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Master's in Data Science in the UK

4,267 Ratings

Ranked #1 Data Science Program by India TV

Acquiring a Master’s in Data Science in the UK is easy with Intellipaat’s carefully structured course curriculum. It includes concepts such as SQL, MongoDB, etc., and industry-relevant projects for practical mastery of the subject matter. Our SMEs are well-experienced in the field and will help you 24/7 throughout the MS in Data Science course.

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Key Highlights

232 Hrs Instructor Led Training
104 Hrs Self-paced Videos
253 Hrs Project & Exercises
Certification
Job Assistance
Flexible Schedule
Lifetime Free Upgrade
Mentor Support
Trustpilot
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MS in Data Science in the UK Overview

What will you be taught in this Master's program in Data Science in the UK?

  • Data Scientist roles and responsibilities
  • Linear and logistic regression
  • Deep Learning models in AI
  • Real-time analytics using Spark
  • MapReduce
  • Deployment of recommender systems
  • R programming language
  • HDFS
  • NoSQL data
  • Clustering for prediction and analysis

No. You can sign up for our Data Science Master’s programs without any prerequisites.

  • Machine Learning experts
  • Information Architects
  • Data Scientists
  • Project Managers
  • Business Intelligence experts
  • Software developers
  • Data Science Architect aspirants
  • In the UK, there are 3,000+ jobs listed for Data Scientists – LinkedIn
  • By 2025, the global Big Data market will reach US$122 billion in revenue – Frost & Sullivan
  • On average, £53,793 is the annual salary of Data Scientists in the UK – Indeed
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Career Transition

55% Average Salary Hike

$1,20,000 Highest Salary

12000+ Career Transitions

300+ Hiring Partners

Career Transition Handbook

*Past record is no guarantee of future job prospects

Meet Your Mentors

What roles does a data scientist play?

Data Scientist

Develops high-quality applications, apart from designing and implementing scalable code.

Analytics and Insights Analyst

Investigates reported problems in the quality of data and comes up with solutions to fix them.

AI and ML Engineer

Deploys models in SageMaker and use Lambda functions and API Gateway to integrate machine learning models in web applications.

Data Engineer and Data Analyst

Understands the data, data cleansing, data transformation, analyzes outcomes, and presents the result in the form of reports and dashboards.

Junior Data Scientist

Uses advanced statistical techniques and tools to understand operating behavior and create algorithms with advanced prescriptive and descriptive methods.

Applied Scientist

Designs and develops various machine learning models to help in deriving intelligence for business products.

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Skills to Master

Data Science with R

Python for Data Science

Machine Learning

Artificial Intelligence

Spark

Power BI

Data Blending

Advanced Excel

MongoDB

T-SQL

Database objects

MS-SQL

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Tools to Master

R python jupyter numpy Scipy hadoop spark tensorflow Power-BI-1 excel mongodb hive mapreduce Oozie sqoop apache-pig pyspark SQL SparkSQL
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Course Fees

Self Paced Training

  • 104 Hrs e-learning videos
  • Flexible Schedule
  • Lifetime Free Upgrade

$702

Online Classroom Preferred

  • Everything in Self-paced Learning
  • 232 Hrs of Instructor-led Training
  • One to one doubt resolution sessions
  • Attend as many batches as you want for lifetime
  • Job Assistance
Weekday (Tue-Fri)

19 Nov 2024 07:00 AM - 09:00 AM
Weekend (Sat-Sun)

23 Nov 2024 08:00 PM - 11:00 PM
Weekday (Tue-Fri)

26 Nov 2024 07:00 AM - 09:00 AM
Weekend (Sat-Sun)

01 Dec 2024 08:00 PM - 11:00 PM
$ 1499 $1,099 10% OFF Expires in

Corporate Training

  • Customized Learning
  • Enterprise Grade Learning Management System (LMS)
  • 24x7 Support
  • Enterprise Grade Reporting

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Master's in Data Science Course Curriculum

Live Course Self-Paced

Python for Data Science

39 Hours 14 Module

Preview

Module 01 – Introduction to Data Science using Python
Module 02 – Python basic constructs
Module 03 – Maths for DS-Statistics and Probability
Module 04 – OOPs in Python (Self-paced)
Module 05 – NumPy for mathematical computing
Module 06 – SciPy for scientific computing (Self-paced)
Module 07 – Data manipulation
Module 08 – Data visualization with Matplotlib
Module 09 – Machine Learning using Python
Module 10 – Supervised learning
Module 11 – Unsupervised Learning
Module 12 – Python integration with Spark (Self-paced)
Module 13 – Dimensionality Reduction (Self-paced)
Module 14 – Time Series Forecasting (Self-paced)

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Tools covered

python jupyter numpy Scipy

Module 01 – Introduction to Machine Learning
Module 02 – Supervised Learning and Linear Regression
Module 03 – Classification and Logistic Regression
Module 04 – Decision Tree and Random Forest
Module 05 – Naïve Bayes and Support Vector Machine (Self-paced)
Module 06 – Unsupervised Learning
Module 07 – Natural Language Processing and Text Mining (Self-paced)
Module 08 – Introduction to Deep Learning
Module 09 – Time Series Analysis (self-paced)

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Tools covered

python jupyter

Module 01 – Introduction to Deep Learning and Neural Networks
Module 02 – Multi-layered Neural Networks
Module 03 – Artificial Neural Networks and Various Methods
Module 04 – Deep Learning Libraries
Module 05 – Keras API
Module 06 – TFLearn API for TensorFlow
Module 07 – DNNS (deep neural networks)
Module 08 – CNNS (convolutional neural networks)
Module 09 – RNNS (recurrent neural networks)
Module 10 – GPU in deep learning
Module 11 – Autoencoders and restricted boltzmann machine (rbm)
Module 12 – Deep learning applications
Module 13 – Chatbots

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Tools covered

tensorflow

Module 01 – Introduction to Power BI
Module 02 – Data Extraction
Module 03 – Data Transformation – Shaping and Combining Data
Module 04 – Data Modelling and DAX
Module 05 – Data Visualization with analytics
Module 06 – Power BI Service (Cloud), Q&A, and Data Insights
Module 07 – Power BI Settings, Administration & Direct Connectivity
Module 08 – Embedded Power BI with API & Power BI
Module 09 – Power BI Advance & Power BI Premium

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Tools covered

Power-BI-1

Module 01 – Introduction to Data Science with R
Module 02 – Data Exploration
Module 03 – Data Manipulation
Module 04 – Data Visualization
Module 05 – Introduction to Statistics
Module 06 – Machine Learning
Module 07 – Logistic Regression
Module 08 – Decision Trees and Random Forest
Module 09 – Unsupervised Learning
Module 10 – Association Rule Mining and Recommendation Engines

Self-paced Course Content

Module 11 – Introduction to Artificial Intelligence
Module 12 – Time Series Analysis
Module 13 – Support Vector Machine (SVM)
Module 14 – Naïve Bayes
Module 15 – Text Mining

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Tools covered

R

Module 01 – Entering Data
Module 02 – Referencing in Formulas
Module 03 – Name Range
Module 04 – Understanding Logical Functions
Module 05 – Getting started with Conditional Formatting
Module 06 – Advanced-level Validation
Module 07 – Important Formulas in Excel
Module 08 – Working with Dynamic table
Module 09 – Data Sorting
Module 10 – Data Filtering
Module 11 – Chart Creation
Module 12 – Various Techniques of Charting
Module 13 – Pivot Tables in Excel
Module 14 – Ensuring Data and File Security
Module 15 – Getting started with VBA Macros
Module 16 – Ranges and Worksheet in VBA
Module 17 – IF condition
Module 18 – Loops in VBA
Module 19 – Debugging in VBA
Module 20 – Dashboard Visualization
Module 21 – Principles of Charting
Module 22 – Getting started with Pivot Tables
Module 23 – Statistics with Excel

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Tools covered

excel

Module 01 – Introduction to NoSQL and MongoDB
Module 02 – MongoDB Installation
Module 03 – Importance of NoSQL
Module 04 – CRUD Operations
Module 05 – Data Modeling and Schema Design
Module 06 – Data Management and Administration
Module 07 – Data Indexing and Aggregation
Module 08 – MongoDB Security
Module 09 – Working with Unstructured Data

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Tools covered

mongodb

Module 01 – Introduction to SQL
Module 02 – Database Normalization and Entity Relationship Model
Module 03 – SQL Operators
Module 04 – Working with SQL: Join, Tables, and Variables
Module 05 – Deep Dive into SQL Functions
Module 06 – Working with Subqueries
Module 07 – SQL Views, Functions, and Stored Procedures
Module 08 – Deep Dive into User-defined Functions
Module 09 – SQL Optimization and Performance
Module 10 – Advanced Topics
Module 11 – Managing Database Concurrency
Module 12 – Programming Databases Using Transact-SQL
Module 13 – Microsoft Courses: Study Material

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Tools covered

SQL
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Project Work

Projects will be a part of your Data Science Architect Master’s program to consolidate your learning. It will ensure that you have real-world experience in Data Science.

Career Services

Career Services
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Assured Interviews
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Exclusive access to Intellipaat Job portal
Mock Interview Preparation
1 on 1 Career Mentoring Sessions
resume
Career Oriented Sessions
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Resume & LinkedIn Profile Building
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Master's in Data Science in the UK Certificates

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This Data Scientist master’s program in the UK is created and delivered to help you land top jobs in the world’s best organizations. The training includes real-world projects and case studies after the completion of which, you will receive industry-recognized certifications from Intellipaat.

This training program aims to prepare you for the following certification exams:

  • Spark component of Cloudera Spark and Hadoop Developer Certification (CCA175)
  • Tableau Desktop Qualified Associate Exam
  • C100DEV: MongoDB Certified Developer Associate Exam
  • Microsoft 70-761 SQL Server Certification Exam
  • Microsoft 70-762 SQL Server Certification Exam

MS in Data Science in the UK Reviews

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Data Science Master’s Program in the UK FAQs

How is Intellipaat’s MS in Data Science in the UK different from its individual courses?

Intellipaat’s Master’s program in Data Science in the UK is specially designed by industry experts, ensuring that you become a Data Science expert through a holistic learning method. Individual courses, on the other hand, are mainly for achieving specializations in particular technologies. If you want to master the whole data science domain, then this program is for you.

Intellipaat has been serving data science aspirants everywhere in the UK. You can learn data science from anywhere, regardless of your location—be it England, Northern Ireland, Scotland, Wales, or any other place in the UK.

Intellipaat offers query resolution, and you can raise a ticket with the dedicated support team at any time. You can avail yourself of email support for all your queries. We can also arrange one-on-one sessions with our support team If your query does not get resolved through email. However, 1:1 session support is given for 6 months from the start date of your course.

Intellipaat provides placement assistance to all learners who have completed the training and moved to the placement pool after clearing the PRT (Placement Readiness Test). More than 500+ top MNCs and startups hire Intellipaat learners. Our alumni work with Google, Microsoft, Amazon, Sony, Ericsson, TCS, Mu Sigma, etc.

No, our job assistance is aimed at helping you land your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final hiring decision will always be based on your performance in the interview and the requirements of the recruiter.

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