GAINTECH NEXUS • DATA SCIENCE

Build Skills.
Build Projects.
Build Your Career.

A structured Data Science journey covering Python, Statistics, Data Analysis, Machine Learning, Deep Learning, NLP, Computer Vision and Time Series Forecasting.

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DATA SCIENCE CAREER PROGRAM

From Python Basics to Advanced Data Science

PythonStatisticsSQL Power BIMachine LearningDeep Learning NLPComputer VisionTime Series
One page. Complete course clarity.

Explore curriculum, plans, projects and career support before you enrol.

Python → AIStructured learning path
Detailed SyllabusTopic-by-topic curriculum
Practical ProjectsLearn by building
Career SupportResume & interview assistance
CHOOSE YOUR PLAN

Four Learning Plans. One Clear Career Path.

Start with the level you need and upgrade as your skills grow.

FOUNDATION

Silver

₹20,000

Build a strong foundation in programming, analytics and business intelligence.

  • Python
  • NumPy
  • Pandas
  • Matplotlib
  • Seaborn
  • Power BI
  • SQL
  • Placement Assistance
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MOST POPULAR
PROFESSIONAL

Golden

₹40,000

Add statistics, advanced Excel, Machine Learning and GenAI-oriented learning.

  • Everything in Silver
  • Statistics
  • Advanced Excel
  • Machine Learning
  • GenAI
  • Placement Assistance
  • Resume Session
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ADVANCED

Platinum

₹60,000

Move into advanced Data Science with Deep Learning and broader AI skills.

  • Everything in Golden
  • MongoDB
  • Deep Learning
  • Advanced ML
  • GenAI
  • Placement Assistance
  • Resume Session
  • Limited Mock Interviews
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COMPLETE

Diamond

₹80,000

The complete advanced track for learners targeting a broader AI and Data Science skill set.

  • Everything in Platinum
  • NLP
  • Computer Vision
  • Time Series Forecasting
  • GenAI
  • Placement Assistance
  • Resume Session
  • Unlimited Mock Interviews
Book My Seat
WHY GAINTECH NEXUS

Learn the skills. Understand the concepts. Build the projects.

Designed as a practical learning journey rather than a list of disconnected tools.

01

Concept to Application

Learn fundamentals first, then apply them to real data and practical problems.

02

Industry-Relevant Stack

Python, SQL, Power BI, Machine Learning and advanced AI topics in one learning path.

03

Project-Based Learning

Build portfolio-ready projects while learning the tools and techniques.

04

Career Preparation

Resume preparation, interview practice and placement assistance as applicable to your plan.

DETAILED CURRICULUM

Complete Data Science Syllabus

A detailed, long-form curriculum so students can clearly see what they will learn.

01

Python Programming

Build the programming foundation required for Data Science.

Python Fundamentals

  • Variables & Naming Conventions
  • Numbers, Strings, Lists, Tuples, Sets & Dictionaries
  • Type Casting & Type Checking
  • Input / Output Operations
  • Operators
  • String Methods & Formatting
  • List / Dictionary Methods

Control Flow & Functions

  • If / Elif / Else Conditions
  • Nested Conditions
  • For & While Loops
  • Break, Continue & Pass
  • Functions & Parameters
  • Return Values
  • Lambda Functions
  • Scope & Recursion

Advanced Python

  • Exception Handling
  • Object-Oriented Programming
  • Classes & Objects
  • Constructors & Methods
  • Inheritance & Polymorphism
  • Encapsulation
  • Regular Expressions
  • File I/O
  • Modules & Packages
02

Data Analysis Libraries

Work with data, clean it, analyse it and communicate insights visually.

NumPy

  • Arrays & Dimensions
  • Array Creation
  • Indexing & Slicing
  • Reshaping
  • Broadcasting
  • Vectorized Operations
  • Statistical Functions

Pandas

  • Series & DataFrame
  • Data Import / Export
  • Data Cleaning
  • Missing Values
  • Duplicates
  • Filtering & Sorting
  • GroupBy & Aggregation
  • Merge, Join & Concatenate
  • Pivot Tables & Crosstab
  • Date / Time Data

Visualization & EDA

  • Matplotlib
  • Seaborn
  • Plotly
  • Interactive Charts
  • Distribution Analysis
  • Correlation & Heatmaps
  • Univariate & Bivariate Analysis

Data Profiling & Exploration

  • D-Tale
  • Pandas Profiling / YData Profiling
  • Automated EDA
  • Data Quality Checks
  • Interactive Data Exploration

Geospatial & Business Analysis

  • Folium
  • Interactive Maps
  • Location-based Analysis
  • Business KPI Analysis
  • Insight Generation
03

Statistics

Develop the statistical thinking required for data-driven decision making.

Foundations

  • Math & Probability
  • Statistical Thinking
  • Population vs Sample
  • Sample & Its Types
  • 5-Point Summary

Inference

  • Inferential Statistics
  • Hypothesis Testing
  • Type I & Type II Errors
  • Binomial Distribution
  • Normal Distribution

Mathematical Foundation

  • Linear Algebra
  • Probability Concepts
  • Statistical Interpretation
04

Machine Learning

Understand, train and compare machine learning models.

ML Foundations

  • Types of Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Semi-Supervised Learning
  • Machine Learning Workflow
  • Dataset & Feature Understanding
  • Target Variable & Feature Selection
  • Missing Value Handling
  • Duplicate & Outlier Handling
  • Data Cleaning for Machine Learning
  • Encoding Categorical Variables
  • Label Encoding
  • One-Hot Encoding
  • Feature Preparation
  • Feature Scaling
  • Standardization & Normalization
  • Train-Test Split
  • Cross Validation Basics
  • Feature Selection
  • Dimensionality Reduction
  • PCA (Principal Component Analysis)
  • Model Training & Prediction
  • Overfitting & Underfitting
  • Bias & Variance
  • Model Evaluation Basics

Supervised Learning

  • Introduction to Supervised Learning
  • Regression vs Classification
  • Linear Regression
  • Simple Linear Regression
  • Multiple Linear Regression
  • Cost Function & Error Measurement
  • Logistic Regression
  • Classification using Logistic Regression
  • Decision Tree
  • Splitting Criteria: Gini Impurity & Entropy
  • Tree Depth & Pruning
  • Random Forest
  • Bagging & Ensemble Learning
  • Feature Importance
  • K-Nearest Neighbors (KNN)
  • Distance Metrics
  • Choosing the Value of K
  • Support Vector Machine (SVM)
  • Linear & Non-Linear SVM
  • Kernel Functions
  • Hyperplane & Margin
  • XGBoost
  • Gradient Boosting Concept
  • Boosting Parameters
  • Naive Bayes
  • Bayes Theorem
  • Gaussian, Multinomial & Bernoulli Naive Bayes

Unsupervised Learning

  • Introduction to Unsupervised Learning
  • Clustering Concepts
  • K-Means Clustering
  • Choosing the Optimal Number of Clusters
  • Elbow Method
  • Centroid Initialization
  • Distance Metrics in Clustering
  • Cluster Assignment & Centroid Updating
  • K-Means++ Initialization
  • Hierarchical Clustering
  • Agglomerative Clustering
  • Dendrograms
  • DBSCAN
  • Core Points, Border Points & Noise
  • Density-Based Clustering
  • Clustering with High-Dimensional Data
  • Feature Scaling for Clustering
  • PCA for Clustering
  • Cluster Visualization
  • Silhouette Score
  • Inertia / Within-Cluster Sum of Squares
  • Davies-Bouldin Index
  • Cluster Interpretation & Profiling
  • Practical Clustering Projects
05

Model Evaluation

Model Performance Evaluation.

Classification Evaluation

  • Classification Concepts
  • Binary Classification
  • Multiclass Classification
  • Multilabel Classification
  • Confusion Matrix
  • True Positive, True Negative, False Positive & False Negative
  • Accuracy
  • Precision
  • Recall
  • F1 Score
  • Classification Report
  • ROC Curve
  • ROC-AUC Score
  • Precision-Recall Curve
  • Log Loss
  • Classification Threshold
  • Probability-Based Predictions
  • Class Imbalance & Evaluation
  • Cross-Validation for Classification
  • Model Performance Comparison

Regression Evaluation

  • Regression Concepts
  • Simple Linear Regression
  • Multiple Linear Regression
  • Regression Line & Predictions
  • Mean Absolute Error (MAE)
  • Mean Squared Error (MSE)
  • Root Mean Squared Error (RMSE)
  • R² Score
  • Adjusted R² Score
  • Mean Absolute Percentage Error (MAPE)
  • Residual Analysis
  • Actual vs Predicted Analysis
  • Regression Error Distribution
  • Overfitting & Underfitting in Regression
  • Feature Importance in Regression
  • Cross-Validation for Regression
  • Prediction Interval Concepts
  • Model Performance Comparison
06

SQL & Database Management

Learn to work with structured data and databases.

SQL

  • Database Fundamentals
  • Database Operations
  • Creating & Managing Databases
  • Creating & Managing Tables
  • SQL Statements
  • DDL, DML, DQL & DCL
  • SELECT Statements
  • Filtering with WHERE
  • Sorting with ORDER BY
  • LIMIT & OFFSET
  • Operators & Conditions
  • Aggregate Functions
  • GROUP BY
  • HAVING Clause
  • Joins
  • INNER JOIN
  • LEFT JOIN
  • RIGHT JOIN
  • FULL OUTER JOIN
  • Self Join
  • Subqueries
  • Nested Queries
  • CASE Statements
  • String Functions
  • Date & Time Functions
  • NULL Handling
  • Primary Key & Foreign Key
  • Constraints
  • Views
  • Common Table Expressions (CTEs)
  • Window Functions
  • ROW_NUMBER, RANK & DENSE_RANK
  • PARTITION BY
  • UNION & UNION ALL
  • Stored Procedures & Functions
  • Query Optimization Basics
  • Real-World SQL Queries & Business Problems

Advanced Database

  • Advanced SQL Concepts
  • Complex Queries & Nested Queries
  • Common Table Expressions (CTEs)
  • Window Functions
  • Ranking & Analytical Functions
  • Query Optimization Basics
  • Database Design & Relationships
  • MongoDB Introduction
  • MongoDB Architecture & Concepts
  • Collections & Documents
  • CRUD Operations in MongoDB
  • MongoDB Queries & Filters
  • MongoDB Operators
  • Sorting & Limiting Documents
  • Aggregation Pipeline
  • Indexes in MongoDB
  • Schema Design Basics
  • MongoDB Usage for Data Applications
  • Connecting MongoDB with Python
  • PyMongo Library
  • Reading & Writing MongoDB Data using Python
  • CRUD Operations with Python
  • Building Data Pipelines with MongoDB & Python
07

Power BI

Turn analysed data into business dashboards and insights.

Power BI & Data Preparation

  • Power BI Fundamentals
  • Power BI Desktop Interface
  • Connecting Data Sources
  • Excel, CSV & Database Connections
  • Data Import & Transformation
  • Power Query Fundamentals
  • Data Cleaning & Data Preparation
  • Handling Missing & Duplicate Data
  • Data Types & Formatting
  • Filtering & Sorting Data
  • Merge & Append Queries
  • Calculated Columns
  • Data Modelling Concepts
  • Tables, Relationships & Keys
  • Star Schema & Snowflake Schema
  • Relationship Types & Cardinality
  • Fact & Dimension Tables
  • Introduction to DAX
  • DAX Measures & Calculated Columns
  • Basic DAX Functions

Dashboard, DAX & Business Reporting

  • Dashboard Development
  • Report & Page Layout Design
  • Charts & Visualizations
  • Cards, Tables & Matrix Visuals
  • Slicers & Filters
  • Drill-Down & Drill-Through
  • Bookmarks & Buttons
  • Interactive Dashboard Design
  • Advanced DAX
  • Time Intelligence
  • KPIs & Business Metrics
  • Conditional Formatting
  • Dynamic Reports
  • Business Reporting
  • Sales & Revenue Analysis
  • Customer & Marketing Analytics
  • Financial & Performance Reporting
  • Dashboard Storytelling
  • Insight Communication
  • Business Insights & Decision Making
  • Power BI Service & Report Publishing
  • Dashboard Sharing & Collaboration
  • Real-World Power BI Projects
08

Deep Learning

Build the foundation for neural-network based applications.

Neural Networks

  • Introduction to Neural Networks
  • Neural Network Fundamentals
  • Biological Neuron vs Artificial Neuron
  • Artificial Neuron & Perceptron
  • Neural Network Architecture
  • Input, Hidden & Output Layers
  • Weights & Biases
  • Activation Functions
  • Sigmoid Activation Function
  • ReLU Activation Function
  • Tanh Activation Function
  • Softmax Activation Function
  • Single Layer Perceptron
  • Single Layer Networks
  • Multiple Layer Networks
  • Multilayer Perceptron (MLP)
  • Forward Propagation
  • Loss Functions
  • Binary Cross Entropy
  • Categorical Cross Entropy
  • Mean Squared Error
  • Backpropagation
  • Gradient Descent
  • Learning Rate
  • Epochs & Batch Size
  • Model Training Concepts
  • Training, Validation & Testing
  • Overfitting & Underfitting
  • Dropout & Regularization
  • Early Stopping
  • Model Performance Evaluation
  • Hyperparameter Tuning
  • Building Neural Network Models with TensorFlow & Keras
  • Practical Neural Network Projects

CNN

  • Introduction to Computer Vision
  • Computer Vision Fundamentals
  • Image Representation & Pixel Concepts
  • Image Classification
  • Convolutional Neural Networks (CNN)
  • CNN Architecture
  • Convolution Operation
  • Filters & Kernels
  • Feature Maps
  • Stride & Padding
  • Pooling Layers
  • Max Pooling & Average Pooling
  • Flattening & Fully Connected Layers
  • Activation Functions in CNN
  • Image Feature Learning
  • Low-Level & High-Level Features
  • Edge, Texture & Shape Detection
  • Training CNN Models
  • Loss Functions & Optimizers
  • Data Augmentation
  • Image Preprocessing & Normalization
  • Overfitting & Regularization
  • Transfer Learning
  • Pre-trained CNN Models
  • VGG16 & ResNet50
  • Model Evaluation for Image Classification
  • Practical Computer Vision Projects
09

Natural Language Processing

Work with text data and understand modern NLP workflows.

NLP Foundation & Text Processing

  • Introduction to Natural Language Processing
  • NLP Fundamentals
  • Text Pre-processing Steps
  • Text Cleaning
  • Lowercasing & Text Normalization
  • Removing Punctuation & Special Characters
  • Handling URLs, Numbers & Emojis
  • Stop Word Removal
  • Tokenization
  • Stemming
  • Lemmatization
  • Sentence & Word Tokenization
  • Feature Representation
  • Bag of Words (BoW)
  • TF-IDF
  • N-Gram Models
  • Word Embeddings
  • Word2Vec

NLP Algorithms, Applications & Libraries

  • Text Classification
  • Sentiment Analysis
  • Text Similarity
  • Text Clustering
  • Naive Bayes for Text Classification
  • Logistic Regression for NLP
  • Support Vector Machine for Text Classification
  • Model Training & Evaluation for NLP
  • Introduction to Sequence Models
  • RNN Fundamentals
  • LSTM & GRU Concepts
  • Introduction to Transformers
  • Attention Mechanism
  • Named Entity Recognition (NER)
  • Text Summarization
  • Question Answering
  • NLP Libraries
  • NLTK
  • spaCy
  • Scikit-learn for NLP
  • Transformers Library
  • Practical NLP Projects
10

Computer Vision

Learn the fundamentals of image understanding and object detection.

Computer Vision & Image Classification

  • Computer Vision Fundamentals
  • Image Processing vs Computer Vision
  • Image Representation & Pixel Concepts
  • Image Pre-processing
  • Image Resizing & Normalization
  • Image Augmentation
  • Image Classification Concepts
  • Binary & Multiclass Image Classification
  • Convolutional Neural Networks (CNN)
  • VGG16 Architecture
  • Transfer Learning with VGG16
  • ResNet50 Architecture
  • Residual & Skip Connections
  • Transfer Learning with ResNet50
  • Fine-Tuning Pre-trained Models
  • Image Classification Model Training
  • Image Classification Model Evaluation

Object Detection & YOLO

  • Object Detection Fundamentals
  • Object Detection vs Image Classification
  • Bounding Boxes
  • Class Labels & Confidence Scores
  • Intersection over Union (IoU)
  • Non-Maximum Suppression (NMS)
  • YOLO Fundamentals
  • YOLO Architecture
  • YOLO Detection Pipeline
  • YOLO Dataset Preparation
  • Image Annotation for Object Detection
  • YOLO Model Training
  • YOLO Model Inference
  • Real-Time Object Detection
  • Precision & Recall for Object Detection
  • Mean Average Precision (mAP)
  • Object Detection Model Evaluation
  • OpenCV for Computer Vision
  • Practical Object Detection Projects
11

Time Series Forecasting

Use historical data to understand patterns and build forecasting solutions.

Forecasting

  • Time Series Fundamentals
  • Trend & Pattern Analysis
  • Forecasting Concepts
  • Case Study 1
  • Case Study 2
PRACTICAL LEARNING

Build Projects While You Learn

Project work can be aligned with the topics covered in each learning stage.

PROJECT 01

Data Analysis & EDA

Clean a real dataset, perform exploratory analysis and present meaningful insights.

PROJECT 02

Regression Project

Build and evaluate a regression model with proper preprocessing and performance metrics.

PROJECT 03

Classification Project

Build a classification solution and analyse model performance using multiple metrics.

PROJECT 04

Power BI Dashboard

Transform business data into an interactive dashboard for decision making.

PROJECT 05

NLP / Text Project

Apply text preprocessing and NLP techniques to a practical text dataset.

PROJECT 06

Computer Vision / Forecasting

Apply advanced concepts to an image or time-series based case study.

CAREER SUPPORT

Learning doesn't stop at the syllabus.

Depending on the selected plan, GainTech Nexus provides career-oriented support to help learners prepare for opportunities.

✓Placement Assistance
✓Resume Preparation Session
✓Mock Interview Support
✓Practical Project Guidance
READY TO START?

Choose your Data Science learning plan.

Talk to GainTech Nexus and find the plan that fits your goal.

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