Data Science

Acquire thorough knowledge to be a Data Scientist with the basic to an advanced level curriculum designed by the experts to cater to the current needs of the corporate world.

6 Months

Online/Offline

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+91 9448403469

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180 Days of Thorough Training Curriculum Designed by Industry Experts Live Training with the Corporate Experts Frequent Mock Interviews and Tests 2000+ Hiring Partners

60 Days of Thorough Training Curriculum Designed by Industry Experts Live Training with the Corporate Experts Frequent Mock Interviews and Tests 700+ Hiring Partners

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Overview
Instructors
Syllabus
Course Features
Course Fee
FAQ's

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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.

240+ hours of Live Training Interactive Sessions

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

Why Data Science Course?

The Data Science course by Global Quest Technologies is designed to build an individual’s foundation as an expert Data Scientist. Along with that, they also get application-level experience through projects and practicals. The students get the relevant resources, training, and insights to help them acquire proper knowledge in the field and outshine the crowd as professionally backed up individuals.

GQT’s Data Science course is designed by industry experts keeping in mind the requirements and necessities for an industry-ready individual. Our experts provide thorough and personalized guidance that caters to the individualistic needs to grow and learn.

With GQT’s we ensure that the students are well-through with the theoretical insights and capable of hands-on implementation of the knowledge they acquire. Our courses are accompanied by relevant aptitude training and projects that would give the students a clear idea of the practical use of what they learn.

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

Basic Data Structures (2hrs)

• List and Tuples
• Sets and Dictionaries
• Associated Methods

Conditions/Loops (4hrs)

• If -elif -else
• Nested Conditions
• AND , OR Operations
• For and while loops
• Loop Counters
• Loops with conditions
• Nested loops

Functions (2hrs)

• Functions with parameters
• Functions with return
• Closures
• Recursive functions

Objects and Classes (4hrs)

• Object and Methods
• Polymorphism
• Abstract Methods
• Compositions

Numpy, Pandas and Visualization

Numerical Python (Numpy) (4hrs)

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

Data Manipulation with Pandas (4hrs)

• 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 (2hrs)

• 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 (4hrs)

• 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 (4hrs)

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

Hypothesis Testing (6hrs)

• 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 (2hrs)

• 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 DataWrangling (4hrs)

• 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 (4hrs)

• Filter Methods
• Wrapper Methods
• Embedding

Linear Regression (4hrs)

• 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 (4hrs)

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

Polynomial Regression (2hrs)

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

Regularization with Lasso/Ridge Regression (4hrs)

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

K Nearest Neighbours (2hrs)

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

Logistic Regression (4hrs)

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

Support Vector Machine (SVM) (4hrs)

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

Decision Tree (6hrs)

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

Naive Bayes (2hrs)

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

Cross-Validation Techniques (4hrs)

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

Evaluation Metrics (4hrs)

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

Ensembling (6hrs)

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

Unsupervised Learning

Clustering (8hrs)

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 (8hrs)

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

Time Series (4hrs)

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

Recommender System (2hrs)

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

ML based Projects (20hrs)

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

Deep Learning

Artificial Neural Networks In Python (2hrs)

• 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 (2hrs)

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

Optimizers (4hrs)

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

Tensorflow / Keras (4hrs)

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

Pytorch (6hrs)

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

Types of Deep Learning Algorithms / Algos (2hrs)

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

Computer Vision and use of OpenCV (8hrs)

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

CNN Architectures (4hrs)

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

Transfer Learning (4hrs)

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

AutoEncoders (4hrs)

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

GANs (4hrs)

• Generators
• Discriminator
• Structure

NLP Natural Learning Process with NLTK/ Spacy (4hrs)

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

Recurrent Neural Networks (6hrs)

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

Different types of Transformers (2hrs)

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

Generative AI and Prompt Engineering (4hrs)

• Prompt Engineering
• Chain of Thought (CoT)

LangChain(4hrs)

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

Fine Tuning (4hrs)

• Multi Instruction Fine Tuning
• Parameter Efficient (PEFT)

Low Rank Matrices (4hrs)

• LoRA
• Quantization
• QLoRA

Retrieval Augmentation Systems (4hrs)

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

Miscellaneous Topics of GenAI (4hrs)

• Knowledge Graphs
• Vector Database
• PAL model

Reinforcement Learning (4hrs)

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

Environments (4hrs)

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

Deep Reinforcement Learning (4hrs)

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

Policy Gradient Methods (4hrs)

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

Projects Deep Learning (4hrs)

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

Course Features

Mentorship by Industry Experts

Get hands-on learning from the industry experts and polish your real-life application skills.

Industrial Boost camps

Polish your skills for the industry with the updated Industrial Boot Camps

Peer Networking

Learn with the like-minded individuals and grow together with them with the network you form

Placement Assistance

Get thorough placement assistance from finding openings to cracking the interview.

Course Fee

Online/Offline Training + Mentorship

Rs. 50,000/-

Register Here

Frequently Asked Questions

Why should I take the Data Science 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

I enrolled for the Unified Course - 3 and I think that was a right choice that I made. I have been able to have a grasp on the course, also I was able to get thorough assistance with my placement. They also helped me work on my soft skills and personality development.

The live sessions have been beneficial for me in understanding the concept. The instructors are very well thorough with the updated trends of the corporate industry and they provided guidance on a very personal level to help me improve my skills

I am impressed by the way they have helped me in getting placed. During the course they provided great help in developing skills like etiquettes and personality development, but even after that they have been there throughout the process of my placement.

I am glad I found GQT. The way they trained me from start to end was really detailed and I liked the way they have handled me when I was here. From the live classes to skill building to helping me find good job openings, they have been there throughout, helping me prepare to crack the interviews.

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