3-Month Intensive Program · Trivandrum, Kerala

Decode data, design intelligence.

Master the complete data science pipeline — from raw data to predictive AI — through hands-on projects, expert mentorship, and an industry-aligned curriculum at Kerala's trusted data science institute.

17
Modules
3
Months
100%
Project-Based
Explore Syllabus → Talk to an Advisor
Python Machine Learning Deep Learning NLP
Python Machine Learning Pandas TensorFlow Deep Learning NLP SQL PyTorch Statistics Scikit-Learn Python Machine Learning Pandas TensorFlow Deep Learning NLP SQL PyTorch Statistics Scikit-Learn
01 / THE JOURNEY

The data science pipeline.

Every dataset tells a story. You'll learn to walk through every stage — from messy raw data to deployed intelligence — using the same workflow practiced by industry teams worldwide.

01
Collect
APIs, scraping, IoT, databases
02
Clean
Missing values, outliers, encoding
03
Analyze
EDA, statistics, visualization
04
Model
ML, deep learning, validation
05
Deploy
Production, monitoring, ethics
02 / CURRICULUM

17 modules. One complete journey.

A meticulously crafted curriculum spanning foundational statistics to cutting-edge transformer architectures. Built to make you industry-ready, not just exam-ready.

01

Foundations of Data Science

  • Data science overview & workflow
  • Roles: science vs analytics vs engineering
  • Types of data analysis
  • Exploratory Data Analysis (EDA)
  • Business intelligence tools
02

Preprocessing & Feature Engineering

  • Cleaning & handling missing values
  • Outlier detection & treatment
  • Feature scaling & encoding
  • Dimensionality reduction (PCA)
  • SMOTE & dataset balancing
03

Artificial Intelligence Foundations

  • Goals & history of AI
  • Narrow, general & super AI
  • NLP, vision, robotics subfields
  • ML vs deep learning
  • Real-world case studies
04

Understanding Data

  • Structured & unstructured data
  • CSV, JSON, XML, HDF5 formats
  • APIs, web scraping, IoT
  • Data quality & validation
  • Governance, privacy, ethics
05

Statistics & Probability

  • Descriptive & inferential statistics
  • Hypothesis testing
  • Bayes' theorem & probability
  • Normal, binomial, Poisson distributions
  • Central limit theorem
06

Python for Data Science

  • NumPy & linear algebra
  • SciPy for optimization
  • Pandas for manipulation
  • Scikit-learn pipelines
  • Jupyter & Google Colab
07

Data Visualization

  • Visualization principles
  • Matplotlib & Seaborn
  • Plotly & Dash interactivity
  • Heatmaps & violin plots
  • Storytelling with data
08

Machine Learning

  • Supervised: regression, classification
  • Ensemble: bagging, boosting, XGBoost
  • Random forest, KNN, SVM
  • Clustering & association rules
  • Reinforcement learning
09

Model Development Lifecycle

  • Phases of model development
  • Data preparation
  • Building & evaluation
  • Cross-validation
  • Deployment & maintenance
10

Evaluation & Validation

  • Accuracy, precision, recall, F1
  • MAE, MSE, RMSE, R²
  • Confusion matrix analysis
  • Cross-validation, bootstrapping
  • Bias-variance tradeoff
11

Deep Learning Foundations

  • Neural networks & perceptrons
  • Activation functions & optimizers
  • Backpropagation & gradient descent
  • Dropout & batch normalization
  • Callbacks & checkpoints
12

Deep Learning Libraries

  • TensorFlow
  • Keras
  • PyTorch
  • OpenCV for vision
  • Scikit-learn integration
13

Neural Network Architectures

  • Multilayer Perceptron (MLP)
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Architecture design principles
  • Hands-on implementation
14

Natural Language Processing

  • Tokenization & lemmatization
  • Bag of Words & TF-IDF
  • Word embeddings (Word2Vec, GloVe)
  • BERT, GPT overview
  • Sentiment analysis & chatbots
15

Sequence Models & Attention

  • Sequence-to-sequence models
  • LSTM & GRU architectures
  • Attention mechanism
  • Transformer & self-attention
  • BERT & GPT internals
16

Ethics & Explainable AI

  • Fairness & bias in ML
  • Interpretability vs explainability
  • LIME, SHAP, PDPs
  • Ethical AI design
  • Responsible & transparent AI
17

Transfer Learning & Optimization

  • Transfer learning concepts
  • Fine-tuning pre-trained models
  • Model compression & quantization
  • Hyperparameter tuning (Optuna)
  • Data augmentation
03 / TOOLKIT

Tools you'll actually use.

Industry-standard frameworks and libraries that power the world's data-driven products. Master the same stack used at top tech companies.

Python
SQL
NumPy
Pandas
SciPy
Matplotlib
Seaborn
Plotly
Scikit-Learn
TensorFlow
PyTorch
Keras
OpenCV
XGBoost
Jupyter
Google Colab
SHAP
LIME
Optuna
Word2Vec
BERT
GPT
ONNX
Dash
04 / THE EDGE

Why Trinity Technologies.

Kerala's trusted destination for data science education. Six reasons learners and recruiters keep coming back to Trinity.

A

Expert Instructors

Learn from industry professionals who've shipped real ML systems. Our mentors guide you through concepts, projects, and career challenges with hands-on expertise.

B

Modern Infrastructure

State-of-the-art labs with high-performance computing, GPU access, and the same tools used across healthcare, finance, marketing, and tech industries.

C

Comprehensive Curriculum

From data wrangling to deep learning to ethics — a syllabus aligned with current industry demand and certification standards. Foundational and advanced, balanced.

D

Hands-On Labs

Work with live datasets, train models, deploy solutions. Theory connects to practice through capstone projects that build a portfolio recruiters notice.

E

Assessments & Mock Tests

Regular evaluations and certification-style mocks ensure you're industry-ready. Constructive feedback continually refines your problem-solving instincts.

F

Placement Support

Resume building, portfolio reviews, interview prep, and direct connections to hiring partners. A dedicated placement team ensures a smooth transition to your first role.

05 / CAREER PATHS

Roles you can step into.

Graduates of Trinity's Data Science program go on to build careers across diverse sectors — from fintech and healthcare to e-commerce and AI research.

Tech · Research
Data Scientist
AI · Engineering
ML Engineer
Business · Insights
Data Analyst
Research · Product
AI Specialist
Strategy · Reporting
BI Analyst
NLP · Voice
NLP Engineer
Computer Vision
CV Engineer
Deep Learning
DL Researcher
06 / QUESTIONS

Things you might be wondering.

Honest answers to the questions we hear most often from prospective students.

How long is the program? +
A comprehensive 3 to 6 month program, with the core intensive lasting 3 months. The duration depends on the depth of modules — foundational topics, advanced techniques, and project-based learning all included.
Who can join? Do I need coding experience? +
Beginners, graduates, and working professionals are all welcome. While basic mathematics and computer literacy help, foundational modules are built in. Motivated beginners with no coding experience consistently succeed in the program.
What's the difference between data analytics and data science? +
Data Analytics focuses on analyzing past data to derive insights for decision-making. Data Science is broader — it includes analytics but also predictive modeling, machine learning, and building AI-driven solutions. Trinity's curriculum covers both.
Are there real projects and a capstone? +
Yes. The program is built around hands-on assignments and a capstone where you apply end-to-end data science techniques on real-world datasets. You finish with a portfolio you can show to recruiters.
Do you offer placement assistance? +
Absolutely. Trinity provides personalized career guidance, resume and portfolio support, interview preparation, and access to industry networks. The dedicated placement team partners with companies to provide internship and job opportunities.
Are online classes available? +
Yes. Trinity offers flexible options including live online sessions, recorded lectures for revision, and on-campus classes at the Trivandrum centre. Pick what fits your schedule.
Will I get a certificate? +
Yes. On completion, you receive a Trinity Technologies Data Science certification that highlights the skills you've acquired and the projects you've completed during the program.
What programming languages will I learn? +
Python and SQL form the core, along with key libraries: pandas, NumPy, matplotlib, seaborn, and scikit-learn. Depending on the batch, TensorFlow and Keras for deep learning are also introduced.

Your data science career starts here.

Speak with our team. Visit our Trivandrum centre. Or enroll today.

Call: +91 94473 87064 → info@trinitytechnology.in