students and top MNCs. Here is how our data science training in Noida gets you hired:

Resume Building: We don’t just leave you with a generic resume. We help you craft an ATS-friendly (Applicant Tracking System) resume highlighting your Python for Data Science skills and the 6 mega projects you built.

Mock Interviews: You will face technical and HR mock interviews conducted by industry experts. They will grill you on statistics, machine learning, and coding, ensuring you never freeze up in a real interview.

LinkedIn Profile Optimization: In 2026, recruiters live on LinkedIn. We teach you how to optimize your profile so HRs reach out to you.

Guaranteed Interview Calls: Because we are the best data science training institute in Noida, we have tie-ups with hundreds of tech companies, startups, and MNCs in the Delhi-NCR region. We arrange direct interview opportunities for our certified students.

When you enroll in our data science institute in Noida, you are not just buying a course; you are investing in a career transformation.

Our Process

Data Science with Gen AI Course Fees

New Batch Starting

6 Months Course Fees

₹55,000/-

₹48,000/-

Flat ₹7,000 OFF

15 Students Per Batch
Live offline & online Classes from Industry Expert
Hands-on curriculum with Real-Life Projects

Enroll Now

New Batch Starting

8 Months Course Fees

₹75,000/-

₹65,000/-

Flat ₹10,000 OFF

15 Students Per Batch
Live offline & online Classes from Industry Expert
Hands-on curriculum with Real-Life Projects

Enroll Now

Got more questions?

Talk to our team directly

Student Testimonials

      EXCELLENT  
   Based on 1224 reviews 
    
 
 
 
   
 
 
       Posted on Google  
   
   Titikhya 
 
 
 
 Trustindex verifies that the original source of the review is Google. Genuine
  
 
      Posted on Google  
   
   Priyanshu Kumar 
 
 
 
 Trustindex verifies that the original source of the review is Google. it was a very good session
  
 
      Posted on Google  
   
   Shubhanshu Tiwari 
 
 
 
 Trustindex verifies that the original source of the review is Google. the session was very good and interacting
  
 
      Posted on Google  
   
   Sarthak Gupta 
 
 
 
 Trustindex verifies that the original source of the review is Google. great experience
  
 
      Posted on Google  
   
   Mayank Kansal 
 
 
 
 Trustindex verifies that the original source of the review is Google. great workshop
  
 
      Posted on Google  
   
   Apurva Sinha 
 
 
 
 Trustindex verifies that the original source of the review is Google. Good
  
 
  
  
 
     Verified by Trustindex  Trustindex verified badge is the Universal Symbol of Trust. Only the greatest companies can get the verified badge who has a review score above 4.5, based on customer reviews over the past 12 months. Read more

Frequently Asked Questions

Which is the best Data Science course in Noida?

Appwars Technologies is widely recognized as providing the best data science course in Noida. We earn this reputation through our beginner-friendly approach, highly experienced trainers, extensive coverage of Python for Data Science, six real-time industry projects, and a dedicated data science course in Noida with placement guarantee.

What is the duration of the Data Science course?

Our complete data science training in Noida typically takes between 4 to 6 months to complete. This duration ensures we cover everything from basic programming to advanced machine learning and deep learning, giving you ample time to build your projects and prepare for interviews. Fast-track weekend batches are also available for working professionals.

What is the Data Scientist salary in India?

In India, the salary for Data Scientists is highly lucrative. Freshers can start anywhere between ₹4,00,000 to ₹8,00,000 per annum. With 2 to 4 years of experience, salaries usually jump to ₹12,00,000 to ₹18,00,000. Experts and Leads can easily earn upwards of ₹25,00,000. Completing your training at a premier data science institute in Noida gives you the negotiation power to secure the higher end of these brackets

Can beginners learn Data Science?

Yes, absolutely! Our data science course in Noida is crafted with a "zero-to-hero" approach. We include a mandatory beginner-friendly statement: You do not need to be a math genius or a coding wizard to start. We teach the foundational mathematics, statistics, and Python for Data Science completely from scratch.

Is Python mandatory for Data Science?

While there are other languages like R, Python is universally considered mandatory for modern data science. It is easy to read, has massive community support, and features powerful libraries (like Pandas and TensorFlow). That is why our data science training in Noida heavily focuses on Python for Data Science, ensuring you are aligned with current industry standards.

Does Appwars provide placement support?

Yes, 100%. We proudly offer a data science course in Noida with placement assistance. This includes resume optimization, portfolio building, multiple mock interviews, and guaranteed interview scheduling with our network of hiring partners across India and the Delhi-NCR region.

What projects are included?

As the best data science training institute in Noida, we believe in practical learning. You will complete a minimum of 6 industry projects, including Sales Forecasting, Fake News Detection, Customer Segmentation, a Movie Recommendation System, Stock Market Prediction, and an HR Analytics Dashboard.

Which companies hire Data Science students?

The demand is everywhere! Our alumni have been placed in top-tier IT service companies (TCS, Infosys, Wipro, HCL), big four consulting firms (EY, Deloitte, PwC, KPMG), product-based startups, e-commerce giants (Amazon, Flipkart), and massive financial institutions. Taking a data science course in Noida opens doors to virtually every sector.

Related Courses

Data Analytics

Machine Learning

Power BI Training

Python

Advance Excel

Best Data Science Course in Noida with AI

Certification Partners

Our Students Placed In These Companies

Data Science with AI Course Syllabus

ADVANCE PYTHON PROGRAMMING

1. Introduction to Python

2. Input and Output Functions

3. Variable and Data Types

4. Operators in Python

5. Conditional Statements in Python

6. Loops in Python

7. User defined functions in python

8. Strings in python

9. List

10. Tuple

11. Sets

12. Dictionaries

13. Python Modules

14. File Handling in Python

15. Exception Handling

16. Regular Expression

ITERATORS- GENERATORS

Object Oriented Programming

STRUCTURE QUERY LANGUAGE (SQL)

1. MySQL

2. Jupyter Overview

PYTHON AND DATA SCIENCE LIBRARY

1. NumPy

2. Pandas

3. Matplotlib

4. Seaborn

STATISTICAL METHODS FOR DECISION MAKING

MACHINE LEARNING

1. Introduction To Machine Learning

2. Supervised Machine Learning

3. Logistic Regression

4. Decision Trees

5. Classification – k Nearest Neighbor (KNN)

6. Naive Bayes Classifier

7. Classification SVM(Support Vector Machine)

8. Unsupervised Learning – Clustering

9. Density based clustering

10. Principal Component Analysis – PCА

11. Ensemble Modelling

DEEP LEARNING

1. Introduction to Deep Learning

2. Artificial Neural Networks (ANN

3. Convolutional Neural Networks (CNN)

4. Recurrent Neural Networks (RNN)

5. Natural Language Processing (NLP) with Deep Learning

6. Deep Learning with Autoencoders

7. Computer Vision with Deep Learning

ARTIFICIAL INTELLIGENCE

PROPOSED PROJECTS

1. Python

2. Machine Learning

3. Deep Learning

4. Data Science

5. Artificial Intelligence

ADVANCE PYTHON PROGRAMMING

1. Introduction to Python

2. Input and Output Functions

3. Variable and Data Types

4. Operators in Python

5. Conditional Statements in Python

6. Loops in Python

7. User defined functions in python

8. Strings in python

9. List

10. Tuple

11. Sets

12. Dictionaries

13. Python Modules

14. File Handling in Python

15. Exception Handling

16. Regular Expression

ITERATORS- GENERATORS

Object Oriented Programming

STRUCTURE QUERY LANGUAGE (SQL)

1. MySQL