Data Science – Global Quest Technologies

Agentic AI with Data Science

Transform Your Passion for Data Into a Powerful Career

Understand data analysis, machine learning, and statistics. Gain hands-on experience with real projects and expert guidance to start your career in data science!

6 Months

Offline/Online

(593+)

Batches Starts On 1st,10th & 20th of every month

Document

Overview
Instructors
Syllabus
Course Features
Course Fee
FAQ's

Talk to Admissions
+ 91 9448403469

Course Overview

6-Months Course Duration

We go over all the prerequisites needed to acquire a fantastic job, from the ground up.

100% Placement Assistance

Providing complete assistance with the preparation to crack the interviews

Placement Opportunities

We are assisting in finding better and more relevant job openings.

Live Training Interactive Sessions

Through guidance ensures the students gain the best out of the course.

Why Choose This Course at Our Institute?

Our institute provides easy-to-follow Agentic AI with Data Science training, covering machine learning, data analysis, and statistics. With practical projects and expert guidance, we help you gain real skills to kickstart your career in data science. Join us for hands-on learning and support every step of the way!

Instructors

Mr.Syed Mahmood

Data Science Trainer

In 2005, Syed Mahmood obtained a degree in electronics engineering.He is almost ten years experienced in Excel, Robotics, and Gen AI. He works as a data science trainer.

Mr. Subramanya Swamy H.N

Java Full Stack Trainer

Mr. Subramanya Swamy H N has been implementing his passion towards teaching from the past 10+ years. He teaches any complex topic in a very simpler manner by giving real time examples such that even naive can easily understands the subject in a crystal clear manner. He is handling Java Full Stack Development, Python Full Stack Development, Devops with AWS, Dotnet Full Stack Development.

Syllabus

Python Introduction

Core Python

Level – 1 Core Python
- Python Introduction
- Python Setup Environment & installation
- Python various versions
- Python – Different programming languages
- Language Fundamentals
- Operators
- Input and Output Statements
- Flow Controls
- String Concept
- List Data structure
- Tuple Data Structure
- Set Data Structure
- Dictionary Data Structure
- Functions, Packages & Modules
- OOPS
- Exception Handling
- File Handling

Level – 2 Advance Python
- Python database Connectivity(PDBC)
- Regular Expressions
- Multithreading
- Decorator
- Generator
- Logging Modules

Numpy, Pandas and Visualization

Numerical Python (Numpy)

• Indexing/ Slicing
• Broadcasting
• Appending/ Inserting on Axis
• Mathematical and Statistical operations
• Sort/ Conditions
• Transpose operations
• Joining/ Splitting
• Linear Algebra

Data Manipulation with Pandas

• Data Extraction
• Series/ DataFrame Creations
• Indexing and Slicing
• Conditions/ Grouping/ Imputation
• Append/ Concat/ Merge/ Join
• DateTime Functionalities and Resampling
• Window Functions
• Excel functions

Data Visualization

• Customization of Matplotlib/ Seaborn
• Scatterplots/ Barplots/ Histograms/ Density Plots
• 3D plots
• Boxplotting and Outlier Detection
• Visualizing Linear Relationships
• Plotting with Pandas

Probability and Statistics

Introduction to Statistics

• Probability
• Basics( Mutually Exclusive/ Joint Probability)
• Conditional Probability
• Dependent/ Independent
• Logs/ Odds
• Bayes Theorem
• Descriptive/ Inferential
• Mean, Median, Mode
• Variance/ Standard Deviation
• Co-variance/ Correlation (Pearson/ Spearman)

Types of Distributions

• PDF/ PMF/ CDF
• Uniform/ Normal/ Skewed Distributions
• Binomial/ Bernoulli Distribution
• Poisson/ Exponential Distributions

Hypothesis Testing

• Central Limit Theorem
• Null/ Alternative Hypothesis
• Z-test/ T-test/ Chi2-test
• p-value
• F-test/ Anova
• Scipy.Stats/ Statsmodels

Machine Learning

Introduction to Machine Learning

• Difference Between AI, ML and DL
• Applications of Machine Learning
• Categorization of Machine Learning
• Supervised / Unsupervised / Semi Supervised
• Parametric vs Non Parametric
• Geometric/ Rule Based/ Gaussian
• Flow Operation (Pipelining)
• Sklearn Usage

EDA and Data Wrangling

• Null Values Imputation
• Outlier Detection
• Encoding
• Label Encoder
• Ordinal Encoding
• One Hot Encoding
• Scaling
• Binarizer
• MinMaxScaling
• Normalizer (L1 and L2)
• StandardScaler
• Imbalance Dataset
• Univariate/ Bivariate/ Multivariate Analysis

Feature Selection and Dimensional Reduction

• Filter Methods
• Wrapper Methods
• Embedding

Linear Regression

• Assumptions
• Introduction to Linear Regression
• Understanding the real meaning of Linear Regression
• Multiple Linear Regression
• Cost Function (Sum of Square Error)

Gradient Descent based approach

• Loss Function
• Derivative of Loss
• Gradient Descent for Multiple Features

Polynomial Regression

• Introduction to Polynomial Regression
• When to use Polynomial regression
• Evaluation based on RMSE/ R2

Regularization with Lasso/Ridge Regression

• Problems with Large Features
• Why penalty is inducted
• Difference between L1 and L2
• Cost Function

K Nearest Neighbours

• Introduction to KNN algorithm
• KNN Classifier vs Regressor
• How to select the best K

Logistic Regression

• Logistic regression vs Linear Regression
• Log Odds / Logit / Sigmoid Function
• Optimization and Log Loss
• Maximum Likelihood Estimation

Support Vector Machine (SVM)

• Margin of SVM’s
• SVM optimization
• SVM for Data which is not linear separable
• Kernel Trick
• SVM Parameter Tuning
• Hinge Loss

Decision Tree

• Introduction to Decision tree
• Decision Tree Classification / Regression
• Types of Decision Tree techniques (ID3 / CART)
• Pruning

Naive Bayes

• Conditional Probability and Bayes Theorem
• Naive Bayes
• Burnoulli / Multinomial and Gaussian Implementation

Cross-Validation Techniques

• Holdout Validation
• K-fold cross Validation
• Stratified Kfold
• Cross_val_score
• GridSearchCV
• RandomizedSearchCV

Evaluation Metrics

• MSE/ MAE
• R2/Adjusted R2
• Accuracy measurement
• Confusion Matrix
• Precision/ Sensitivity/ Specificity/ F1 Score
• AUC/ ROC
• AIC and BIC

Ensembling

• Voting/ Averaging
• Bagging / Boosting / Stacking
• Random Forest
• AdaBoost
• Gradient Boosting
• XGBoost

Unsupervised Learning

Clustering

K Means
• Applications of Clustering
• Understanding Euclidean Distance
• Basics of Clustering
• Elbow Method and Silhouette score
Hierarchical Clustering
• Agglomerative
• Divisive
DBSCAN
• Reachability
• Connectivity
• epsilon and r
GMM
• Gaussian surface
• Relation with EM
• Difference between GM and other Clusters

Dimensionality Reduction

• What is PCA?
• Understanding Matrix Transformations
• Eigen Values and Eigen Vectors
• tSNE and Umap

Time Series

• Lag Values
• AutoRegression/ AutoCorrelation
• Stationarity
• Dicky Fuller Test
• Time Series Decomposition
• Modelling and Forecasting
• ARIMA/ SARIMA

Recommender System

• Support, Confidence, Lift
• Jacard Matrix
• Cosine Similarity

ML based Projects

Project Regression
Project Classification
Project Unsupervised
Project Time Series
Project Recommender System

Deep Learning

Artificial Neural Networks In Python

• Perceptron and relate it with Logistic Regression
• Multiple layer Neural network
• Similiarities and Differences with Baisc ML
• Forward Propogation
• Back Propagation Algorithm
• Vanishing Gradient and Exploding Gradient

Activation Functions

• Non Linearity
• Sigmoid / Tanh Function
• Relu /Leaky Relu /Gelu
• Softmax Function

Optimizers

• Gradient Descent
• Stochastic Gradient Descent
• Momentum
• AdaGrad
• RMSProp
• NAG
• Adam/ Nadam

Tensorflow / Keras

• Tensors
• Session, Placeholders and Variables
• Hands on with Tensorflow
• Sequential vs Functional
• Model Creation

Pytorch

• Difference between Tensorflow and Pytorch
• Autograd
• Graphs
• Data Loaders

Types of Deep Learning Algorithms / Algos

• Feed Forward Networks
• Fully Connected Networks
• Recurrent Neural Networks
• Convolutional Networks

Computer Vision and use of OpenCV

• Convolution/ Filters/ Pooling
• Back Propogation in CNN
• Image Recognition vs Object Localization
• Types Of CNN
• FastRCNN, YOLO

CNN Architectures

• LeNet/ Alexnet
• VGG 16/ 19
• ResNet
• MobileNet

Transfer Learning

• ImageNet
• Need of Transfer Learning
• Freezing of Layers
• Reusing of Structure

AutoEncoders

• Encoder vs Decoder
• Difference with PCA
• KL Divergence
• Variable Auto Encoders

GANs

• Generators
• Discriminator
• Structure

NLP Natural Learning Process with NLTK/ Spacy

• Word Embedding
• Frequency vs Prediction based embedding
• Count Vector/ TFIDF/ Co Occurrence
• Bag of Words / Skip Gram
• Word2Vec /GloVe

Recurrent Neural Networks

• Classical RNN
• LSTM/GRU
• Vanishing Gradient
• Exploding Gradient
• Bidirectional RNN

Different types of Transformers

• Encoder and Decoder
• Encoder only Transformers
• Decoder only Transformers

Generative AI and Prompt Engineering

• Prompt Engineering
• Chain of Thought (CoT)

LangChain

• Components of Langchain
• Open AI and Hugging Face
• How to query database

Fine Tuning

• Multi Instruction Fine Tuning
• Parameter Efficient (PEFT)

Low Rank Matrices

• LoRA
• Quantization
• QLoRA

Retrieval Augmentation Systems

• Difference between Retrieval and Augmentation
• Limitations of RAG
• ReAct

Miscellaneous Topics of GenAI

• Knowledge Graphs
• Vector Database
• PAL model

Reinforcement Learning

• MDP
• Value Functions
• State Values vs Action Value (Q)
• Exploitation vs Exploration
• TD Learning (Q Learning vs Sarsa)

Environments

• Hands on RL
• Tensorflow Agents Library
• OpenAI Gym environment

Deep Reinforcement Learning

• Deep Q Networks
• Policy Gradient Methods
• Actor-Critic Architectures

Policy Gradient Methods

• Gradient Based Techniques
• Proximal Policy Optimization (PPO)
• RLHF

Projects Deep Learning

Project Image Recognition Using Transfer Learning
Project Image Location using YONO
Project Sentiment Analysis using LSTM
Project Auto Encoders or GANs
Project Language Translator
Project ChatBot using LLMs

Generative AI Concepts

Quantitative Aptitude

Speed maths
Number System
Percentages
Ratios
Averages
Profit and Loss
Simple and Compound Interest
Ages
Mixtures
Time and Work
Time speed and Distance
Permutations and Combinations
Probability
Clocks

Reasoning

Coding and Decoding
Blood Relations
Seating Arrangements
Syllogisms
Direction Sense
Ranking test
- Data Sufficiency

Soft Skill

Interview Skills:
- Resume Writing
- Group Discussion
- Mock Interview (HR Round)
Communicative Skills:
- Comprehension Skills
- Grammar with fun
- Vocabulary Skills
- Email Skills
- Art of Presentation
- Art of Writing
- Body Language
- Cover Letter
- Listening Skills
Personality Development:
- Attitude
- Self Confidence
- Grooming
- Meeting & Reporting
- Time management
- Creativity at workplace
- Ethical values
- Leadership and Team building
- Decision making and Negotiation
- Stress management
- Corporate Expectations
- Customer Relationship Management
- Goal Setting
- SWOT Analysis
- Self–Efficacy
Etiquettes:
- Telephonic
- Dining
- Corporate

Generative AI Concepts

Generative AI and Prompt Engineering (4 hrs)
- Prompt Engineering
- Chain of Thought (CoT)
- Tree of Thought
LangChain (4 hrs)
- Components of LangChain
- OpenAI and Hugging Face
- How to query a database
Fine Tuning (4 hrs)
- Multi Instruction Fine Tuning
- Parameter Efficient (PEFT)
Low Rank Matrices (4 hrs)
- LoRA, Quantization, QLoRA
Retrieval Augmentation Systems (4 hrs)
- Difference between Retrieval and Augmentation
- Limitations of RAG
- Graph RAG
- Vector Database

Advanced Agentic AI

LangGraph (4 hrs)
- What is Graph
- AI Agents
- Nodes and Edges
- Memory

Course Features

Easy-to-Understand Learning

Learn data science basics like machine learning and data analysis.

Mini Projects

Work on real projects to practice what you learn.

Expert Support

Get help and guidance from experienced instructors.

Career-Focused

Build skills that prepare you for a job in data science.

Course Fee

Offline/Online Training + Mentorship

Offline - ₹50,000 / Online - ₹25,000

Train with the industry experts
Get access to the updated curriculum for the courses
Prepare for both technical and soft skill improvement
Get a complete start to end guidance
Placement assistance to find better opportunities

Register Here

Frequently Asked Questions

Why should I take the Data Science with Agentic AI course by Global Quest Technologies?

The Data Science course by Global Quest Technologies is especially designed to acquaint the individuals from the very root of introduction to Data Science to the expert-level practical applications of it. The students get complete guidance not only for clarifying course-related doubts but also regarding the preparation for placements.

What is the duration of the Data Science courses provided by GQT?

Data Science is a 6-month long course that would teach you the basics of Data Science, AI, ML and more. It will also prepare you with appropriate soft skills that would be beneficial for the placements and interviews.

How much do I need to pay for the Data Science course?

For the complete curriculum and the placement assistance through the Data Science course offered by GQT, it would cost you INR 50,000.

Who should enroll in the Data Science course by Global Quest Technologies?

Students looking forward to building a career as Professional Data Scientists can enroll themselves in the GQT’s Data Science course. The graduation and post graduation students looking forward to upskill their profile to land better jobs are also welcome to apply.

Does GQT provide placement assistance?

Yes, GQT provide a complete placement assistance along with the necessary skill development for the students so that they can land on better jobs. We also prepare the students with rigorous mock interviews and tests to prepare them for the corporate world.

Student Testimonials

    
 
 
         
   Likitha K A 
 
 
 
 Trustindex verifies that the original source of the review is Google. I had a really good learning experience at Global Quest Technologies. The Core Java and MySQL training was very practical and easy to follow. The trainers, especially Raghu Sir, were supportive and explained everything clearly. I feel much more confident.
  
 
        
   Nandana T 
 
 
 
 Trustindex verifies that the original source of the review is Google. I recently completed the MySQL training at Global Quest Technologies under the guidance of Raghu Sir, and it was an excellent learning experience. The course was very well-structured, starting from fundamentals and gradually moving into advanced topics.He explained every concept with great clarity and real-time examples, which made it easy to understand even the complex parts of SQL. I am very thankful to Raghu Sir and Global Quest Technologies for their excellent training and support. I would definitely recommend this institute to anyone who wants to build strong MySQL skills. And also I have currently completed core java from global quest technologies as java full stack intern under the mentoring of Raghu sir.The classes are absolutely worth and he has coverd all the important core concepts in java.I would recommend the classes to all.Such a good class.
  
 
        
   Umamaheswara Rao Uppu 
 
 
 
 Trustindex verifies that the original source of the review is Google. It is a great Experience to coming here and learning the technology currently i have completed Core Java by Raghu sir, all the concepts is clear.. thank you so much GQT
  
 
        
   ARJUN ARJUN 
 
 
 
 Trustindex verifies that the original source of the review is Google. Global quest technologies is providing the best trainer now i have completed my corejava classes
  
 
        
   Pruthvi Shivanagi 
 
 
 
 Trustindex verifies that the original source of the review is Google. I had privilege of learning MySQL under the guidance of Raghu sir, and it has been excellent experience. Raghu sir is not only an expert in SQL but also a great mentor, His classes are well paced, engaging, and perfectly suited for both beginners and advanced learners.
  
 
        
   Munganda Chandrika 
 
 
 
 Trustindex verifies that the original source of the review is Google. Trained by professional corporate trainer Raghu sir. Gained practical experience in Java and MYSQL.
  
 
        
   Jyothirmayi Runku 
 
 
 
 Trustindex verifies that the original source of the review is Google. I am so thankful to GQT (Global Quest Technologies) for providing excellence knowledge and giving practical experience through out the training . Thank you to GQT
  
 
        
   Satish Tiwari 
 
 
 
 Trustindex verifies that the original source of the review is Google. It is a great expreince to coming here and learning the technology currently i have completed SQL by Raghu sir, all the concet is cleare... thank you so much GQT
  
 
        
   Indu 
 
 
 
 Trustindex verifies that the original source of the review is Google. Raghu Sir’s SQL sessions were not just about queries and commands, but about truly understanding how databases think. His way of connecting real-life scenarios with SQL concepts made the subject come alive. What I liked most was his patience in clearing doubts and the way he built confidence in approaching complex problems step by step. It was more than a class – it was an experience that strengthened my foundation in SQL. Honestly i liked it. Thank You sir [Indra]
  
 
        
   Reena Reenu 
 
 
 
 Trustindex verifies that the original source of the review is Google. It was an good experience being in this soft skill class where i can learn more things like how to communicate, manage the things . Where i learned how to perform in group discussion, interview and built my confident. It was an amazing class and i am very glad and thankful for being in sir class. The MYSQL class which gives a  good experience on how to solve queries and learn  from it , the topic which was covered like ddl , dml , dql,  clause, function, transaction, er diagarm ,view ,stored procedure, triggers , variables .