ELINAUT

E L I N A U T

Address
Vennala puthiya Road Thykauv jun, opp. Clearway Appt, Arakkakadavu, Kakkanad, Kerala 682028
Email
mail@elinaut.com
Contact
+91 9567863498

Select Curriculum:

Introduction to Data Science - 1-month Intermediate Data Science - 3 Month Advanced Data Science - 6 month

Target Audience

Intermediate learners or those who completed the 3-month course.

Objective

Master advanced data science techniques, including deep learning, big data, and real-world applications.

What You'll Learn

20 hours of comprehensive training

480 hours per week

Hands-on practical experience

Industry-relevant curriculum

Expert instructors

Week 1-6: Advanced Machine Learning

Advanced ML Algorithms (18 hours)
- Ensemble methods: XGBoost, LightGBM.
- Time-series forecasting with ARIMA, Prophet.
Deep Learning Fundamentals (12 hours)
- Neural networks with TensorFlow/Keras.
- CNNs for image data, RNNs for sequences.
Lab: Implementing advanced ML models (20 hours).
Model Optimization (10 hours)
- Hyperparameter tuning with GridSearch, Bayesian optimization.
- Explainable AI (XAI) techniques.

Week 7-12: Big Data and Cloud Computing

Big Data Technologies (15 hours)
- Introduction to Hadoop, Spark.
- Processing large datasets with PySpark.
Cloud for Data Science (15 hours)
- AWS/GCP services: S3, EC2, SageMaker.
- Building data pipelines in the cloud.
Lab: Handling big data with Spark and cloud deployment (20 hours).
NLP Basics (10 hours)
- Text processing, sentiment analysis with NLTK/Spacy.

Week 13-18: Deep Learning and NLP

Advanced Deep Learning (15 hours)
- Transfer learning, GANs.
- Transformers: BERT for NLP tasks.
Advanced NLP (12 hours)
- Sequence models, chatbots.
- Multimodal data (text + image).
Lab: Building deep learning models for NLP or vision (23 hours).

Week 19-24: Advanced Projects and Industry Applications

Data Science in Industry (15 hours)
- Case studies: Recommendation systems, fraud detection.
- MLOps: CI/CD for ML models.
Project Management (10 hours)
- Agile methodologies, portfolio building.
- Ethics and bias in AI.
Lab: Developing industry-level data pipelines (25 hours).
Capstone Project: End-to-end data science project with deployment (20 hours).
Industry Case Studies and Certification Prep (10 hours)
- Real-world projects analysis.
- Preparation for certifications (e.g., AWS Certified Data Scientist, Google Data Analytics).

Prerequisites

Basic programming knowledge for the 1-month course; completion of prior modules or equivalent for 3-month and 6-month courses.

Tools & Software

Python

Jupyter

scikit-learn

TensorFlow

Spark

SQL

AWS/GCP.

Delivery Mode

Combination of lectures, hands-on labs, and project work.

Assessment

Weekly quizzes, lab assignments, and capstone projects.

Certification

Certificate of completion for each module; preparation for industry-recognized certifications in the 6-month course.

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Duration 20 hours
Weekly 480 hours
Level All Levels

Available Curricula

Introduction to Data Science - 1-month

Duration: 1-month

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Intermediate Data Science - 3 Month

Duration: 3-month

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Advanced Data Science - 6 month

Duration: 6-month

Selected

Course Highlights

Expert instructors with industry experience
Hands-on projects and real-world applications
Flexible learning schedule
Placement assistance

Diploma in Data Science

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