Data Science
Data is currently used in all industries, and Data-Science courses in the UK and Data-Analytics are widely recognised as vital industrial activity. The position of Data Scientist is quickly becoming a must-have for any firm that wants to fully utilize the data that they collect.
Call The Trainer
Batch Timing
- Regular: 2 Batches
- Weekends: 2 Batches
Request Call Back
Class Room & Online Training Quotation
About Data Science
Data is currently used in all industries, and Data-Science courses in the UK and Data-Analytics are widely recognized as vital industrial activity. The position of Data Scientist is quickly becoming a must-have for any firm that wants to fully utilize the data that they collect. This Data Science course in UK is meant to prepare you for a career as a Data Scientist in a variety of industries and enterprises.
The current state of affairs in Data science
In summary, Data science Training in UK is concerned with analyzing data in order to generate insights, and then using algorithms and machine learning to make informed judgments and predictions. In a world where we are all producing more data than ever before, it is critical that we understand and manage the ramifications of this data.
What is the global market worth of the data science industry?
There are various publications available to help us determine the global value of the data science sector. According to Grand View Research, the global data science classes in the UK market was worth $3.93 billion in 2019. According to Statista, the worldwide big data market is expected to be worth $1 trillion in 2022.
Highlights of the course
Investigate theoretical and practical issues using industry-recognized abilities.
Learn about a course that is one-of-a-kind in its combination of machine learning, visual analytics, and corporate data governance.
Prepare to use machine learning and visual analytics on any data source.
Learn how to properly evaluate and understand data.
The unique Data Science Course course in UK will help you advance your career in data science. Experience world-class Data Science training in the UK from a thought leader in the field on the most in-demand Data Science skills. Learn crucial technologies such as Python, Machine Learning, Data Visualization, SQL, and Artificial Intelligence firsthand. Learn how to become a Data Science specialist now.
The PGP-DS (Post Graduate Program in Data Science) covers a wide range of concepts and methodologies, including Python, exploratory data analysis, machine learning, deep learning, and others. Practical laboratories and assignment work bring these concepts to life, with our professors and assistants on hand to guide you along the way.
Equip your profession with this well regarded PG Program in Data Science.
You will have a solid understanding of :
Analytics tools and technologies such as Python, Tableau, and SQL.
Use machine learning techniques applicable to the sector, such as regression, predictive modeling, clustering, time series forecasting, classification, and so on.
Create an analytics framework for a business challenge utilizing statistics and data modeling.
Use a variety of tools and strategies to perform data cleaning and transformation tasks.
Be well-versed in Deep Learning and Natural Language Processing (NLP).
Present yourself as a strong candidate for analyst, data engineer, and data scientist positions at premier analytics firms.
Data Science
This curriculum, which is part of the computer and informatics discipline, teaches students how to extract useful information from massive amounts of data. International students wishing to pursue a master's degree in data science in the United Kingdom must have at least a 60% average in their bachelor's degree. You must also have IELTS (6.0 or higher) or equivalent English language proficiency levels. LORs are the primary supporting documentation that must be included in your application package. With a bachelor's degree in data science, you can expect to earn roughly 116,000 GBP per year.
Why Study Data Science in the United Kingdom?
As the volume of data collected by various organizations grows, so does the demand for data science graduates, particularly in the United Kingdom, as illustrated by the Royal Society's Dynamics of Data Science Skills report. Because of the increased demand, data science has become one of the most sought-after master's degrees in the United Kingdom. In the United Kingdom, the demand for Data Scientists and Data Engineers has surged by 231% in the last five years. A total of 13 million GBP in funding has been announced for AI, data analytics, and related initiatives.
A 45 million GBP investment in 200 Ph.D. programmes in AI and allied subjects suggests that the breadth of Ph.D. in AI in the UK is expanding.
What is an MS in Data Science?
A master's degree in data science teaches students how to analyze enormous amounts of data and extract valuable statistics from it. This programme is likewise concerned with putting the extracted data to use for the benefit of the company or client. Students pursuing a master's degree in data science classes in the United Kingdom gain in-depth knowledge and skills in Computational data analysis, Machine learning, Statistical principles of data analysis, Insightful large-scale data analysis, Understanding ethical issues in the application of data science techniques, and Initiating independent data science projects.
Online Classes
It is a good opportunity for students to get admission in training institutes to some of the best courses. Technology is booming and any of it can enhance students' career prospects and skills. Online Data Science Course in UK is in high demand and is highly recommended. SevenMentor & Training Pvt. Ltd. is an institute where students can learn different trending technology topics. Students can easily be successful in their career and further growth in their lifetime. Our trainers fully focus on students' performance. Our full support can encourage students to get placed in any of the best companies.
Course Eligibility
- Freshers
- BE/ Bsc Candidate
- Any Engineers
- Any Graduate
- Any Post-Graduate
- Working Professionals
Syllabus of Data Science
- 1. Installation Of Vmware
- 2. MYSQL Database
- 3. Core Java
- 1.1 Types of Variable
- 1.2 Types of Datatype
- 1.3 Types of Modifiers
- 1.4 Types of constructors
- 1.5 Introduction to OOPS concept
- 1.6 Types of OOPS concept
- 4. Advance Java
- 1.1 Introduction to Java Server Pages
- 1.2 Introduction to Servlet
- 1.3 Introduction to Java Database Connectivity
- 1.4 How to create Login Page
- 1.5 How to create Register Page
- 5. Bigdata
- 1.1 Introduction to Big Data
- 1.2 Characteristics of Big Data
- 1.3 Big data examples
- 6. Hadoop
- i) BigData Inroduction,Hadoop Introduction and HDFS Introduction
- 1.1. Hadoop Architecture
- 1.2. Installing Ubuntu with Java on VM Workstation 11
- 1.3. Hadoop Versioning and Configuration
- 1.4. Single Node Hadoop installation on Ubuntu
- 1.5. Multi Node Hadoop installation on Ubuntu
- 1.6. Hadoop commands
- Cluster architecture and block placement
- 1.8. Modes in Hadoop
- Local Mode
- Pseudo Distributed Mode
- Fully Distributed Mode
- 1.9. Hadoop components
- Master components(Name Node, Secondary Name Node, Job Tracker)
- Slave components(Job tracker, Task tracker)
- 1.10. Task Instance
- 1.11. Hadoop HDFS Commands
- 1.12. HDFS Access
- Java Approach
- ii) MapReduce Introduction
- 1.1 Understanding Map Reduce Framework
- 1.2 What is MapReduceBase?
- 1.3 Mapper Class and its Methods
- 1.4 What is Partitioner and types
- 1.5 Relationship between Input Splits and HDFS Blocks
- 1.6 MapReduce: Combiner & Partitioner
- 1.7 Hadoop specific Data types
- 1.8 Working on Unstructured Data Analytics
- 1.9 Types of Mappers and Reducers
- 1.10 WordCount Example
- 1.11 Developing Map-Reduce Program using Eclipse
- 1.12 Analysing dataset using Map-Reduce
- 11.13 Running Map-Reduce in Local Mode.
- 1.14 MapReduce Internals -1 (In Detail) :
- How MapReduce Works
- Anatomy of MapReduce Job (MR-1)
- Submission & Initialization of MapReduce Job (What Happen ?)
- Assigning & Execution of Tasks
- Monitoring & Progress of MapReduce Job
- Completion of Job
- Handling of MapReduce Job
- Task Failure
- TaskTracker Failure
- JobTracker Failure
- 1.15 Advanced Topic for MapReduce (Performance and Optimization) :
- Job Sceduling
- In Depth Shuffle and Sorting
- 1.16 Speculative Execution
- 1.17 Output Committers
- 1.18 JVM Reuse in MR1
- 1.19 Configuration and Performance Tuning
- 1.20 Advanced MapReduce Algorithm :
- 1.21 File Based Data Structure
- Sequence File
- MapFile
- 1.22 Default Sorting In MapReduce
- Data Filtering (Map-only jobs)
- Partial Sorting
- 1.23 Data Lookup Stratgies
- In MapFiles
- 1.24 Sorting Algorithm
- Total Sort (Globally Sorted Data)
- InputSampler
- Secondary Sort
- 1.25 MapReduce DataTypes and Formats :
- 1.26 Serialization In Hadoop
- 1.27 Hadoop Writable and Comparable
- 1.28 Hadoop RawComparator and Custom Writable
- 1.29 MapReduce Types and Formats
- 1.30 Understand Difference Between Block and InputSplit
- 1.31 Role of RecordReader
- 1.32 FileInputFormat
- 1.33 ComineFileInputFormat and Processing whole file Single Mapper
- 1.34 Each input File as a record
- 1.35 Text/KeyValue/NLine InputFormat
- 1.36 BinaryInput processing
- 1.37 MultipleInputs Format
- 1.38 DatabaseInput and Output
- 1.39 Text/Biinary/Multiple/Lazy OutputFormat MapReduce Types
- iii)TOOLS:
- 1.1 Apache Sqoop
- Sqoop Tutorial
- How does Sqoop Work
- Sqoop JDBCDriver and Connectors
- Sqoop Importing Data
- Various Options to Import Data
- Table Import
- Binary Data Import
- SpeedUp the Import
- Filtering Import
- Full DataBase Import Introduction to Sqoope
- 1.2 Apache Hive
- 1.2 Apache Hive
- What is Hive ?
- Architecture of Hive
- Hive Services
- Hive Clients
- How Hive Differs from Traditional RDBMS
- Introduction to HiveQL
- Data Types and File Formats in Hive
- File Encoding
- Common problems while working with Hive
- Introduction to HiveQL
- Managed and External Tables
- Understand Storage Formats
- Querying Data
- 1.3 Apache Pig :
- What is Pig ?
- Introduction to Pig Data Flow Engine
- Pig and MapReduce in Detail
- When should Pig Used ?
- Pig and Hadoop Cluster
- Pig Interpreter and MapReduce
- Pig Relations and Data Types
- PigLatin Example in Detail
- Debugging and Generating Example in Apache Pig
- 1.4 HBase:
- Fundamentals of HBase
- Usage Scenerio of HBase
- Use of HBase in Search Engine
- HBase DataModel
- Table and Row
- Column Family and Column Qualifier
- Cell and its Versioning
- Regions and Region Server
- HBase Designing Tables
- HBase Data Coordinates
- Versions and HBase Operation
- Get/Scan
- Put
- Delete
- 1.5 Apache Flume:
- Flume Architecture
- Installation of Flume
- Apache Flume Dataflow
- Apache Flume Environment
- Fetching Twitter Data
- 1.6 Apache Kafka:
- Introduction to Kafka
- Cluster Architecture
- Installation of kafka
- Work Flow
- Basic Operations
- Real time application(Twitter)
- 4)HADOOP ADMIN:
- Introduction to Big Data and Hadoop
- Types Of Data
- Characteristics Of Big Data
- Hadoop And Traditional Rdbms
- Hadoop Core Services
- Hadoop single node cluster(HADOOP-1.2.1)
- Tools installation for hadoop1x.
- Sqoop,Hive,Pig,Hbase,Zookeeper.
- Analyze the cluster using
- a)NameNode UI
- b)JobTracker UI
- SettingUp Replication Factor
- Hadoop Distributed File System:
- Introduction to Hadoop Distributed File System
- Goals of HDFS
- HDFS Architecture
- Design of HDFS
- Hadoop Storage Mechanism
- Measures of Capacity Execution
- HDFS Commands
- The MapReduce Framework:
- Understanding MapReduce
- The Map and Reduce Phase
- WordCount in MapReduce
- Running MapReduce Job
- WordCount in MapReduce
- Running MapReduce Job
- Hadoop single node Cluster
- Hadoop single node Cluster Setup :
- Hadoop single node cluster(HADOOP-2.7.3)
- Tools installation for hadoop2x
- Sqoop,Hive,Pig,Hbase,Zookeeper
- Hadoop single node Cluster Setup :
- Hadoop single node cluster(HADOOP-2.7.3)
- Tools installation for hadoop2x
- Sqoop,Hive,Pig,Hbase,Zookeeper.
- Yarn:
- Introduction to YARN
- Need for YARN
- YARN Architecture
- YARN Installation and Configuration
- Hadoop Multinode cluster setup:
- hadoop multinode cluster
- Checking HDFS Status
- Breaking the cluster
- Copying Data Between Clusters
- Adding and Removing Cluster Node
- Name Node Metadata Backup
- Cluster Upgrading
- Hadoop ecosystem:
- Sqoop
- Hive
- Pig
- HBase
- zookeeper
- >7. MONGODB
- 8. SCALA
- 1.1 Introduction to scala
- 1.2 Programming writing Modes i.e. Interactive Mode,Script Mode
- 1.3 Types of Variable
- 1.4 Types of Datatype
- 1.5 Function Declaration
- 1.6 OOPS concepts
- 9. APACHE SPARK
- 1.1 Introduction to Spark
- 1.2 Spark Installation
- 1.3 Spark Architecture
- 1.4 Spark SQL
- Dataframes: RDDs + Tables
- Dataframes and Spark SQL
- 1.5 Spark Streaming
- Introduction to streaming
- Implement stream processing in Spark using Dstreams
- Stateful transformations using sliding windows
- 1.6 Introduction to Machine Learning
- 1.7 Introduction to Graphx
- Hadoop ecosystem:
- Sqoop
- Hive
- Pig
- HBase
- zookeeper
- 10. TABLEAU
- 11. DATAIKU
- 12. Product Based Web Application Demo based on java(EcommerceApplication)
- 13. Data deduplication Project
- 14. PYTHON
- 1.Introduction to Python
- What is Python and history of Python?
- Unique features of Python
- Python-2 and Python-3 differences
- Install Python and Environment Setup
- First Python Program
- Python Identifiers, Keywords and Indentation
- Comments and document interlude in Python
- Command line arguments
- Getting User Input
- Python Data Types
- What are variables?
- Python Core objects and Functions
- Number and Maths
- Week 1 Assignments
- 2.List, Ranges & Tuples in Python
- Introduction
- Lists in Python
- More About Lists
- Understanding Iterators
- Generators , Comprehensions and Lambda Expressions
- Introduction
- Generators and Yield
- Next and Ranges
- Understanding and using Ranges
- More About Ranges
- Ordered Sets with tuples
- 3.Python Dictionaries and Sets
- Introduction to the section
- Python Dictionaries
- More on Dictionaries
- Sets
- Python Sets Examples
- 4. Python built in function
- Python user defined functions
- Python packages functions
- Defining and calling Function
- The anonymous Functions
- Loops and statement in Python
- Python Modules & Packages
- 5.Python Object Oriented
- Overview of OOP
- Creating Classes and Objects
- Accessing attributes
- Built-In Class Attributes
- Destroying Objects
- 6. Python Object Oriented
- Overview of OOP
- Creating Classes and Objects
- Accessing attributes
- Built-In Class Attributes
- Destroying Objects
- 7. Python Exceptions Handling
- What is Exception?
- Handling an exception
- try….except…else
- try-finally clause
- Argument of an Exception
- Python Standard Exceptions
- Raising an exceptions
- User-Defined Exceptions
- 8. Python Regular Expressions
- What are regular expressions?
- The match Function
- The search Function
- Matching vs searching
- Search and Replace
- Extended Regular Expressions
- Wildcard
- 9. Python Multithreaded Programming
- What is multithreading?
- Starting a New Thread
- The Threading Module
- Synchronizing Threads
- Multithreaded Priority Queue
- Python Spreadsheet Interfaces
- Python XML interfaces
- 10. Using Databases in Python
- Python MySQL Database Access
- Install the MySQLdb and other Packages
- Create Database Connection
- CREATE, INSERT, READ, UPDATE and DELETE Operation
- DML and DDL Oepration with Databases
- Performing Transactions
- Handling Database Errors
- Web Scraping in Python
- 11.Python For Data Analysis –
- Numpy:
- Introduction to numpy
- Creating arrays
- Using arrays and Scalars
- Indexing Arrays
- Array Transposition
- Universal Array Function
- Array Processing
- Arrary Input and Output
- 12. Pandas:
- What is pandas?
- Where it is used?
- Series in pandas
- Index objects
- Reindex
- Drop Entry
- Selecting Entries
- Data Alignment
- Rank and Sort
- Summary Statics
- Missing Data
- Index Heirarchy
- 13. Matplotlib: Python For Data Visualization
- 14. Welcome to the Data Visualiztion Section
- 15. Introduction to Matplotlib
- 16. Django Web Framework in Python
- 17. Introduction to Django and Full Stack Web Development
- 15. R Programming
- 1.1 Introduction to R
- 1.2 Installation of R
- 1.3 Types of Datatype
- 1.4 Types of Variables
- 1.5 Types of Operators
- 1.6 Types of Loops
- 1.7 Function Declaration
- 1.8 R Data Interface
- 1.9 R Charts and Graphs
- 1.10 R statistics
- 16) Advance Tool for Analysis
- 1.1 git
- 1.2 nmpy
- 1.3 scipy
- 1.4 github
- 1.5 matplotlib
- 1.6 Pandas
- 1.7 PyQT
- 1.8Theano
- 1.9 Tkinter
- 1.10 Scikit-learn
- 1.11 NPL
- 17. Algorithm
- 1.naive bayes
- 2.Linear Regression
- 3.K-nn
- 4.C-nn
Trainer Profile of Data Science
Our Trainers explains concepts in very basic and easy to understand language, so the students can learn in a very effective way. We provide students, complete freedom to explore the subject. We teach you concepts based on real-time examples. Our trainers help the candidates in completing their projects and even prepare them for interview questions and answers. Candidates can learn in our one to one coaching sessions and are free to ask any questions at any time.
- Certified Professionals with more than 8+ Years of Experience
- Trained more than 2000+ students in a year
- Strong Theoretical & Practical Knowledge in their domains
- Expert level Subject Knowledge and fully up-to-date on real-world industry applications
Data Science Exams & Certification
SevenMentor Certification is Accredited by all major Global Companies around the world. We provide after completion of the theoretical and practical sessions to fresher’s as well as corporate trainees.
Our certification at SevenMentor is accredited worldwide. It increases the value of your resume and you can attain leading job posts with the help of this certification in leading MNC’s of the world. The certification is only provided after successful completion of our training and practical based projects.
Proficiency After Training
- Learn all new aspects of Data Science.
- You will have a good understanding of Data Science Algorithms.
- You will be able to work on real time projects.
- You will be able to work on file formats on different data.
Key Features
Skill Level
Beginner, Intermediate, Advance
We are providing Training to the needs from Beginners level to Experts level.
Course Duration
90 Hours
Course will be 90 hrs to 110 hrs duration with real-time projects and covers both teaching and practical sessions.
Total Learners
2000+ Learners
We have already finished 100+ Batches with 100% course completion record.
Frequently Asked Questions
Batch Schedule
DATE | COURSE | TRAINING TYPE | BATCH | CITY | REGISTER |
---|---|---|---|---|---|
18/11/2024 |
Data Science |
Online | Regular Batch (Mon-Sat) | UK | Book Now |
19/11/2024 |
Data Science |
Online | Regular Batch (Mon-Sat) | UK | Book Now |
16/11/2024 |
Data Science |
Online | Weekend Batch (Sat-Sun) | UK | Book Now |
16/11/2024 |
Data Science |
Online | Weekend Batch (Sat-Sun) | UK | Book Now |
Students Reviews
The vast majority of people were awake and having a fantastic time. The planetarium was amazing! The man who narrated it was incredible! Such a pleasant, peaceful tone to your voice. I initially assumed it was pre-recorded.
- Parekh Jadhav
There are some intriguing old and recent structures. The website contains a walking tour map if you want to view the main sights. Bring water if you're doing this in the summer because it's hot and the campus is large!
- Apurva Ghodke
What a wonderful institute! My son is pursuing his PHD here. He is enamored with it! It's a small but gorgeous campus teeming with interesting people.
- Ajay Khurana
Course video & Images
Corporate Training
Employees can upgrade their skill set by taking a Corporate Data Science course in UK with SevenMentor & Training Pvt. Ltd.. They can increase their performance in their current jobs by honing their practical skills and expanding their knowledge. A company's productivity is essential. Skilled personnel can increase a company's productivity. Our lecturers are subject matter experts in a variety of fields. Employee training can gain from this and improve your work growth.
Our Placement Process
Eligibility Criteria
Placements Training
Interview Q & A
Resume Preparation
Aptitude Test
Mock Interviews
Scheduling Interviews
Job Placement
Related Courses
Have a look at all our related courses to learn from any location
Data Analysis where dealing with unstructured and structured data, Data Science is a field that encompasses anything related to data cleansing, preparation, and analysis. Put simply, Data Science is an...
R is an open-source programming language .That facilitates statistical computing for data preprocessing and graphical libraries for visualization. Being open-source,in R, there's a comprehensive environment that facilitates the performance of...
In Python, there is a comprehensive environment that facilitates the performance of statistical operations as well as the generation of data analysis in graphical or text format.
Request For Call Back
Class Room & Online Training Quotation