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

Learners with basic data science knowledge or completion of the 1-month course.

Objective

Develop intermediate skills in machine learning, data visualization, and handling real-world datasets.

What You'll Learn

240 hours of comprehensive training

20 hours per week

Hands-on practical experience

Industry-relevant curriculum

Expert instructors

Week 1-3: Advanced Data Handling and Statistics

Advanced Python and Libraries (12 hours)
- Advanced Pandas: Groupby, pivot tables, time-series analysis.
- Introduction to SciPy for statistical computations.
Inferential Statistics (10 hours)
- Confidence intervals, p-values, ANOVA.
- A/B testing and experimental design.

Lab: Analyzing time-series data and performing statistical tests (14 hours).

Week 4-6: Machine Learning Algorithms

Supervised Learning (10 hours)
- Decision trees, random forests, SVM.
- Model evaluation: Cross-validation, ROC curves.
Unsupervised Learning (12 hours)
- Clustering: K-Means, hierarchical.
- Dimensionality reduction: PCA, t-SNE.
Lab: Building and evaluating ML models on datasets (14 hours).

Week 7-9: Data Visualization and SQL

Advanced Data Visualization (12 hours)
- Geospatial visualization with Folium.
- Building interactive dashboards with Dash.
SQL for Data Science (10 hours)
- Advanced queries: Subqueries, window functions.
- Integrating SQL with Python (SQLAlchemy).
Lab: Creating dashboards and querying large datasets (14 hours).

Week 10-12: Intermediate Projects and Deployment

Feature Engineering and Pipelines (10 hours)
- Automated feature selection and ML pipelines with scikit-learn.
- Handling imbalanced data.
Project Management (8 hours)
- Version control with Git, data ethics.
- Deploying models with Flask.
Lab: Building an end-to-end ML pipeline (18 hours).
Capstone Project: Develop a data science project (e.g., predictive modeling) (10 hours).

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 240 hours
Weekly 20 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

Selected

Advanced Data Science - 6 month

Duration: 6-month

Select

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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