Data Science

ClusterPhi
Last Update September 6, 2022

About This Course

Data science is a multidisciplinary course that involves scientific procedures, methods,
algorithms, and systems that fetch knowledge from disparate structured and unstructured
sources of data, which can then be applied to glean actionable insights across a wide range of applications.

Tags

Learning Objectives

Preparation of Technical And HR Interview Rounds
Practice Assignments

Requirements

  • Bachelor’s Degree with minimum 50% or equivalent passing marks.
  • No coding experience required.

Target Audience

  • Software & IT Professionals, Freshers, Engineers, Marketing & Sales Professionals.

Curriculum

133 Lessons

Pre-Program Preparatory Content

DATA ANALYSIS IN EXCEL
ANALYTICS PROBLEM SOLVING

DATA TOOLKIT

MACHINE LEARNING

“ADVANCED MACHINE LEARNING AND STORYTELLING”

"SPECIALISATION 1: DATA SCIENCE GENERALIST"

ADVANCED PROGRAMMING AND DATABASES

ADVANCED MACHINE LEARNING

SPECIALISATION 2: NATURAL LANGUAGE PROCESSING

NATURAL LANGUAGE PROCESSING

CAPSTONE

ADVANCED MACHINE LEARNING

"SPECIALISATION 3: DEEP LEARNING"

DEEP LEARNING AND NEURAL NETWORKS

CAPSTONE

ADVANCED MACHINE LEARNING

"SPECIALISATION 4: Business Analytics"

BUSINESS REQUIREMENTS

CAPSTONE PROJECT

SQL AND NOSQL DATABASES

"SPECIALISATION 5: BUSINESS INTELLIGENCE / DATA ANALYTICS"

STORYTELLING WITH ADVANCED VISUALISATIONS

CAPSTONE PROJECT

DATA ENGINEERING I

"SPECIALISATION 6: DATA ENGINEERING"

DATA ENGINEERING – II

CAPSTONE PROJECT

“RESEARCH METHODOLOGIES “

DISSERTATION

EXAMPLES OF PROJECT OUTLINES:

"• Investigate the risk factors for eye disease from complex longitudinal datasets • Investigate a diagnosis of eye diseases using imaging ophthalmic data • Multi-task learning for drug design and discovery • Using stacking for brain tumour discrimination • Investigate dietary patterns and metabolite fingerprints of takeaway (fast) food consumers using PCA and Clustering methods • Longitudinal studies to investigate the complex link between corporate environment engagement, green disclosure, business model transformation and supply chain performance • Preventing credit card fraud through pattern recognition • Developing a recommender system for a Media giant • Using social media feed to place tweets regarding natural disasters on a map”"

Your Instructors

ClusterPhi

4.0/5
18 Courses
1 Review
66 Students
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Free
Level
All Levels
Lectures
133 lectures
Subject
Language
English
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