Master of Science in Computer Science
Course Information
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Overview
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Learning Outcomes
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Who Should Attend
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Testimonials
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Trainer's Profile
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Course Outline
Learn in-depth about frontend development (HTML, CSS, Javascript), backend (NoSQL-MongoDB) and microservices and kick-start your career as a full stack developer/ software engineer with the tech giants across the globe.
6 Specializations to chose from:
1. BigData Programming
2. Cybersecurity
3. Devops
4. Blockchain
5. Cloud Backend Development
6. Full Stack Development
You will learn: 1. Statistics, Predictive Analytics, Exploratory Data Analysis
2. Machine Learning, Deep Learning
3. Data Visualization, Big Data Analytics, Data Engineering
4. Python, Tableau, MySQL, Advanced Excel etc.
Who Is This Program For?:
IT and Technology Professionals, Project Leads and Managers in IT/Tech Companies, Data Professionals, Java & Other Coding Professionals, Testing Professionals.
Minimum Eligibility:
Bachelor’s Degree with 50% or equivalent passing marks. No coding experience required.
"The program has been very useful and my experience with upGrad and the student mentors at upGrad has been very good. The content taught is very relatable and the method of delivery is also convenient for working professionals like us."- Joel Varghese Software Engineer, Zoreum Blockchain Labs
Dr. Gabriela Czanner
Faculty- Engineering and Technology
Senior Lecturer in Statistics and Data Science at Department of Applied Mathematics at LJMU. Her research focus is advanced statistics for decision support.
Prof. Paulo Lisboa
Head of Dept - Applied Mathematics
Studied mathematics physics at LU and was the chairman of Industrial Mathematics at JMU in 1996 and Head of Graduate School in 2002.
Pre-Program Preparatory Content
- Data Analysis in Excel
- Analytics Problem Solving
Data Toolkit (12 Weeks)
- Introduction to Python
- Programming in Python
- Python for Data Science
- Data Visualization in Python
- Exploratory Data Analysis
- Credit EDA Case Study
- Inferential Statistics
- Hypothesis Testing
- Data Analysis using SQL
- Advaced SQL & Best Practices
- SQL Assignment: RSVP Movies
- Machine Learning (10 Weeks)
Machine Learning (10 Weeks)
- Linear Regression
- Linear Regression Assignment
- Logistic Regression
- Classification using Decision Trees
- Unsupervised learning: Clustering
- Basics of NLP and Text Mining
- Business Problem Solving
- Case Study: Lead Scoring
Specialisation- Deep Learning (22 Weeks)
- Bagging & Random Forest
- Boosting
- Model Selection & General ML Techniques
- Principal Component Analysis
- Advanced Regression
- Advanced ML case Stuy
- Time Series Analysis
- Introduction to Neural Networks and ANN
- Neural Network Assignment
- Convolutional Neural Networks
- Convolutional Neural Networks -Industry Applications
- Object Detection & Image Segmentation (Optional)
- Recurrent Neural Networks
- Gesture Recognition
- Capstone Project
Specialisation- Business Intelligence/ Data Analysis (22 Weeks)
- Visualisation using Tableau
- Advanced Excel
- Visualisation using PowerBI
- Structured Problem Solving using Frameworks
- Data Storytelling
- AirBnB Case Study
- Data Modelling
- Advanced SQL and Best Practices
- Introduction to Big Data and Cloud
- Analytics using Spark
- Big Data Case Study
- Data Structures - Sets, Dictionaries, Stacks, Queues
- Searching and Sorting
- Algorithm Analysis + Recursion
- Advanced Database Programming using Pandas
- Python & SQL Lab
- Capstone Project
Specialisation- Data Engineering (22 Weeks)
- Data Management and Relational Database Modelling
- Introduction to Big Data(Optional)
- Introduction to Cloud and AWS Setup
- Introduction to Hadoop and MapReduce Programming
- Assignment (Optional)
- NoSQL Databases and Apache HBase and NoSQL Databases and MongoDB(Optional)
- Data Warehousing (Optional)
- Data Ingestion with Apache Sqoop and Apache Flume
- Map reduce Programming Assignment
- Hive & Querying
- Assignment (Optional)
- Amazon Redshift
- Introduction to Apache Spark
- Project: ETL Data Pipline
- AWS Cloud Infrastructure (Optional)
- Optimising Spark for Large Scale Data Processing
- Apache Flink(Optional)
- Real-Time Data Streaming with Apache Kafka
- Real-Time Data Processing using Spark Streaming
- Assignment (Optional)
- Building Automated Data Pipelines with Airflow
- Analytics using PySpark
- Project: Real Time data processing
- Capstone Project
Research Methodologies (8 Weeks)
- Introduction to Research and Research Process
- Research Design
- Literature Reviewing
- Research Project Management
- Report Writing and Presentation Skills
- Scientific Ethics
Master's Dissertation (16 Weeks)
- Investigate dietary patterns and metabolite fingerprints of takeaway (fast) food consumers using PCA and Clustering methods
- Investigate a diagnosis of eye diseases using imaging ophthalmic data
- Structure medical images with information geometry
- Using Social media feed to place tweets regarding natural disasters on a map
- Preventing credit card fraud through pattern recognition
- Developing a recommender system for a Media giant
- Risk modelling for Financial activities and Investment Banking
