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DATA SCIENCE COURSE FEE IN LIVERPOOL, UK

Live Virtual

Instructor Led Live Online

1,650
1,029

  • IABAC® & NASSCOM® Certification
  • 8-Month | 700 Learning Hours
  • 120-Hour Live Online Training
  • 25 Capstone & 1 Client Project
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Blended Learning

Self Learning + Live Mentoring

990
629

  • Self Learning + Live Mentoring
  • IABAC® & NASSCOM® Certification
  • 1 Year Access To Elearning
  • 25 Capstone & 1 Client Project
  • Job Assistance
  • 24*7 Leaner assistance and support

Corporate Training

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  • Instructor-Led & Self-Paced training
  • Customized Learning Options
  • Industry Expert Trainers
  • Case Study Approach
  • Enterprise Grade Learning
  • 24*7 Cloud Lab

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UPCOMING DATA SCIENCE TRAINING SCHEDULES IN LIVERPOOL

BEST DATA SCIENCE CERTIFICATIONS

The entire training includes real-world projects and highly valuable case studies.

IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.

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WHY DATAMITES INSTITUTE FOR DATA SCIENCE COURSE

Why DataMites Infographic

SYLLABUS OF DATA SCIENCE COURSE IN LIVERPOOL

MODULE 1: DATA SCIENCE COURSE INTRODUCTION 

  • CDS Course Introduction
  • 3 Phase Learning
  • Learning Resources
  • Assessments & Certification Exams
  • DataMites Mobile App
  • Support Channels

MODULE 2: DATA SCIENCE ESSENTIALS 

  • Introduction to Data Science
  • Evolution of Data Science
  • Data Science Terminologies
  • Data Science vs AI/Machine Learning
  • Data Science vs Analytics

MODULE 3: DATA SCIENCE DEMO 

  • Business Requirement: Use Case
  • Data Preparation
  • Machine learning Model building
  • Prediction with ML model
  • Delivering Business Value

MODULE 4: ANALYTICS CLASSIFICATION 

  • Types of Analytics
  • Diagnostic Analytics
  • Predictive Analytics
  • Prescriptive Analytics

MODULE 5: DATA SCIENCE AND RELATED FIELDS

  • Introduction to AI
  • Introduction to Computer Vision
  • Introduction to Natural Language Processing
  • Introduction to Reinforcement Learning
  • Introduction to GAN
  • Introduction to  Generative Passive Models

MODULE 6: DATA SCIENCE ROLES & WORKFLOW

  • Data Science Project workflow
  • Roles: Data Engineer, Data Scientist, ML Engineer and MLOps Engineer
  • Data Science Project stages

MODULE 7: MACHINE LEARNING INTRODUCTION

  • What Is ML? ML Vs AI
  • ML Workflow, Popular ML Algorithms
  • Clustering, Classification And Regression
  • Supervised Vs Unsupervised

MODULE 8: DATA SCIENCE INDUSTRY APPLICATIONS 

  • Data Science in Finance and Banking
  • Data Science in Retail
  • Data Science in Health Care
  • Data Science in Logistics and Supply Chain
  • Data Science in Technology Industry
  • Data Science in Manufacturing
  • Data Science in Agriculture

MODULE 1: PYTHON BASICS 

  • Introduction of python
  • Installation of Python and IDE
  • Python objects
  • Python basic data types
  • Number & Booleans, strings
  • Arithmetic Operators
  • Comparison Operators
  • Assignment Operators
  • Operator’s precedence and associativity

MODULE 2: PYTHON CONTROL STATEMENTS 

  • IF Conditional statement
  • IF-ELSE • NESTED IF
  • Python Loops basics
  • WHILE Statement
  • FOR statements
  • BREAK and CONTINUE statements

MODULE 3: PYTHON DATA STRUCTURES 

  • Basic data structure in python
  • String object basics and inbuilt methods
  • List: Object, methods, comprehensions
  • Tuple: Object, methods, comprehensions
  • Sets: Object, methods, comprehensions
  • Dictionary: Object, methods, comprehensions

MODULE 4: PYTHON FUNCTIONS 

  • Functions basics
  • Function Parameter passing
  • Iterators
  • Generator functions
  • Lambda functions
  • Map, reduce, filter functions

MODULE 5: PYTHON NUMPY PACKAGE 

  • NumPy Introduction
  • Array – Data Structure
  • Core Numpy functions
  • Matrix Operations

MODULE 6: PYTHON PANDASPACKAGE

  • Pandasfunctions
  • Data Frame and Series – Data Structure
  • Data munging with Pandas
  • Imputation and outlier analysis

 

MODULE 1: OVERVIEW OF STATISTICS 

  • Descriptive And Inferential Statistics
  • Basic Terms Of Statistics
  • Types Of Data

MODULE 2: HARNESSING DATA 

  • Random Sampling
  • Sampling With Replacement And Without Replacement
  • Cochran's  Minimum Sample Size
  • Simple Random Sampling
  • Stratified Random Sampling
  • Cluster Random Sampling
  • Systematic Random Sampling
  • Biased Random Sampling Methods
  • Sampling Error
  • Methods Of Collecting Data

MODULE 3: EXPLORATORY DATA ANALYSIS 

  • Exploratory Data Analysis Introduction
  • Measures Of Central Tendencies: Mean, Median And Mode
  • Measures Of Central Tendencies: Range, Variance And Standard Deviation
  • Data Distribution Plot: Histogram
  • Normal Distribution
  • Z Value / Standard Value
  • Empherical Rule  and Outliers
  • Central Limit Theorem
  • Normality Testing
  • Skewness & Kurtosis
  • Measures Of Distance: Euclidean, Manhattan And MinkowskiDistance

MODULE 4: HYPOTHESIS TESTING 

  • Hypothesis Testing Introduction
  • P- Value, Confidence Interval
  • Parametric Hypothesis Testing Methods
  • Hypothesis Testing Errors : Type I And Type Ii
  • One Sample T-test
  • Two Sample Independent T-test
  • Two Sample Relation T-test
  • One Way Anova Test

MODULE 5: CORRELATION AND REGRESSION 

  • Correlation Introduction
  • Direct/Positive Correlation
  • Indirect/Negative Correlation
  • Regression
  • Choosing Right Method

 

MODULE 1: MACHINE LEARNING INTRODUCTION 

  • What Is ML? ML Vs AI
  • ML Workflow, Popular ML Algorithms
  • Clustering, Classification And Regression
  • Supervised Vs Unsupervised

MODULE 2: PYTHON NUMPY & PANDAS PACKAGE 

  • NumPy & Pandas functions
  • Array – Data Structure
  • Core Numpy functions
  • Matrix Operations
  • Data Frame and Series – Data Structure
  • Data munging with Pandas
  • Imputation and outlier analysis

MODULE 3: VISUALIZATION WITH PYTHON 

  • Visualization Packages (Matplotlib)
  • Components Of A Plot, Sub-Plots
  • Basic Plots: Line, Bar, Pie, Scatter
  • Advanced Python Data Visualizations

MODULE 4: ML ALGO: LINEAR REGRESSION

  • Introduction to Linear Regression
  • How it works: Regression and Best Fit Line
  • Modeling and Evaluation in Python

MODULE 5: ML ALGO: KNN 

  • Introduction to KNN
  • How It Works: Nearest Neighbor Concept
  • Modeling and Evaluation in Python

MODULE 6: ML ALGO: LOGISTIC REGRESSION 

  • Introduction to Logistic Regression
  • How it works: Classification & Sigmoid Curve
  • Modeling and Evaluation in Python

MODULE 7: PRINCIPLE COMPONENT ANALYSIS (PCA) 

  • Building Blocks Of PCA
  • How it works: Finding Principal Components
  • Modeling PCA in Python

MODULE 8: ML ALGO: K MEANS CLUSTERING 

  • Understanding Clustering (Unsupervised)
  • K Means Algorithm
  • How it works: K Means theory
  • Modeling in Python

MODULE 1: MACHINE LEARNING INTRODUCTION 

  • What Is ML? ML Vs AI
  • ML Workflow, Popular ML Algorithms
  • Clustering, Classification And Regression
  • Supervised Vs Unsupervised

MODULE 2: ML ALGO: LINEAR REGRESSSION 

  • Introduction to Linear Regression
  • How it works: Regression and Best Fit Line
  • Modeling and Evaluation in Python

MODULE 3: ML ALGO: LOGISTIC REGRESSION 

  • Introduction to Logistic Regression
  • How it works: Classification & Sigmoid Curve
  • Modeling and Evaluation in Python

MODULE 4: ML ALGO: KNN 

  • Introduction to KNN
  • How It Works: Nearest Neighbor Concept
  • Modeling and Evaluation in Python

MODULE 5: ML ALGO: K MEANS CLUSTERING 

  • Understanding Clustering (Unsupervised)
  • K Means Algorithm
  • How it works : K Means theory
  • Modeling in Python

MODULE 6: PRINCIPLE COMPONENT ANALYSIS (PCA) 

  • Building Blocks Of PCA
  • How it works: Finding Principal Components
  • Modeling PCA in Python

MODULE 7: ML ALGO: DECISION TREE 

  • Random Forest Ensemble technique
  • How it works: Bagging Theory
  • Modeling and Evaluation in Python

MODULE 8 : ML ALGO: NAÏVE BAYES 

  • Introduction to Naive Bayes
  • How it works: Bayes' Theorem
  • Naive Bayes For Text Classification
  • Modeling and Evaluation in Python

MODULE 9: GRADIENT BOOSTING, XGBOOST 

  • Introduction to Boosting and XGBoost
  • How it works: weak learners' concept
  • Modeling and Evaluation of in Python

MODULE 10: ML ALGO: SUPPORT VECTOR MACHINE  (SVM) 

  • Introduction to SVM
  • How It Works: SVM Concept, Kernel Trick
  • Modeling and Evaluation of SVM in Python

MODULE 11: ARTIFICIAL NEURAL NETWORK (ANN) 

  • Introduction to ANN
  • How It Works: Back prop, Gradient Descent
  • Modeling and Evaluation of ANN in Python

MODULE 12: ADVANCED ML CONCEPTS 

  • Adv Metrics (Roc_Auc, R2, Precision, Recall)
  • K-Fold Cross-validation
  • Grid And Randomized Search CV In Sklearn
  • Imbalanced Data Set: Smote Technique
  • Feature Selection Techniques

MODULE 1: TIME SERIES FORECASTING - ARIMA 

  • What is Time Series?
  • Trend, Seasonality, cyclical and random
  • Autoregressive Model (AR)
  • Moving Average Model (MA)
  • Stationarity of Time Series
  • ARIMA Model
  • Autocorrelation and AIC 

MODULE 2: FEATURE ENGINEERING 

  • Introduction to Features Engineering
  • Transforming Predictors
  • Feature Selection methods
  • Backward elimination technique
  • Feature importance from ML modeling

MODULE 3: SENTIMENT ANALYSIS 

  • Introduction to Sentiment Analysis
  • Python packages: TextBlob, NLTK
  • Case study: Twitter Live Sentiment Analysis

MODULE 4: REGULAR EXPRESSIONS WITH PYTHON 

  • Regex Introduction
  • Regex codes
  • Text extraction with Python Regex

MODULE 5: ML MODEL DEPLOYMENT WITH FLASK

  • Introduction to Flask
  • URL and App routing
  • Flask application – ML Model deployment

MODULE 6: ADVANCED DATA ANALYSIS WITH MS EXCEL 

  • MS Excel core Functions
  • Pivot Table
  • Advanced Functions (VLOOKUP, INDIRECT..)
  • Linear Regression with EXCEL
  • Goal Seek Analysis
  • Data Table
  • Solving Data Equation with EXCEL
  • Monte Carlo Simulation with MS EXCEL

MODULE 7: AWS CLOUD FOR DATA SCIENCE

  • Introduction of cloud
  • Difference between GCC, Azure,AWS
  • AWS Service ( EC2 and S3 service)
  • AWS Service (AMI), AWS Service (RDS)
  • AWS Service (IAM), AWS (Athena service)
  • AWS (EMR), AWS, AWS (Redshift)
  • ML Modeling with AWS Sage Maker 

MODULE 8: AZURE FOR DATA SCIENCE 

  • Introduction to AZURE ML studio
  • Data Pipeline and ML modeling with Azure

MODULE 1: DATABASE INTRODUCTION 

  • DATABASE Overview
  • Key concepts of database management
  • CRUD Operations
  • Relational Database Management System
  • RDBMS vs No-SQL (Document DB)

MODULE 2: SQL BASICS 

  • Introduction to Databases
  • Introduction to SQL
  • SQL Commands
  • MY SQL  workbench installation
  • Comments
  • import and export dataset

MODULE 3: DATA TYPES AND CONSTRAINTS 

  • Numeric, Character, date time data type
  • Primary key, Foreign key, Not null
  • Unique, Check, default, Auto increment

MODULE 4: DATABASES AND TABLES (MySQL) 

  • Create database
  • Delete database
  • Show and use databases
  • Create table, Rename table
  • Delete table, Delete  table records
  • Create new table from existing data types
  • Insert into, Update records
  • Alter table

MODULE 5: SQL JOINS 

  • Inner join
  • Outer join
  • Left join
  • Right join
  • Cross join
  • Self join

MODULE 6: SQL COMMANDS AND CLAUSES 

  • Select, Select distinct
  • Aliases, Where clause
  • Relational operators, Logical
  • Between, Order by, In
  • Like, Limit, null/not null, group by
  • Having, Sub queries

MODULE 7 : DOCUMENT DB/NO-SQL DB 

  • Introduction of Document DB
  • Document DB vs SQL DB
  • Popular Document DBs
  • MongoDB basics
  • Data format and Key methods
  • MongoDB data management

MODULE 1: GIT  INTRODUCTION 

  • Purpose of Version Control
  • Popular Version control tools
  • Git Distribution Version Control
  • Terminologies
  • Git Workflow
  • Git Architecture

MODULE 2: GIT REPOSITORY and GitHub 

  • Git Repo Introduction
  • Create New Repo with Init command
  • Copying existing repo
  • Git user and remote node
  • Git Status and rebase
  • Review Repo History
  • GitHub Cloud Remote Repo

MODULE 3: COMMITS, PULL, FETCH AND PUSH 

  • Code commits
  • Pull, Fetch and conflicts resolution
  • Pushing to Remote Repo

MODULE 4: TAGGING, BRANCHING AND MERGING 

  • Organize code with branches
  • Checkout branch
  • Merge branches

MODULE 5: UNDOING CHANGES 

  • Editing Commits
  • Commit command Amend flag
  • Git reset and revert

MODULE 6: GIT WITH GITHUB AND BITBUCKET 

  • Creating GitHub Account
  • Local and Remote Repo
  • Collaborating with other developers
  • Bitbucket Git account

MODULE 1: BIG DATA INTRODUCTION 

  • Big Data Overview
  • Five Vs of Big Data
  • What is Big Data and Hadoop
  • Introduction to Hadoop
  • Components of Hadoop Ecosystem
  • Big Data Analytics Introduction

MODULE 2 : HDFS AND MAP REDUCE 

  • HDFS – Big Data Storage
  • Distributed Processing with Map Reduce
  • Mapping and reducing  stages concepts
  • Key Terms: Output Format, Partitioners, Combiners, Shuffle, and Sort
  • Hands-on Map Reduce task

MODULE 3: PYSPARK FOUNDATION 

  • PySpark Introduction
  • Spark Configuration
  • Resilient distributed datasets (RDD)
  • Working with RDDs in PySpark
  • Aggregating Data with Pair RDDs

MODULE 4: SPARK SQL and HADOOP HIVE 

  • Introducing Spark SQL
  • Spark SQL vs Hadoop Hive
  • Working with Spark SQL Query Language

MODULE 5 : MACHINE LEARNING WITH SPARK ML 

  • Introduction to MLlib Various ML algorithms supported by MLib
  • ML model with Spark ML
  • Linear regression
  • logistic regression
  • Random forest

MODULE 6: KAFKA and Spark 

  • Kafka architecture
  • Kafka workflow
  • Configuring Kafka cluster
  • Operations

MODULE 1: BUSINESS INTELLIGENCE INTRODUCTION 

  • What Is Business Intelligence (BI)?
  • What Bi Is The Core Of Business Decisions?
  • BI Evolution
  • Business Intelligence Vs Business Analytics
  • Data Driven Decisions With Bi Tools
  • The Crisp-Dm Methodology

MODULE 2: BI WITH TABLEAU: INTRODUCTION

  • The Tableau Interface
  • Tableau Workbook, Sheets And Dashboards
  • Filter Shelf, Rows And Columns
  • Dimensions And Measures
  • Distributing And Publishing

MODULE 3 : TABLEAU: CONNECTING TO DATA SOURCE 

  • Connecting To Data File , Database Servers
  • Managing Fields
  • Managing Extracts
  • Saving And Publishing Data Sources
  • Data Prep With Text And Excel Files
  • Join Types With Union
  • Cross-Database Joins
  • Data Blending
  • Connecting To Pdfs

MODULE 4: TABLEAU : BUSINESS INSIGHTS 

  • Getting Started With Visual Analytics
  • Drill Down And Hierarchies
  • Sorting & Grouping
  • Creating And Working Sets
  • Using The Filter Shelf
  • Interactive Filters
  • Parameters
  • The Formatting Pane
  • Trend Lines & Reference Lines
  • Forecasting
  • Clustering

MODULE 5: DASHBOARDS, STORIES AND PAGES 

  • Dashboards And Stories Introduction
  • Building A Dashboard
  • Dashboard Objects
  • Dashboard Formatting
  • Dashboard Interactivity Using Actions
  • Story Points
  • Animation With Pages

MODULE 6: BI WITH POWER-BI 

  • Power BI basics
  • Basics Visualizations
  • Business Insights with Power BI

OFFERED DATA SCIENCE COURSES IN LIVERPOOL

DATA SCIENCE COURSE REVIEWS

ABOUT DATA SCIENTIST TRAINING IN LIVERPOOL

Data Science is no more the future, it has now become the present and has put forward many possibilities, with regards to incorporating it into the business activities. Organisations often come face to face with abundant data, and the need to manage them is also paramount. A Data Scientist happens to be a significant part of the workforce of an organisation, as it is based on the inferences drawn by a data scientist that further plans of action are decided upon. A report published by IBM shows that there will be around 700000 new job opportunities generated in the field of Data Science.

Liverpool - one of the cities in England with a rich history and draws a good number of young population every year, who step into the city portals to realise their dreams. The Certified Data Scientist, is a Data Science certification course offered by DataMites in Liverpool. The Data Science online course is led by experienced instructors.

Liverpool's IT sector is thriving, cementing the city as a digital powerhouse. With a strong network of tech companies and startups, the sector continues to grow exponentially. Statistics reveal that Liverpool's IT industry has experienced a 15% annual growth rate over the past three years. The city is home to over 500 IT businesses, employing more than 20,000 skilled professionals. Furthermore, Liverpool's robust infrastructure and supportive ecosystem have attracted significant investment, with over £100 million poured into the sector in the last year alone. The city's IT sector is poised for even greater success in the future.

Data science has emerged as a crucial field in today's data-driven world. The importance of a data science course cannot be overstated. With the exponential growth of data, organizations need skilled professionals who can extract valuable insights and make informed decisions. A data science course equips individuals with the necessary tools and techniques to collect, analyze, and interpret data effectively. It provides a comprehensive understanding of statistical methods, machine learning algorithms, and data visualization techniques. Moreover, it enhances critical thinking and problem-solving skills, enabling professionals to tackle complex business challenges. By mastering data science, individuals can unlock endless career opportunities in various industries and contribute to innovation and growth.

DataMites, in Liverpool, offers dual certification programs in Liverpool, in collaboration with IBM and IABAC, that are recognised on a global platform. The courses are accredited to IABAC - Globally recognised body for Business Analytics and Data Science Certifications. A duration of 8 months with 700 Hrs of training.Faculties with rich industry experience, and intense subject matter expertise.Guidance for resume building, interview preparations, expanding networks for job possibilities.Internship opportunities, with 25 capstone projects and 1 live project.

DataMites training institute is providing 55% discount on online data science course fee in Liverpool, UK. It’s a limited time offer. For any further details, you can get in touch with our executives.

DataMites also provides training for Machine Learning, Certifed Data Analyst, Deep Learning, Python, IoT, Data Engineer, Python for Data Science, and the Artificial Intelligence courses in Liverpool.

DESCRIPTION OF DATA SCIENCE COURSE IN LIVERPOOL

Data Science is the art of collecting, classifying, summarizing data sets, and deriving valuable insights from these data sets. These insights are used to take further decisions. Data Science has become instrumental in adding value to the business.

There are no mandatory prerequisites. However, basic knowledge of Statistics would be an added advantage.

  • Analytical skills

  • Basic knowledge of Mathematics and Statistics 

  • Knowledge of coding

  • Skills of working with programming languages like ‘R’ and Python.

The various business skills required, to become a Data Scientist are as follows:-

  • Industry Knowledge

  • Problem Solving Skills

  • Communication Skills 

  • Curiosity  

Industry Knowledge:- A Data Scientist should have a clear understanding of the areas that need to be paid attention and the areas that need to be ignored. This is possible only if the Data Scientist has sound knowledge of the industry.

Problem Solving Skills:- A Data Scientist is known for finding solutions to problems. For doing so, a Data Scientist must understand the problem, which can be achieved only after a deep study of the scenario.

Communication Skills:- A Data Scientist often needs to communicate the findings arrived at, with regards to analytics and business insights. A Data Scientist should be a good conversationalist. 

Curiosity:- A Data Scientist should always be curious enough while approaching a problem. Finding out the root of the problem depends upon the curiosity of a Data Scientist.

As far as Data Scientist is concerned Python is the most effective programming language, with a lot of libraries available. Python can be deployed at every phase of data science functions. It is beneficial in capturing data and importing it into SQL. Python can also be used to create data sets.

Data Science is all about managing a set of information received from various sources, to arrive at conclusions. The data that is acquired needs to be analysed and decisions need to be taken. Statistics makes it easier to work on data. Various statistical techniques such as Classification, Regression, Hypothesis Testing, Time Series Analysis is used to construct data models. With the help of Statistics, a Data Scientist can gain better insights, which enables to effectively streamline the decision-making process.

  • The different roles, Data Science is subjected to, in an organisation.

  • Analysing and managing projects.

  • Employing various data models.

  • Making use of sampling techniques

  • Prediction and Analysis

  • Segmentation through clustering technique

  • Making use of Linear and Logistics regression methods

The duration of the Data Science course in Liverpool is 8 months, a total of 700 hours of training. The training sessions are provided on weekdays and weekends. You can opt between the two, as per your convenience.

The course fee for the Data Science course in the U.K range from £ 613.32-£1533. DataMites offers a Data Science course in Liverpool at an affordable price of £1390.

Data Science is a vast subject for study, it is a mix of Statistics and Computer Science. DataMites in Liverpool, offers quality training sessions in Data Science, Artificial Intelligence, Machine Learning etc. The data science courses provided by DataMites in Liverpool are exclusively designed in tune with the current industry requirements. Also with a number of projects to work on, under the mentoring of industry experts.

Whether you need a P.G degree to pursue a data science certification can be better understood, based on your knowledge in the Science & Technology, Engineering and Management domain. If you have a strong knowledge base in any of the mentioned areas.

After completing the  Certified Data Scientist Course in Liverpool, an individual will be well equipped with the following:-

  • Intense knowledge of the workflow, of a Data Science project.

  • Learn the basics of the use of Statistics in Data Science.

  • Gain knowledge of the various Machine Learning Algorithms.

  • Knowledge of Data Forecasting, Data Mining, and Data Visualization.

  • Ways to deliver end to end Data Science projects.

Liverpool is known as the financial capital of Europe, with lots of business opportunities and large corporate houses adorning the city. This, in turn, contributes to new employment opportunities being created. Hence opting for a Data Science course in Liverpool will help an individual to leverage the available possibilities in the best manner, to land a career in Data Science.

Data Scientists have been in great demand in Liverpool. As an acknowledgement to this rising demand, DataMites has come with the Certified Data Scientist course in Liverpool. The course covers all the areas of Data Science, Machine Learning, basics of Mathematics and Statistics, etc. Also, the Certified Data Scientist course, covers all the practical aspects of the knowledge required to become a Data Scientist.

Liverpool, in the U.K, has a lot of business opportunities. It consists of many large companies, business houses, with large amounts of transactions happening every day, as a result of which there is an equally large amount of data generated daily. Also, the U.K. is known for many recognised universities. Learning Data Science in the U.K will be a great opportunity for students as well as professionals. Graduates freshers and employees working in organisations can leverage these opportunities to easily land a Data Science job.

Liverpool has several large companies, Banking, and Financial institutions, Insurance companies, Automobile companies, Manufacturing enterprises, as a result, Liverpool happens to be the most sought after city when it comes to career opportunities in Data Science.

Liverpool is a city that is always bustling with business activities, financial transactions happening in huge volumes. Hence it serves to be a great opportunity for starting a Data Science Career in Liverpool.

As per the reports published by Indeed.com, the average salary of Data Scientists in Liverpool is £44,761 annually.

A large amount of data is being generated through various activities daily. For instance, data of investments done in the stock market, data of the financial transactions, data with regards to the browsing history. The company which you are associated with records and maintains your data. For example, when you make regular online purchases, the provider collects all the information on your activity and stores it securely. It then makes use of the same data to make further product recommendations. Different companies use data in different ways.

  • Small-sized companies employ  Google Analytics for analyzing the small size of data.

  • Medium-sized companies have data that will need a Machine Learning Expert to work on it.

  • Big sized companies may need data science professionals who are experts in Machine Learning and Data Visualization.

Data Science is all about the collection and classification of information and using the same to derive insights. Python and R are the two programming languages that are used in the data science process. Some of the reasons, for python being the most preferred programming language in comparison to R:-

  • Easy to learn: Python is easier to understand and master, in comparison to R 

  • Flexible: The flexibility offered by Python offers is better when compared to the R programming language.

  • Availability of libraries: Python has a wide range of libraries available, such as pandas, scikit-learn, etc. This makes it easier in handling machine learning projects.

  • Data visualization: By using matplotlib in Python, you can do the plotting of complex data representations into 2D plots. Data visualization is a significant process in the job of a data scientist. Python can be used for Data Visualisation.
  • Dual Certification

  • Experienced Trainers

  • Industry aligned courses

  • Internship Opportunities

  • Job assistance

The mode of training offered by DataMites for the Data Science course in Liverpool is online training.

  • Graduate Freshers 

  • Individuals looking to switch their career into Data Science.

  • Professionals who have experience in the Data Science domain.

View more

FAQ’S OF DATA SCIENCE TRAINING IN LIVERPOOL

DataMites provides a range of courses in Data Science, Machine Learning, Artificial Intelligence,in Liverpool with training sessions uncompromised of quality, conducted by industry experts, professional data scientists who possess intense knowledge of the subject matter. The training is conducted in the online mode. The sessions are conducted based on case studies approach, with business cases taken up for discussion.

DataMites is a training provider that imparts quality training and upskilling in Data Science, for freshers who are data enthusiasts and professionals who wish to enhance their career possibilities. Above all DataMites offers the following;-

  • Industry aligned courses 

  • Online sessions that ensure good engagement.

  • Expert Trainers, who possess a vast knowledge of the subject matter.

  • Case studies approach, which delved deep into the practical application of the concepts.

  • Opportunity to get connected with a network of Data Science professionals.

  • Career Guidance

  • Opportunity to work on projects

DataMites has a faculty of trainers who possess deep subject matter expertise and significant years of experience in the field of Data Science.

The course fee for the Data Science course in the U.K range from £ 613.32-£1533. DataMites offers, Data Science course in Liverpool at an affordable price of £1390.

The registrations cancelled within 48 hrs of enrollment will be refunded in full. The processing time of the refund is within 30 days, from the date of the receipt of  cancellation request.

Yes. You will receive a certificate from DataMites after the completion of the course.

DataMites in Liverpool offers dual certifications in collaboration with IABAC and IBM. IABAC is a global body, which offers certifications in Business Analytics and Data Science. IABAC is founded on the principles of EDISON Data Science Framework (EDSF). IBM provides the best in class industry certifications. DataMites provides a range of certifications in Data Science, Machine Learning, Artificial Intelligence. All the data science certifications offered by DataMites are structured based on the industry trends.

Enrolling for online training online is very simple. The payment can be done using your debit/credit card that includes Visa Card, MasterCard; American Express or via PayPal. You will receive the receipt after the payment is successful. In the case of more queries, you can get in touch with our educational counsellor who will guide you with the same.

You have access to the online study materials from 6 months up to 1 year.

DataMites offers online training in Liverpool. However, classroom training can also be made available, if there is adequate demand.

DataMites offers data science sessions, both on weekdays and weekends. You can opt between the two, based on your convenience.

DataMites offers data science sessions, in the Morning and Evening. You can opt, based on your convenience.

Yes, DataMites do provide an online lab facility to practice.

Yes. DataMites do provide live data science projects, which are done under the guidance of industry experts.

The data science course offered by DataMites in Liverpool includes 25 capstone projects and 1 client project.

The training sessions provided by DataMites in Liverpool are primarily online. However, classroom training can be made available.

DataMites is a training provider that imparts quality training and upskilling in Data Science, for freshers who are data enthusiasts and professionals who wish to enhance their career possibilities. Above all DataMites offers the following;-

  • Industry aligned courses 

  • Online sessions that ensure good engagement.

  • Expert Trainers, who possess a vast knowledge of the subject matter.

  • Case studies approach, which delved deep into the practical application of the concepts.

  • Opportunity to get connected with a network of Data Science professionals.

  • Career Guidance

  • Opportunity to work on projects 

DataMites provides Flexi Pass, which gives you the privilege to attend unlimited batches in a year. The flexi pass is specific to one particular course. Therefore if you have a flexi pass for one particular course of your choice, you will be able to attend any number of sessions of that course. It is to be noted that a flexi pass is valid for a particular period.

DataMites accepts all the online payments(Debit/Credit) through Razor pay. If you opt to pay through your credit card, there will be an EMI option. DataMites collect token advance during the time of registration and the remaining payment should be settled in full before the completion of the course. 

All the data science online sessions are recorded and shared with the candidates. If you happen to miss any session, you can have access to the recording.

Yes, the Data Science Certification exam fee is included in the total course fee.

Yes. DataMites offers internship opportunities along with the course. You will be mentored by industry experts through the internship. Once the internship is completed, DataMites provides you with the internship certificate along with the experience certificate.

The DataMites Placement Assistance Team(PAT) helps the candidates to have an easy start in his/her career. The team offers services like Resume Building, Interview Preparation. The team will assist you in the following areas;-

  • Project Mentoring- 100 hrs Live mentoring in industry projects.
  • Interview Preparations- Mock Interview sessions.
  • Resume Support- Personal guidance in resume creation by professionals.
  • Doubt clearing sessions- Live doubt clearing sessions on 
  • Job updates- Interview connects.

No, DataMites doesn’t guarantee a job, but it will provide all the support and guidance needed, in getting a job, Resume Building, Interview preparations. DataMites internships offer a candidate to work with industry experts, which helps in knowing the corporate way of working. This proves as a stepping stone to an individual’s professional life.

DataMites internship programs are exclusively designed for a candidate to enable him/her to get a practical experience of working on live projects. The candidate gets an opportunity to work under the guidance of industry experts.

The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -

  • 1. Job connect
  • 2. Resume Building
  • 3. Mock interview with industry experts
  • 4. Interview questions

The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.

No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.

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