Data Science with Python Training in London, United Kingdom

Get the ability to analyze data with Python using basic to advanced concepts

  • 42 hours of Instructor led Training
  • Interactive Statistical Learning with advanced Excel
  • Comprehensive Hands-on with Python
  • Covers Advanced Statistics and Predictive Modeling
  • Learn Supervised and Unsupervised Machine Learning Algorithms

Description

Rapid technological advances in Data Science have been reshaping global businesses and putting performances on overdrive. As yet, companies are able to capture only a fraction of the potential locked in data, and data scientists who are able to reimagine business models by working with Python are in great demand.

Python is one of the most popular programming languages for high level data processing, due to its simple syntax, easy readability, and easy comprehension. Python’s learning curve is low, and due to its many data structures, classes, nested functions and iterators, besides the extensive libraries, this language is the first choice of data scientists for analysing, extracting information and making informed business decisions through big data.

This Data science for Python programming course is an umbrella course covering major Data Science concepts like exploratory data analysis, statistics fundamentals, hypothesis testing, regression classification modeling techniques and machine learning algorithms.Extensive hands-on labs and an interview prep will help you land lucrative jobs.

What You Will Learn

Prerequisites

There are no prerequisites to attend this course, but elementary programming knowledge will come in handy.

3 Months FREE Access to all our E-learning courses when you buy any course with us

Who should Attend?

  • Those Interested in the field of data science
  • Those looking for a more robust, structured Python learning program
  • Those wanting to use Python for effective analysis of large datasets
  • Software or Data Engineers interested in quantitative analysis with Python
  • Data Analysts, Economists or Researchers

KnowledgeHut Experience

Instructor-led Live Classroom

Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.

Curriculum Designed by Experts

Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.

Learn through Doing

Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.

Mentored by Industry Leaders

Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.

Advance from the Basics

Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.

Code Reviews by Professionals

Get reviews and feedback on your final projects from professional developers.

Curriculum

Learning Objectives:

Get an idea of what data science really is.Get acquainted with various analysis and visualization tools used in  data science.

Topics Covered:

  • What is Data Science?
  • Analytics Landscape
  • Life Cycle of a Data Science Project
  • Data Science Tools & Technologies

Hands-on:  No hands-on

Learning Objectives:

In this module you will learn how to install Python distribution - Anaconda,  basic data types, strings & regular expressions, data structures and loops and control statements that are used in Python. You will write user-defined functions in Python and learn about Lambda function and the object oriented way of writing classes & objects. Also learn how to import datasets into Python, how to write output into files from Python, manipulate & analyze data using Pandas library and generate insights from your data. You will learn to use various magnificent libraries in Python like Matplotlib, Seaborn & ggplot for data visualization and also have a hands-on session on a real-life case study.

Topics Covered:

  • Python Basics
  • Data Structures in Python
  • Control & Loop Statements in Python
  • Functions & Classes in Python
  • Working with Data
  • Analyze Data using Pandas
  • Visualize Data 
  • Case Study

Hands-on:

  • Know how to install Python distribution like Anaconda and other libraries.
  • Write python code for defining your own functions,and also learn to write object oriented way of writing classes and objects. 
  • Write python code to import dataset into python notebook.
  • Write Python code to implement Data Manipulation, Preparation & Exploratory Data Analysis in a dataset.

Learning Objectives: 

Visit basics like mean (expected value), median and mode. Understand distribution of data in terms of variance, standard deviation and interquartile range and the basic summaries about data and measures. Learn about simple graphics analysis, the basics of probability with daily life examples along with marginal probability and its importance with respective to data science. Also learn Baye's theorem and conditional probability and the alternate and null hypothesis, Type1 error, Type2 error, power of the test, p-value.

Topics Covered:

  • Measures of Central Tendency
  • Measures of Dispersion
  • Descriptive Statistics
  • Probability Basics
  • Marginal Probability
  • Bayes Theorem
  • Probability Distributions
  • Hypothesis Testing 

Hands-on:

Write python code to formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario

Learning Objectives: 

In this module you will learn analysis of Variance and its practical use, Linear Regression with Ordinary Least Square Estimate to predict a continuous variable along with model building, evaluating model parameters, and measuring performance metrics on Test and Validation set. Further it covers enhancing model performance by means of various steps like feature engineering & regularization.

You will be introduced to a real Life Case Study with Linear Regression. You will learn the Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. It also covers techniques to find the optimum number of components/factors using screen plot, one-eigenvalue criterion and a real-Life case study with PCA & FA.

Topics Covered:

  • ANOVA
  • Linear Regression (OLS)
  • Case Study: Linear Regression
  • Principal Component Analysis
  • Factor Analysis
  • Case Study: PCA/FA

Hands-on: 

  • With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
  • Reduce Data Dimensionality for a House Attribute Dataset for more insights & better modeling.

Learning Objectives: 

Learn Binomial Logistic Regression for Binomial Classification Problems. Covers evaluation of model parameters, model performance using various metrics like sensitivity, specificity, precision, recall, ROC Cuve, AUC, KS-Statistics, Kappa Value. Understand Binomial Logistic Regression with a real life case Study.

Learn about KNN Algorithm for Classification Problem and techniques that are used to find the optimum value for K. Understand KNN through a real life case study. Understand Decision Trees - for both regression & classification problem. Understand Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID. Use a real Life Case Study to understand Decision Tree.

Topics Covered:

  • Logistic Regression
  • Case Study: Logistic Regression
  • K-Nearest Neighbor Algorithm
  • Case Study: K-Nearest Neighbor Algorithm
  • Decision Tree
  • Case Study: Decision Tree

Hands-on: 

  • With various customer attributes describing customer characteristics, build a classification model to predict which customer is likely to default a credit card payment next month. This can help the bank be proactive in collecting dues.
  • Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
  • Wine comes in various types. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Learning Objectives:

Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
Work on a real- life Case Study with ARIMA.

Topics Covered:

  • Understand Time Series Data
  • Visualizing Time Series Components
  • Exponential Smoothing
  • Holt's Model
  • Holt-Winter's Model
  • ARIMA
  • Case Study: Time Series Modeling on Stock Price

Hands-on:  

  • Write python code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
  • Write python code to Use Holt's model when your data has Constant Data, Trend Data and Seasonal Data. How to select the right smoothing constants.
  • Write Python code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
  • Dataset including features such as symbol, date, close, adj_close, volume of a stock. This data will exhibit characteristics of a time series data. We will use ARIMA to predict the stock prices.

Learning Objectives:

A mentor guided, real-life group project. You will go about it the same way you would execute a data science project in any business problem.

Topics Covered:

  • Industry relevant capstone project under experienced industry-expert mentor

Hands-on:

 Project to be selected by candidates.

Meet your instructors

Become an Instructor
Sukesh

Sukesh Marla

Founder

Irrespective of project size I believe in working as a team. We are a team of highly qualified engineers with each specializing in their own field like designing, testing and development.
Working in a team ensures the work is not affected in case of any eventuality of any of team member. This guarantees timely delivery.

View Profile
Biswanath

Biswanath Banerjee

Trainer

Provide Corporate training on Big Data and Data Science with Python, Machine Learning and Artificial Intelligence (AI) for International and India based Corporates.
Consultant for Spark projects and Machine Learning projects for several clients

View Profile

Projects

Predict House Price using Linear Regression

With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.

Predict credit card defaulter using Logistic Regression

This project involves building a classification model.

Read More

Predict chronic kidney disease using KNN

Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.

Predict quality of Wine using Decision Tree

Wine comes in various styles. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Note:These were the projects undertaken by students from previous batches. 

Data Science with Python

What is Data Science

Data Science has become a popular career choice for professionals and aspirants, not just in London, but throughout the United Kingdom, which is on the brink of a technology revolution. London being the capital of the UK, is at the forefront of this revolution, being a hub that connects corporations, tech organizations and leading universities, such as University of London, King’s College, Birkberk, London South Bank University, etc. All these pioneering institutes and conglomerates have adopted data science with open arms because of the huge business opportunities it presents, which is why there is also a huge demand for data science professionals in London. There’s no wonder then that “data scientist” was dubbed the sexiest job of the 21st Century by Harvard Business Review in 2012. 

In today’s world, it is data that makes the earth go round. Be it the ads you see on Facebook or the products you get recommended to on Amazon - your internet experience is customized for you by an algorithm that has been written by a data scientist who knows your web browsing history. Not just your retail experience but many more avenues in our lives are influenced by data science making it a much sought after career choice. 

  • Data-driven jobs are increasing in demand in every industry.
  • Well-trained professionals are offered lucrative salaries.
  • Data Collection also helps the company in related fields such as marketing, sales and IT.

Therefore it's a Win-Win situation for both the employees and the employers.

While any one can opt to become a data scientist and pursue data science as a career in London, industry demands that data scientists have expertise in the following technologies.  In order to be a skilled Data Scientist, you need to know the following:

  1. Python Coding
  2. R Programming
  3. Hadoop Platform
  4. SQL database and coding
  5. Machine Learning and Artificial Intelligence
  6. Apache Spark
  7. Data Visualization
  8. Unstructured data
  • Python Coding - Python is the most common and popularly used programming language. It is used in various formats of data as a result of its versatility and simplicity of use. Data scientists use it to create datasets and perform operations on them.
  • R Programming - Data scientists often seek good knowledge of at least one analytical tool for better efficiency. R programming is one such tool that helps you become a better data scientist because of its immense use in the field.
  • Hadoop Platform -  A popular LinkedIn survey revealed expertise in Hadoop platform to be one of the top skill requirements to become a data scientist. Not required in the job per se, it is commonly needed in data science projects.
  • SQL database and coding -  This language is particularly designed to help scientists access, communicate and work on data. Not only does it lay out the structure and formation of the data for a better understanding, but it also offers concise commands to save time and energy. It allows less qualified professionals to smoothly work on the data.
  • Machine Learning and Artificial Intelligence - Artificial intelligence and machine learning are both very important concepts that aren’t just tied to data science. They go over and beyond it. However, their knowledge is very important to get the nuances of data science right as they form the core of data science and related fields. Some specific fields to cover are:
    • Reinforcement Learning
    • Neural Network
    • Adversarial learning 
    • Decision trees
    • Machine Learning algorithms
    • Logistic regression etc.

  • Apache Spark - It is a very popular data sharing technology similar to Hadoop because it deals with Big Data computation as well. It varies from Hadoop because it is faster owing to its cache formation in its own system while Hadoop reads and writes the data every time.
  • Apache Spark makes the data algorithm run faster as a result. It makes the dissemination of data processing of large sets easier and helps work through unstructured, uncategorized data easily. Since it is easier to store data with Apache spark, loss of data is less frequently recorded.
  • Data Visualization: Tools like d3.js, Tableau, ggplot and matplotlib help a data scientist process raw unstructured data into legible sets with coherent representation. It makes complicated data easy to understand and work with. With techniques like data visualization, it becomes easier for organizations to collect and work with primary data instead of relying on second-hand sources for their projects.
  • Unstructured data: Every data scientist should have hands-on experience working with unstructured data, that is data that is not labeled or organized. Examples of such can be videos, social media posts, audio samples, customer reviews, blog posts, etc.

A successful data scientist would exhibit these 4 behavioural traits:

  • Curiosity - An undying hunger to explore and know more each day is what you need to excel in data science. You will have to keep track of the massive amounts of data that change on a daily basis.
  • Clarity - Understanding your field of work right from the intricacies to its need in even the most mundane of activities is important. Data science is a vast field and the clarity you have for your work will make you stand apart from others.
  • Creativity - The very fact that data is everywhere, calls for incredible creativity all around to make your work stand out. Your creativity can range from ways to present data to ways to collect it to tools to process it.
  • Skepticism - Skepticism is what keeps the creativity in check. At the end of the day, you are still dealing with business and analytics - you should not get carried away with creativity.

London is home to many prominent organisations, such as Google, Financial Times, LiquidNet, Monzo, FaceIT, etc. Besides holding the hottest jobs of the 21st century, data scientists possess several privileges over other professions. Here are the major benefits of being a data scientist today:

  • Great pay - Simply put, due to the incredibly high demand and low supply, data scientist roles are among the highest paying jobs in the IT industry.
  • Lucrative bonuses - Apart from the benefits of equity shares and signing perks, one can expect good bonuses to boot.
  • High education - With the inherent need of data scientists owning at least a Masters or Ph.D. in their respective fields, the job ensures a high education for you - rendering other high-paying fields a viable option along the way.
  • Mobility - Most data science jobs are located in developing countries - offering avenues for you to relocate and aim for good packages and high standard of living. 
  • Networking - Networking in your job is of the utmost importance in the well-connected world we live in. Through research papers and tech conferences that you will get to be a part of,  you will be able to build a great network for referral purposes.

Data Scientist Skills and Qualifications

The top 4 business skills needed to become a data scientist are as follows:

  • Analytical problem-solving - A clear perspective and awareness are needed to formulate the right strategies to deal with problems on a day-to-day basis. 
  • Communication skills - Being able to communicate with people - be it customers or top-ranking business professionals is key.
  • Intellectual curiosity - The insatiable hunger to know more than there is coupled with the strong desire to deliver top-notch results is what produces valuable work in this highly competitive field.
  • Industry knowledge - Having a thorough knowledge of the industry is a prerequisite for the job of a data scientist. Knowing your industry inside out helps you process data more accurately and curate it better for the target audience.

Following are a few of the ways you can polish your skills to be a data scientist:

  • Boot camps - Boot camps are perfect to get in touch with your Python skills. Perfect for both hands-on training and by-the-books knowledge, these last anywhere between 4 to 5 days, so are easy to accommodate into your schedule.
  • MOOC Courses - Online courses that aim to freshen up your memory in the form of assignments. Taught by data science experts, they are perfect for people looking to get back to the field.
  • Certifications - Not only do they teach you additional skills, but they also improve your resume with tangible recognition of your expertise. Some helpful certifications:
    • Applied AI with Deep Learning, IBM Watson IoT Data Science Certificate
    • Cloudera Certified Associate - Data Analyst
    • Cloudera Certified Professional: CCP Data Engineer
  • Projects - They offer the opportunity for you to explore new solutions to already existing problems while also brushing up your theoretical knowledge in the face of a practical setting.
  • Competitions - Competitions like Kaggle etc. help you improve important skills like problem-solving and time management as well as working within restraints helps you deal with challenging real world situations. 

We are surrounded by data in our daily lives. Every kind of company uses data in varying capacity in its day-to-day operations. London is home to many prominent organisations, such as Google, Financial Times, LiquidNet, Monzo, FaceIT, etc. It’s entirely your choice what kind of work you want to employ yourself in.

  •  Small companies rely on Google analytics as they have fewer resources.
  •  Medium-sized companies require Machine learning experts to put their already collected data to use.

 Big companies like MNCs already work with pre-decided teams so a new addition there would need to be a specialist in something like Artificial Intelligence etc.

The most practical way to practice data science is to engage in problems and solve them on your own. Following are some of the problems, categorized according to the level of difficulty:

  • Beginner level
    • Iris data set – One of the most popular, versatile and easy data set in the field of pattern recognition. Easy in the way of structure and understanding, it’s inclusion is a must in your learning of various classification techniques. Consisting of only 4 rows and 50 columns, it can be found in every beginner's training schedule. Practice problem – Predict the class of a flower based on the following parameters.
    • Loan Prediction data set – The bread and butter of the banking industry. The banking industry uses data analytics way more than any other industry. This particular data set provides the learner a hands-on experience of the basic concepts of banking and insurance. The loan prediction data set is made of 13 columns and 615 rows which introduces the learner to the challenges faced and the policies implemented by the industry. Practice problem – Predict if this particular loan will be implemented by the bank or not.
    • Bigmart sales data set – The retail industry relies on the heavy use of data analytics to operate on a daily basis. Product bundling, offer customizations and inventory management are few of the operations that are possible because of data science. With 12 variables and 8522 rows, it is an expansive set used in regression problems. Practice problem – Predict the sales of this particular retail store.
  • Intermediate level
    • Black Friday data set – It is considered an intermediate level set because it deals with the sales transactions on a daily basis. It is great to gain an understanding of the shopping experiences of millions of shoppers shopping from a single place. It consists of 12 columns and 55 0,069 rows and deals with a regression problem. Practice problem – Predict the amount of purchase made in this store today.
    • Human activity recognition data set – It records data of up to 30 humans collected via smartphone recordings. It has 561 columns and 10299 rowsPractice problem – Predict the human activity for this particular set of time.
    • Text mining data set - Originating from the Siam text mining competition 2007, this sets consists of Aviation safety reports. These describe the problems encountered by flights. This high dimensional data set is made up of 30,438 rose and 21,519 columns. Practice problem – Classify the following documents based on their labels.
  • Advanced level
    • Urban sound classification - Learning about hypothetical situations like the Titanic survival does not give the learner a taste of real-world situations. For those machine learning experiences, the beginner can opt for urban sound classification data sets that deal with the implementation of machine learning concepts to real-world problems. It is made of 8732 sound clippings of urban sounds that can be segregated into 10 different groups.
      Practice problem – Classify the given sound from this audio.
    • Identify the digits data set - Taking up the space of 31 MB, it comprises of 7000 images with the dimensions of 28x28 each. It helps the developer learn and recognize elements of an element.
      Practice problem – Identify the digits in the given image.
    • Vox celebrity data set -  It deals with the very important concept of audio processing. It is a collection of words spoken by celebrities taken from YouTube videos. It helps in isolation as well as identification of speech recognition. it consists of 100,000 words spoken by 1,251 celebrities.
      Practice problem – Identify the celebrity in this audio clip.

How to become a Data Scientist in London, UK

Below are the 4 steps you can take to become a successful data scientist in London:

  • Getting started - First and foremost, you need to pick a programming language you are comfortable in and would want to learn more about. Python and R languages are quite popular in this regard.
  • Mathematics and statistics - Be it numerical, textual or images, data science is all about dealing with data on a daily basis. You must be familiar with mathematics and statistics like basic algebra and patterns to excel in the field of data science.
  • Data visualization - Representing the collected data is just as important as recording it. You must strive to make it simple as well as lucrative to make it coherent and useful at the same time. The goal is to make it easy to understand for the end users.
  • Machine learning and deep learning - Pair up your deep learning with machine learning to become a successful data scientist as machine learning techniques work best when teamed up with practical knowledge.

Here are the key skills and steps that you need to take to become a data scientist:

  • Degree/certificate - In today's world, online courses hold just as much value as courses completed in a classroom. Be it an online or offline course, certification is needed for you to get documented proof of your knowledge as well as gain some fundamental knowledge of the same. Due to the continuous learning needed in this field, data science has more Ph.D. holders than most.
  • Unstructured data - The most important part of a data science job is to figure out meaningful patterns in unstructured data. You must be familiar with identifying important things in uncategorized data.
  • Software and frameworks - Since you are going to work with incredible amounts of unstructured data, you must familiarize yourself with software and frameworks of the field.
    • R may have a steep learning curve, it is still the most used language for statistical problems. Around 43% of the data scientists use R language in their projects.
    • Hadoop framework is used by scientists when the data exceeds the memory at hand. It quickly conveys the data into the machine. Though Spark is fast becoming popular due to its advantage of the speed that it has over Hadoop. Apart from computing faster, it also prevents the loss of data as has been feared with Hadoop.
    • You must be proficient in SQL queries as the knowledge of database is just as important as the framework and language.
  • Machine learning and deep learning - Applying the textbook is what makes a successful data scientist. Machine learning is all about the implementation of the textbook concepts to the real-world for better analysis and growth.
  • Data visualization - Representing data in a coherent fashion is important to make informed business decisions. You will have to make sense out of a huge pile of unstructured data to make decisions for the betterment of your company.

A degree is an absolute must-have in data science as 88% of data scientists hold a Masters degree while 46% are PhD holders. Moreover, you can get several opportunities in London as it is home to many leading universities which offer advanced courses in Data Science, such as the University of London, King’s College, Birkbeck, London South Bank University, etc. It is important because of the following key reasons:

  • Networking - While pursuing these high-level degrees, you get to socialize with important people in the field and advance yourself.
  • Structured learning - Picking up books here and there or unplanned learning does not benefit you much in the long run. Study by following a fixed curriculum and schedule to get a better understanding of the field.
  • Internships - High-level degrees always come with the chances of good internships that help you achieve hands-on experience with the proper certification of the same.
  • Recognized academic qualification - It will reflect well on your resume and make you appear a better-suited contender for top jobs in your selected field.

There are many prominent universities in London offering Master’s degree in London, such as the University of London, King’s College, Birkbeck, London South Bank University, etc. Simply grade yourself on the scoreboard below. If your total is more than 6 points, we advise you to pursue a Masters degree:

  • You have a strong STEM (Science/Technology/Engineering/Management) background: 0 point
  • You have a weak STEM background ( biochemistry/biology/ economics or another similar degree/diploma): 2 points
  • You belong to a non-STEM background: 5 points
  • You have less than 1 year of experience in working with Python programming language: 3 points
  • You have never been part of a job that requires you to code on a regular basis: 3 points
  • You think you are not good at independent learning: 4 points
  • You do not understand what it means when we tell you that this scorecard is a regression algorithm: 1 point

Knowledge of programming is perhaps the most important skill that a data scientist must possess, irrespective of where you are situated in. It is important because of the following reasons:

  • Data sets - almost everything you work on will include huge data sets of unstructured data. You must have appropriate knowledge of the sets to work with them.
  • Statistics - The ability to both read and construct well-laid out statistics is a must to deal with data.
  • Framework - is what helps the data scientist to present his work in an efficient manner. These frameworks help build systems that help organizations with their projects and high-scale decisions.

Data Scientist Salary in London, UK

The annual pay for a Data Scientist in London is £50,211 on an average basis. 

The average salary for a Data Scientist is £42,880 in Manchester, which is £7,331 less in comparison to the salary in London. 

A Data Scientist earns about £50,211 every year in London, which is less than the income earned by a data scientist in Liverpool, which is £46,814. 

A Data Scientist can earn about £53,268 in Bristol, which is higher than the £50,211 earned by a Data Scientist in London. 

There are various organizations in the city that are stepping into the world of Data Science and are looking for data scientists to convert their raw material into useful business insights. So, the demand for data scientists in London is high.

A data scientist working in London enjoys multiple benefits including the opportunity for tremendous job growth. They work head-to-head with top level management and help them make important business decisions by offering business insights from raw data.

Being a data scientist offers certain perks and advantages over other jobs. These include:

  • The ability to work in any field that you want
  • Multiple job opportunities due to high demand
  • Networking opportunities with top-level executives

Companies recruiting Data Scientists in London include Oliver Bernard, IBM and Opus Recruitment Solutions Ltd. 

Data Science Conferences in London, UK

S.NoConference nameDateVenue
1.Python for Data Science8 October, 2019

London 18 Clerkenwell Green Clerkenwell London EC1R 0DP United Kingdom

2.ODSC Europe 2019 - Open Data Science Conference19 - 22 November, 2019

Hotel Novotel London West 1 Shortlands London W6 8DR United Kingdom

3.Industrial Strength Data Science presents: “We are not unicorns”16 May, 2019

Royal Statistical Society (HQ) 12 Errol Street London EC1Y 8LX United Kingdom

4.DataOpticon4 September, 2019

The Microsoft Reactor London 70 Wilson Street London EC2A 2DB United Kingdom

5.

Incremental Transformation in Charities with Data Science and Dynamics 365

27 June, 2019

Microsoft, 2 Kingdom St 2 Kingdom Street London W2 6BD United Kingdom

6.Geo Data Minds Seminar - #GDMLDN - Science Museum
6 June, 2019

The Science Museum Exhibition Rd South Kensington London SW7 2DD United Kingdom

7.Demystifying Data Science and Machine Learning on Azure – FREE Half-Day Workshop.
29 May, 2019
White City House Television Centre 101 Wood Ln London W12 7FR United Kingdom
8.Big Data LDN (London)
13 Nov, 2019 to 14 Nov, 2019

Olympia London Hammersmith Road London W14 United Kingdom

9.NIHR - HDR UK Incubator in Health Data Science Launch Event30 May, 2019
Wellcome Trust 215 Euston Road London NW1 2BE United Kingdom
10.NHS RTT & Data Assurance Summit
10 May, 2019
Kings Cross, London, United Kingdom

1. Python for Data Science, London

  • About the conference: One cannot understand Data Science without having the knowledge of the programming language. This seminar will help you understand the use of python for high-level data analysis.
  • Event Date: 8 October, 2019
  • Venue: London 18 Clerkenwell Green Clerkenwell London EC1R 0DP United Kingdom
  • Days of Program: 1
  • Timings: 09:00 – 17:00 BST
  • Purpose: The aim of the seminar is to help the participants understand the use of Python in data analysis.
  • Registration cost: £353.81 – £499.39
  • Who are the major sponsors: Digital 360 ltd

2. ODSC Europe 2019 - Open Data Science Conference, London

  • About the conference: This conference will be attended by data science professionals who will learn about the latest techniques used in Data Science.
  • Event Date: 19 - 22 November, 2019
  • Venue: Hotel Novotel London West 1 Shortlands London W6 8DR United Kingdom
  • Days of Program: 4
  • Timings: 8:00 AM – 8:00 PM GMT
  • Purpose: The purpose of the conference is to learn new skills, network with the community, learn from the leading experts, and talk about everything in AI and Data Science.
  • Registration cost: £1,202.34 – £3,842.34
  • Who are the major sponsors: ODSC team

3. Industrial Strength Data Science presents: “We are not unicorns”, London

  • About the conference: The event is for the data Science enthusiasts who are looking to learn about the current and upcoming trends of Data Science and build a better network.
  • Event Date: 16 May, 2019
  • Venue: Royal Statistical Society (HQ) 12 Errol Street London EC1Y 8LX United Kingdom
  • Days of Program: 1
  • Timings: 18:30 to 21:00 (BST)
  • Purpose: The purpose of the event is to have renowned industry professionals share their journey and insights regarding Data Science.
  • Registration cost: Free

4. DataOpticon, London 

  • About the conference: The conference is aimed at helping the attendees understand the implementation of DataOps.
  • Event Date: 4 September, 2019
  • Venue: The Microsoft Reactor London 70 Wilson Street London EC2A 2DB United Kingdom
  • Days of Program: 1
  • Timings: 08:30 – 18:00 BST
  • Purpose: The purpose of the conference is to help the professionals from data science, data engineering, business intelligence, and databases get familiar with DataOps.
  • Registration cost: Free
  • Who are the major sponsors: Nightingale HQ

5. Incremental Transformation in Charities with Data Science and Dynamics 365, London

  • About the conference: The conference will cover the Data Science revolution and also different Machine Learning techniques.
  • Event Date: 27 June, 2019
  • Venue: Microsoft, 2 Kingdom St 2 Kingdom Street London W2 6BD United Kingdom
  • Days of Program: 1
  • Timings: 10:00 – 16:30 BST
  • Purpose: The purpose of the conference is to help the attendees get acquainted with tools used in Machine Learning like R/Spark MLLIB/Python.
  • Registration cost: Free
  • Who are the major sponsors: cloudThing LTD

6. Geo Data Minds Seminar - #GDMLDN - Science Museum, London

  • About the conference: The seminar is going to deal with the use of GeoData. Geospatial data is used to make consumer decisions and transactions simple and fast.
  • Event Date: 6 June, 2019
  • Venue: The Science Museum Exhibition Rd South Kensington London SW7 2DD United Kingdom
  • Days of Program: 1
  • Timings: 09:00 – 12:00 BST
  • Purpose: The purpose of the seminar is to explore the data intelligence and help the businesses up their game.
  • Registration cost: Free
  • Who are the major sponsors: CACI LTD

7. Demystifying Data Science and Machine Learning on Azure – FREE Half-Day Workshop, London

  • About the conference: The workshop is a half-day session for professionals from business as well as IT to discuss Data Science, Advanced Analytics, and Machine learning on Azure.
  • Event Date: 29 May, 2019
  • Venue: White City House Television Centre 101 Wood Ln London W12 7FR United Kingdom
  • Days of Program: 1
  • Timings: 12:00 – 18:00 BST
  • Purpose: The purpose of the workshop is to make the attendees have a better understanding of the real world use cases of Data Science and Machine Learning. Also, it will provide a detailed insight on how to use these on the Azure AI platform.
  • How many speakers: 3
  • Speakers & Profile:
    • Kate Rosenshine, Head of Data & AI Cloud Solution Architecture, Financial Services at Microsoft
    • Henry Brown, Head of Data and Analytics at BJSS
    • John Davis, Head of Technology Consulting at BJSS
  • Registration cost: Free
  • Who are the major sponsors: BJSS

8. Big Data LDN (London)

  • About the conference: It is a conference and exhibition where the analytics expert will give you complete insights about the tools used to deliver data-driven strategy.
  • Event Date: 13 Nov, 2019 to 14 Nov, 2019
  • Venue: Olympia London Hammersmith Road London W14 United Kingdom
  • Days of Program: 2
  • Timings: 9 AM to 4:30 PM
  • Purpose: The purpose of the conference is to bring together 150 speakers from different industries and discuss latest launches, products, and demos.
  • Registration cost: Free
  • Who are the major sponsors: 3rd Street group LTD

9. NIHR - HDR UK Incubator in Health Data Science Launch Event, London

  • About the conference: The conference will have clinical as well as non clinical researchers from the field of health data science. The attendees will have an opportunity to develop a wider network.
  • Event Date: 30 May, 2019
  • Venue: Wellcome Trust 215 Euston Road London NW1 2BE
  • Days of Program: 1
  • Timings: 10:30 – 17:00 BST
  • Purpose: The purpose of the event is to launch an incubator that will provide an opportunity to bring together experts from NIHR and HDR UK and know about their research.
  • Registration cost: Free
  • Who are the major sponsors: NIHR academy

10. NHS RTT & Data Assurance Summit, London

  • About the conference: The conference is in partnership with Stalus and ICS insourcing. The summit will cover the opportunities and challenges faced while building an effective RTT recovery plan.
  • Event Date: 10 May, 2019
  • Venue: Kings Cross London United Kingdom 
  • Days of Program: 1
  • Timings: 11:00 – 16:00 BST
  • Purpose: The purpose of the summit is to help attendees learn to create an effective recovery program, including data assurance.
  • Registration cost: Free
  • Who are the major sponsors: Draper and Dash
S.NoConference nameDateVenue
1.Chief Data Officer Europe 201720-23 February, 2017

Grand Connaught Rooms 61-65 Great Queen Street, WC2B 5BZ London

2.Deep Learning in Healthcare Summit28 February, 2017 - 1 March, 2017LSO St Luke's, 161, Old Street, London
3.Big Data Innovation Summit30-31 March, 2017
155 Bishopsgate, London, EC2M 3YD
4.Chief Analytics Officer Europe 2017
25-27 April, 2017

Amba Hotel Marble Arch London, United Kingdom

5.

Strata + Hadoop World

23-25 May, 2017

ExCeL London One Western Gateway Royal Victoria Dock London, E16 1XL, UK

6.AI Congress - Hello Business, Meet the Future
11-12 Sep, 2018

Olympia London Hammersmith, Rd, London W14 8UX, UK

7.Big Data Innovation Summit
21-22 March, 2018
155 Bishopsgate, London, EC2M 3YD
8.Robotic Process Automation & AI Week
26-28 November, 2018
Twickenham Stadium, London
9.Data Visualisation Summit
1-2 November, 2018
8 Fenchurch Place London EC3M 4PB

1. Chief Data Officer Europe 2017, London

  • About the conference: The conference allowed its attendees to achieve their goals effectively by connecting them to technologies, insights, and people.
  • Event Date: 20-23 February, 2017
  • Venue: Grand Connaught Rooms, 61-65 Great Queen Street, WC2B 5BZ London
  • Days of Program: 4
  • Purpose: This conference connected over 100 leaders from the field of the data industry and imparted knowledge on best practices, latest innovations, and challenges.

2. Deep Learning in Healthcare Summit, London

  • About the conference: Around 200 technologists and innovators came together to present the upcoming technologies in Deep Learning and how it can be applied in medicine and healthcare.
  • Event Date: 28 February, 2017 - 1 March, 2017
  • Venue: LSO St Luke's, 161 Old Street, London
  • Days of Program: 2
  • Timings: 28 Feb - 8:15 A.M. to 5 P.M, 1 March - 9 A.M. to 3 P.M.
  • Purpose: The purpose of the conference was to cover the important aspects of deep learning including image retrieval, pattern recognition, speech recognition, machine learning, and MedTech and their applications in healthcare, diagnostics, and medicine.
  • Speakers & Profile:
    • Ben Glocker - Lecturer, Medical Image Computing - Imperial College London
    • Kyung Hyun Sung - Assistant Professor - UCLA
    • Anastasia Georgievskaya - Research Scientist - Beauty.AI
    • Polina Mamoshina - Research Scientist - Insilico Medicine
    • Oladimeji Farri - Senior Research Scientist - Philips Research
    • Neil Lawrence - Professor of Machine Learning & Computational Biology - University of Sheffield
    • Michael Kuo - Associate Professor - UCLA
    • Fangde Liu - Research Associate - Imperial College London
    • Václav Potesil - CEO & Co-Founder - Optellum
    • Viktor Kazakov - Co-Founder - SkinScanner
    • Natalia Simanovsky - Business Development Lead - CVEDIA
    • Stephen Hicks - Founder & Head of Innovation - OxSight
    • Luca Bertinetto - Ph.D. Candidate - University of Oxford
  • Who were the major sponsors:

    • Intel
    • Nvidia
    • Accenture
    • MIT Technology Review
    • Graphcore
    • KDnuggets
    • IBM
    • Facebook
    • Forbes

    3. Big Data Innovation Summit, London

    • About the conference: The conference connected executives for in-depth knowledge of Big Data through discussions, keynotes, panel sessions, and networking. 
    • Event Date: 30-31 March, 2017
    • Venue: 155 Bishopsgate, London, EC2M 3YD
    • Purpose: The purpose of the conference was to cover innovations and latest technologies in the field of Data Analytics through case studies, and to discuss topics such as Advanced Analytics, Hadoop, Data Science and Cultural Transformation by the use of data.
    • How many speakers: 27
    • Speakers & Profile:
      • Berian James - Head of Data Science, Maersk
      • James de Souza - Head of Customer Analytics, Post Office
      • Jayshree Kottapalli - Head of Big Data Analytics, Vodafone
      • Pierre du Toit - Head of Technical Pricing & Big Data Analytics, Vitality
      • Sarah Phenix - Global Head of Data Governance, Aviva
      • Sudip Trivedi - Head of Business Intelligence, London Borough of Camden
      • Sue Daley - Head of Big Data & Mobile Services, techUK
      • Tomas Sanchez - Head of Data Analysis & Interaction, Airbus
      • Xiaolan Sha - Lead Data Scientist, Sky
      • Mike Hyde - Director, Data Science, Workplace by Facebook
      • David Teague - Head of Data Insight, BBC
      • Pedro Cosa - VP, Insights & Analytics, Turner Broadcasting
      • Steven James - Partner, Big Data, Brown Rudnick
      • Sharukh Naqvi - Vice President, Analytics, Barclays
      • Harvinder Atwal - Head of Data Strategy, MoneySuperMarket
      • Ozoda Muminova - Head of Insight, Telegraph Media Group
      • Robin Goad - Head of Customer Analytics, Financial Times
      • Adrian Foltyn - Head of Data Science, HelloFresh
      • Mark Ainsworth - Head of Data Insight, Schroders
      • Tommy Ockerby-Tran - Director, Data & Analytics, Expedia
      • Charlie Boundy - Head of Data Science, Head of Analytics & Marketing Services, Department for Work & Pensions
      • Christophe Loyce - Head of Analytics & Marketing Services, Tesco
      • Bryan Lawrence - Director, Data & Models, National Centre for Atmospheric Science
      • Johanna Hutchinson - Head of Data, The Pensions Regulator
      • Jonathan Francis - Creative Strategist, Google
      • Alpesh Doshi - Principal & Co-Founder, Redcliffe Capital
      • Jamie Wheeler - Data Science Associate, George Mason University
    • Who were the major sponsors:
      • Domo
      • Alteryx
      • Oracle
      • Datanami
      • Mind Commerce
      • Financial Times
      • Enterprise Management 360
      • Decideo
      • Tibco
      • Women who code

      4. Chief Analytics Officer Europe 2017, London

      • About the conference: The conference brought together senior analytics community to explore and discuss the analytics challenges faced by the industry.
      • Event Date: 25-27 April, 2017
      • Venue: Amba Hotel Marble Arch, London, United Kingdom
      • Days of Program: 3
      • Timings: 8:00 am EDT to 5:00 pm EDT
      • Purpose: The purpose of the conference was to explore and discuss strategies to gain benefits through data analytics.
      • How many speakers: 30
      • Speakers & Profile:
        • Joe DeCosmo - Chief Analytics Officer
        • Sanna Pöyhönen - European Head of Research and Analytics
        • Matt Lovell - Head of Customer Insight
        • Karin Immenroth - Chief Analytics  Officer
        • Dr. Theresa Stangl - Director of Analytics & Insight
        • Pablo Suarez - BI & Analytics Director, Digital D2C
        • Holger Hürtgen, Partner
        • Tom Smith - Managing Director - Data Science Campus
        • Alan Gormley - Chief Analytics Officer
        • Stavros Apostolou - VP Business and Consumer Analytics
        • Harry Powell - Head of Advanced Data Analytics
        • Ivan Guz - Chief Analytics Officer
        • Jean-Pierre Rabbath - Chief Product and Analytics Officer
        • Jessica Rusu - Senior Director, EU Analytics & Research
        • Delia Di Bona - Chief Analytics Officer
        • Anine Stiansen - Chief Analytics Officer
        • Simon Hayter - Chief Analytics Officer
        • Philip Theron - Global Data Analytics Lead & IFC ITGC Specialist
        • Cesar Perez - Director of BI
        • Davide Maiello - Head of Market and Business Intelligence
        • Dr. Mark Nasila - Head of Advanced Analytics
        • Francesco Marzoni - Vice President, Business Intelligence & Analytics
        • Kristina Hännikäinen - Director, Insight & Analytics
        • Mahendra Jape - Director and Data Governance Leader
        • Peter Laflin - Chief Data Scientist
        • Prof J. Mark Bishop - Director - TCIDA (Tungsten Centre for Intelligent Data Analytics)
        • Dr. Peter V.W. Hartmann; Ph.D. - Director of People Performance and Analytics
        • Sergio Romero Peñas - Director, Marketing & Performance Analytics
        • Tomasz Wyszynski - Director, Data Analytics
        • Vipul Chhabra - Global Head of Customer Analytics
      • Who were the major sponsors:
        • McKinsey & Company
        • Information Builders
        • Alteryx
        • Mango Solutions
        • Disruptive Tech TV
        • Compare the Cloud
        • Barclay Hedge
        • Modern Analyst
        • Women in Big Data
        • Big Data Made Simple
        • Financial IT
        • Private Banking
        • Datafloq
        • 7wData
        • Hedgeweek
        • Institutional Asset Manager
        • Private Equity Wire
        • Clocate
        • EIN Presswire

        5. Strata + Hadoop World, London

        • About the conference: The conference helped its attendees to acquire deeper knowledge in the latest and upcoming technologies in data.
        • Event Date: 23-25 May, 2017
        • Venue: ExCeL London, One Western Gateway, Royal Victoria Dock, London, E16 1XL, UK                                                  
        • Days of Program: 2
        • Timings: Tuesday, 23 May: 17:00 – 18:00 (Opening Reception), Wednesday, 24 May: 10:30 – 17:05 (Exhibits Open), 18:05 – 19:05 (Expo Hall Reception), Thursday, 25 May: 10:30 – 16:35 (Exhibits Open)
        • Purpose: The conference aimed at exploring the future of data science, through case studies, develop skills through workshops and tutorials and discuss best practices in data science.
        • How many speakers: 10
        • Speakers & Profile:
          • Andre Araujo - Cloudera
          • Arturo Bayo - Synergic Partners
          • Mark Donsky - Okera
          • Mark Grover - Lyft
          • Luke Han - Kyligence
          • Mubashir Kazia - Cloudera
          • Ted Malaska - Capital One
          • Mostafa Mokhtar - Cloudera
          • Syed Rafice - Cloudera
          • Jonathan Seidman - Cloudera
        • Who were the major sponsors:
          • Cloudera 
          • O'Reilly Media
          • IBM 
          • Intel 
          • Dell EMC
          • Google 
          • BMC Software
          • Honeywell 
          • NVIDIA 
          • Snowflake Computing
          • Teradata 

          6. AI Congress - Hello Business, Meet the Future, London

          • About the conference: The 2 days long conference provided panel discussions, conference presentations, networking opportunities with experts and more than 50 booth exhibitions.
          • Event Date: 11-12 Sep, 2018
          • Venue: Olympia London, Hammersmith Rd, London W14 8UX, UK
          • Days of Program: 2
          • Timings: 9:00 AM - 8:00 PM (Sep 11) (General), 9:00 AM - 5:00 PM (Sep 12) (General) Purpose: The purpose of the conference was to bring together business leaders from leading companies to discuss the real-time applications of AI and the latest technologies, challenges, and opportunities in AI.
          • How many speakers: 1
            • Speakers & Profile:
            • Jeremy Gu, Senior Data Scientist at Uber

            7. Introduction to Machine Learning in Healthcare Workshop, London

            • About the conference: The conference aimed at exploring the prospects of digital healthcare and medicine and discusses the challenges faced in order to improve patient outcomes.
            • Event Date: 14 February, 2018
            • Venue: etc Venues Bonhill House, 1-3 Bonhill Street, London, EC2A 4BX
            • Days of Program:
            • Timings: 1:45 P.M. to 5:30 P.M. 
            • Purpose: The purpose of the conference was to cover the important aspects of deep learning including image retrieval, pattern recognition, speech recognition, machine learning, and MedTech and their applications in healthcare, diagnostics, and medicine.
            • Speakers & Profile:
              • Dr. Maria Chatzou, CEO of Lifebit
              • Andreas Theodorou,  Teaching Fellow and a Ph.D. student in the Intelligent Systems group at the University of Bath
              • David A. Clifton, Associate Professor of Engineering Science at the University of Oxford and leads the Computational Health Informatics (CHI) Laboratory 
              • Nophar Geifman, Lecturer, University of Manchester

              8. Big Data Innovation Summit, London

              • About the conference: The conference connected executives for in-depth knowledge of Big Data through discussions, keynotes, panel sessions, and networking.
              • Event Date: 21-22 March, 2018
              • Venue: 155 Bishopsgate, London, EC2M 3YD
              • Days of Program: 2
              • Timings: 8 A.M. to 5 P.M.
              • Purpose:  The purpose of the conference was to cover innovations and latest technologies in the field of Data Analytics through case studies, and discuss topics such as Advanced Analytics, Hadoop, Data Science and Cultural Transformation by the use of data.
              • Speakers & Profile:
                • Sue Daley - Head of Big Data & Mobile Services, techUK
                • Anders Drachen - Professor, University of York
                • Clemence Chee - International Director Operations Business Intelligence, HelloFresh
                • Benjamin Lavoie - Sr. Director, Operations, Anheuser-Busch
                • Maciej Partyka - Global Head of Customer Insights, Barclaycard
                • Alex Healing - Chief Researcher, BT
                • Daniel Gilbert - Head of Data, News UK
                • Wilma Smythe - Founder, Insight for Good
                • Lucy Alexander - Product Analytics, Financial Times

                • Who were the major sponsors:
                  • Datanami
                  • Enterprise Management 360
                  • Cynozure
                  • 7Data
                  • Decideo
                  • Financial Times
                  • Data iku
                  • Cloud pro
                  • alteryx

                  9. Robotic Process Automation & AI Week, London

                  • About the conference: The conference brought together the world’s best experts to present sessions and workshops for AI and RPA.
                  • Event Date: 26-28 November, 2018
                  • Venue: Twickenham Stadium, London
                  • Days of Program: 3
                  • Purpose: The conference aimed to help its attendees master AI and Robot Process automation by learning about its application in business management and risk management.

                  10. Data Visualisation Summit, London

                  • About the conference: The conference included workshops on charting and visual tools and discussions on the impact of data visualization and Open Data.
                  • Event Date: 1-2 November, 2018
                  • Venue: 8 Fenchurch Place London EC3M 4PB
                  • Days of Program: 2
                  • Timings: 8 AM to 5:30 PM
                  • Purpose: The conference provided a platform to share best practices and ideas with experts and leaders in big data and visualization.
                  • How many speakers: 18
                  • Speakers & Profile:
                    • Albert Zarys - Digital Analyst, TalkTalk
                    • Charalampos Xanthopoulakis - Senior Software Designer, Koninklijke Philips
                    • Charlie Boundy - Head of Data Science, Department for Work and Pensions
                    • Dan Isaac - Business Intelligence Officer, University of Exeter
                    • Daniel Kellett - Director of Data Science, Capital One
                    • Fergus Wadsley - Principal Data Scientist, Capital One
                    • Marc Garcia - Senior Data Scientist, Badoo
                  • Who were the major sponsors:
                    • Actuate
                    • Geckoboard
                    • Visualizing.org
                    • iDashboards 

                  Data Scientist Jobs in London, UK

                  The following would be the most suited pattern to follow if you want to land a job as a data scientist.

                  • Getting started - First of all, choose a programming language you are comfortable with, like Python or R language. Then, get familiar with the job, roles, and responsibilities of a data scientist to understand your role better.
                  • Mathematics - Data science deals with making coherent analysis out of raw data which might not make a lot of sense on its own. You need to have good command over mathematics to be comfortable in data science. Pay special attention to: 
                    • Descriptive statistics
                    • Probability
                    • Linear algebra
                    • Inferential statistics
                  • Libraries - Processing raw data into a structured format includes real-life application of machine learning techniques. Some famous libraries are: 
                    • Scikit-learn
                    • SciPy
                    • NumPy
                    • Pandas
                    • Ggplot2
                    • Matplotlib
                  • Data Visualization - A data scientist is expected to make coherent and presentable content out of raw, unstructured data. One of the most popular ways to present data has been the graph. Some commonly used ones:
                    • Matplotlib - Python
                    • Ggplot2 - R

                  • Data Processing - To make it presentable and usable, it becomes important to process the data right. Data scientists need to know how to apply machine learning concepts to real-world practical problems smartly and make them analysis-ready.
                  • Machine learning and deep learning - Invest some time in deep learning to go with your basic machine learning skills to make for an impressive resume boost. You can get familiar with neural networks, CNN and RNN for those.
                  • Natural language processing - You must be good with NLP, which involves processing and classification of the text form of the data.
                  • Polishing skills - Keep brushing your skills from time to time to keep yourself in touch with your knowledge of the field. Competitions like Kaggle are a great way to do that. You can also experiment on your own with personal projects.

                  Follow these 5 steps to prepare for your dream job of being a data scientist in the city of London:

                  • Study - To prepare for your data science job interview, you need to cover all the important topics: 
                    • Probability
                    • Statistics
                    • Statistical models
                    • Machine Learning
                    • Understanding neural networks

                  • Conferences - To expand your professional connections and build your network, conferences and meetups serve as great avenues.
                  • Competitions - Taking part in competitions offer you opportunities to implement and test your knowledge as well as brush up your skills.
                  • Referral - With data science scientist being a high profile job, references turn out to be the primary source of recruitment for top-notch companies. For this, you should make sure that your professional accounts are up to date and that you invest time in building a network.
                  • Interview - Interviews are an indispensable part of the recruitment process. You should not see them as something intimidating but as a chance to get to know your company better.

                  Major responsibilities of a data scientist include discovering patterns and creating useful information from vast amounts of undefined data to meet business goals of an organization.

                  In the modern business environment, it means generating insights from data every day. Thus, the role of a data scientist becomes all the more important. Collected data is essentially a gold mine of ideas and it takes an exceptional data scientist to identify and fabricate them. He or she is responsible to make the best of the data at hand. Key responsibilities include:

                  • Fetching relevant data from the unstructured mass of data after recognizing what is most beneficial for the company.
                  • Organize as well as analyze the data extracted from the unstructured pile.
                  • Creation and implementation of machine learning techniques and programs to analyze the data.
                  • Perform statistical analysis to predict future outcomes.

                  Data Scientist has been declared the sexiest job of the 21st century by Harvard Business Review in 2012. It entails lucrative job packages due to high demand and limited supply, with base salaries being as much as 36% higher than other predictive analytics professionals. Your basic salary as a data scientist would depend on 2 things:

                  • Type of company
                    • Startups: Highest pay 
                    • Public: Medium pay 
                    • Governmental & Education sector: Lowest pay 
                  • Roles and responsibilities
                    • Data scientist: £ 7,462/yr
                    • Data analyst: £ 4,650/yr
                    • Database Administrator: £ 7,450/yr

                  A data scientist is a mathematician, a computer scientist and a trend spotter all in one. His or her job is to decipher vast volumes of data and pick the relevant parts, analyze them and make predictions for the future. A career path for a data scientist can be explained as follows:

                  • Business Intelligence Analyst - A person who figures out both the business trends as well as what works best for his or her company. A clear picture needs to be drawn to figure where exactly the business stands in its environment.
                  • Data Mining Engineer - The role includes extracting and examining the data not just for his or her business establishment, but also for the third party. Then they create algorithms for further analysis.
                  • Data Architect - A data architect works with system designers and developers to create blueprints to be used by data management systems. This helps create and maintain data sources to be used efficiently.
                  • Data Scientist - A data scientist works a business case by analyzing, developing a hypothesis and developing an understanding of data so as to ensure maximum exploration. Developing algorithms and systems to make the best use of such data in a productive manner is also the job of a data scientist.
                  • Senior Data Scientist - This high profile job deals with the anticipation of business needs and curating projects for the same.

                  Referring to one another is the most effective and personal way to build a network with fellow scientists. Other ways include:

                  • Conferences
                  • Online platforms like LinkedIn
                  • Social gatherings like Meetups

                  Following are the top 8 career opportunities to look forward to in 2019 in the field of data science in London:

                  1. Data Scientist
                  2. Data Architect
                  3. Data Administrator
                  4. Data Analyst
                  5. Business Analyst
                  6. Marketing Analyst
                  7. Data/Analytics Manager
                  8. Business Intelligence Manager

                  London is home to many leading universities, such as University of London, King’s College, Birkberk, London South Bank University, etc. which offer prominent courses in Data Science. The following are the key points employers look for when employing data scientists:

                  • Education - Data Scientists have more PhDs than any other job titles. So getting a degree certainly helps your resume.
                  • Programming - Make sure you get the basics of Python right before moving to other data science libraries as it is the most common programming languages in this field.
                  • Machine learning - Machine learning skills are a must as they help you implement your textbook knowledge to real-life situations and come up with crafty solutions.
                  • Projects - Getting engaged with real-world projects helps you keep your skills up-to-date  and also learn more with each project.

                  Data Science with Python London, UK

                  Python is preferred by most data scientist because of the following reasons:

                  • Multi-paradigm programming language - The various facets of Python are aptly suited for the field of data science. It contains several libraries for your perusal, is structured and object-oriented to offer a smooth experience for the end user.
                  • Simplicity and readability - Analytical data is hard to process. A simple programming language makes the process easier. Both the libraries and the packages are tailor-made for the use by the end user.
                  • A broad and diverse range of resources - available at the disposal of the user also makes Python the language of their choice. Troubleshooting becomes very easy because of this feature.
                  • Vast community - A benefit that isn’t inherent to the language itself, but a wonderful byproduct of being a user. As there are millions of developers working on the same problems while using Python as their language of choice, they automatically become part of a wider community that they can easily reach out to whenever they need to. It’s very easy to find solutions for your problems this way as well as network at the same time.

                  Data Science is a huge field that involves various libraries and tools being in use at the same time. The following are the 5 most common languages used in data science:

                  • R - Even though it has a steep learning curve, it offers a bunch of advantages:
                    • A big open source community that helps you get rich open source packages.
                    • Plenty of statistical functions that handle matrix operations well.
                    • A great data visualization tool in ggplot2.
                  • Python - Though it has fewer packages to offer when compared to R, Python is still one of the most sought after languages in the world of Data Science.
                    • Pandas, scikit-learn, and tensorflow provide most of the libraries you need when working your projects.
                    • Very easy to learn and implement.
                    • Big open source community that guarantees a solution to any problem you might face.
                  • SQL - A structured-query language that is mostly used for relational databases.
                    • Readable syntax.
                    • Very easy to update, manipulate and query relational databases.
                  • Java - With fewer libraries to boot and Java's verbosity limiting its potential, it still has many benefits to offer.
                    • Because of so many existing systems being coded in java at the backend, it becomes easier to integrate java data science projects to it.
                    • High performance, compiled language.
                  • Scala - It is a preferred language in data science domain despite running on JVM and having a complex syntax.
                    • Any Scala program can easily run on Java as well because it itself runs on JVM.
                    • Offers high-performance cluster programming when paired with Apache Spark.

                  Follow these steps to successfully install Python 3 on your windows:

                  • Go to the download page and set up the Python program on your windows via GUI installer.
                  • Open it and select the checkbox at the bottom asking you to add Python 3.x to PATH. It will allow you to use Python’s functionalities from the terminal.

                  Or you can also install Python via Anaconda.

                  Note: You can also install virtualenv to your computer to create isolated Python environments and pipenv - a Python dependency manager.

                  You can download and install Python 3 from the official website by using a .dmg package. However, we recommend using Homebrew to install python along with its dependencies. To install python 3 on Mac OS X, follow these 3 steps:

                  • Install xcode: To install brew, type in the following command: 

                  $ xcode-select --install

                  • Install brew: Install the package manager for Apple, Homebrew, using the following command: 

                  /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)" To confirm that it is installed, type: brew doctor

                  • Install python 3: Install the latest version of Python and type: 

                  brew install python

                  • To confirm its version, use: 

                  Python --version

                  We recommend that you also install virtualenv, which will help you in creating isolated places to help run different projects. It will also be helpful when using different Python versions.

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                  I would like to extend my appreciation for the support given throughout the training. My trainer was very knowledgeable and I liked the way of teaching. The hands-on sessions helped us understand the concepts thoroughly. Thanks to Knowledgehut.

                  Ike Cabilio

                  Web Developer.
                  Attended Certified ScrumMaster (CSM)® workshop in May 2018

                  FAQs

                  The Course

                  Python is a rapidly growing high-level programming language which enables clear programs on small and large scales. Its advantage over other programming languages such as R is in its smooth learning curve, easy readability and easy to understand syntax. With the right training Python can be mastered quick enough and in this age where there is a need to extract relevant information from tons of Big Data, learning to use Python for data extraction is a great career choice.

                   Our course will introduce you to all the fundamentals of Python and on course completion you will know how to use it competently for data research and analysis. Payscale.com puts the median salary for a data scientist with Python skills at close to $100,000; a figure that is sure to grow in leaps and bounds in the next few years as demand for Python experts continues to rise.

                  • Get advanced knowledge of data science and how to use them in real life business
                  • Understand the statistics and probability of Data science
                  • Get an understanding of data collection, data mining and machine learning
                  • Learn tools like Python

                  By the end of this course, you would have gained knowledge on the use of data science techniques and the Python language to build applications on data statistics. This will help you land jobs as a data analyst.

                  Tools and Technologies used for this course are

                  • Python
                  • MS Excel

                  There are no restrictions but participants would benefit if they have basic programming knowledge and familiarity with statistics.

                  On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.

                  Your instructors are Python and data science experts who have years of industry experience. 

                  Finance Related

                  Any registration canceled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of a written request for refund. Kindly go through our Refund Policy for more details.

                  KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.

                  The Remote Experience

                  In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.

                  Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor

                  Have More Questions?

                  Data Science with Python Certification Course in London

                  This dynamite of a city with its unique blend of modern and the traditional is a complete package and has something for everyone. Nothing from its history to culture and art to architecture will ever disappoint anyone. While central London is all about major museums, galleries, art houses, mansions, and some of the most iconic sights around the world, the outskirts of the city are dotted with the most charming parks, botanical gardens, and walkways. Being the largest economy in Europe, London is home to all major corporations of the world. From technology to transport and fashion to finance, the city is headquarters to such powers as the Bank of England, London Stock Exchange, Lloyd?s of London, British Airways, GSK, PricewaterhouseCoopers, Shell and many more. This is a perfect place to start your career and KnowledgeHut helps you all the way by offering globally recognized courses such as PRINCE2, PMP, PMI-ACP, CSM, CEH and others. Note: Please note that the actual venue may change according to convenience, and will be communicated after the registration.