Data Science with Python Training in London, United Kingdom

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.

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.

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.

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. 

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: 1 
• 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 

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.

Here are some of the top organisations for data scientists in London:

• Urban Traffic Data Hackathon -TfL Datasets
• Live Webinar: Big Data & Hadoop 2.x using Hive
• NoSQL Data Modeling. Recommender w/ Scala, Spark & Spark Notebook
• Data Science @Twitter: Deep Learning, Machine Vision & Social Dynamics

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 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.