Data Science

Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms andsystems to extract knowledge and insights from many structural and unstructured data.

Training Features

Instructor-Led Training Sessions

We believe to provide our students the Best interactive experience as part of their learning

What is Data Science

Data Science is the technology that goes behind handling and working with data in the 21st century.
Data Science concepts have proved to be the pinnacle in the last couple of years with the onset of Artificial Intelligence and Deep Learning.
The core foundation of Data Science lies in the aspect of how insights can be drawn about using raw and unstructured data.
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data.
Data science is related to data mining, deep learning and big data.
Data Science is the future of Artificial Intelligence.
Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data.
Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data.
Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence.
Data science provides meaningful information based on large amounts of complex data or big data.
Data science, or data-driven science, combines different fields of work in statistics and computation to interpret data for decision-making purposes.

Course Syllabus

DATA SCIENCE SYLLABUS

Data mining
Statistics
Machine learning
Information visualization
Network analysis
Algorithms
Software engineering
Databases
Distributed systems
Big data

MODULES

Machine Learning: Students will learn how to explore new data sets, implement a HOURS comprehensive set of machine learning algorithms from scratch, andmaster all the components of a predictive model, such as data preprocessing, feature engineering, model selection, performance metrics and hyperparameter optimization.

Result Modeling: Regression, Classification, Data Preprocessing, Model Evaluation and Ensembles

Data Mining: Dimensionality Reduction, Clustering, Association Rules, Anomaly Detection,
Network Analysis and Recommender Systems.

Special Topics: Data Engineering, Natural Language Processing, and Web Applications.

Applied Projects: After mastering the curriculum, the Project Phase is all about applying HOURS
what you’ve learned on either mock or live industry projects. You’ll be given the opportunity to create real-world deliverable’s.

Career Prep: The support and mentorship doesn’t end at graduation. We’re personally HOURS committed to working with every one of our alumni for as long as they need to continue evaluating work, providing mentorship and guidance, and facilitating their job search.

Workshops: Resume, Cover Letter, LinkedIn, GitHub Portfolio and Networking Advice.

Interview Preparation: Behavioral Questions and Mock Technical Challenges

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