Mind Project

Data Science Training and Big Data Predictive Analytics

Python for Data Analysis – London – March 2019

Applied Data Science with Python

Python for Data Analysis – London – March 2019

£675.00 – £825.00

When? 6th-8th of March 2019

Where? London Institute of Banking & Finance, 8th Floor, Peninsular House, 36 Monument Street, London, EC3R 8LJ, United Kingdom

Course type? 3-day open-to-public training course.

Deadline for registrations? Friday, 1st of March 2019 at 16:00 London (UK) time

SKU: N/A Category: Tags: , , , , , , , , , , , , , , ,

The “Python for Data Analysis” course will introduce you to all most essential and practical applications of Python programming language for data wrangling, management, analysis and basic visualisations. The course will provide you with practical skills in general Python programming language for data science purposes and a number of Python’s libraries specifically designed for scientific computing and data analysis e.g. NumPy, pandas, matplotlib, IPython, SciPy etc.

The course is suitable for data and insights analysts/scientists, data engineers and data product developers who are responsible for pre-processing of data, analytics and reporting of findings.

The course covers a variety of topics related to data processing and analysis using Python language including standard Python data structures and other data objects used for scientific and statistical computing available in NumPy (multi-dimensional arrays) and pandas (Series, DataFrame) libraries, importing/exporting data from various file formats (Excel spreadsheets, csv, tab, txt etc.), basic and more advanced data transformations and essential data wrangling techniques, summaries, data aggregations, cross-tabulations, frequency and pivot tables, simple graphical representations of the data (bar plots, histograms, box plots etc.) using matplotlib and seaborn libraries, introduction to hypothesis testing with correlations, t-tests and essentials of predictive modelling using multiple linear regression methods with SciPy, statsmodels and scikit-learn packages.  


 

Programme outline

The course will run for three days (Wednesday to Friday) between 9:30am and 5:00pm and will consist of alternating lecture-style presentations and practical tutorials. The example datasets used during tutorial sessions will come from social sciences, psychology, business and finance fields, however the contents may vary depending on specific interests of participants (based on the Participant’s Skills Inventory). There will be two 15-minute coffee/tea breaks and one 1-hour lunch break on each day of the course.

The programme for this course covers the following concepts and topics:

    • Use Python’s Anaconda distribution and its integrated development environment Spyder with Jupyter Notebooks to manage, develop and share a Python analytics project,

    • Understand and differentiate between a variety of data structures within the core Python language as well as a highly-efficient and optimised data structures from NumPy and pandas libraries,

    • Perform basic mathematical and more advanced control flow operations,

    • Import and export data from/to various data file formats e.g. Excel spreadsheets, CSV, tab-delimited, text files, and also SQL databases,

    • Prepare, transform and manage datasets and their variables, add/delete rows, create samples and subsets, identify specific cases based on conditional search, sort cases, add/edit value and variable labels, deal with missing data, standardise, normalise and reshape data, merge datasets and use joins,

    • Carry out an extensive Exploratory Data Analysis (EDA): inspect the structure of datasets and their variables, calculate cross-tabulations and descriptive statistics to summarise the data e.g. pivot tables, summary tables and data aggregations,

    • Introduction to EDA plotting and graphical visualisations: histograms, density plots, scatterplots, box plots, bar plots, line graphs etc.,

    • Perform simple hypothesis testing and inference statistics: tests of differences and correlations. Run tests for normality assumptions, t-tests, analyses of variance (ANOVA), correlations and simple regressions,

    • Carry out data modelling tasks using multiple linear and logistic regressions.

What is included?

Apart from the contents of the course, Mind Project will provide you with the following:

  • printed course pack with all presentation slides, cheatsheets and other essential course information,

  • digital (USB memory stick) Course Manual including all presentation slides, Python course code scripts (Jupyter notebooks) and a list of reference books and online resources,

  • additional home exercises and all data sets available to download,

  • stimulating, friendly and inclusive learning environment in a small group (typically 10-14 attendees) led by experienced and energetic tutors and course leaders,

  • modern and comfortable training venue located in the heart of City of London – at the London Institute of Banking & Finance, next to the Monument underground station,

  • refreshments and a light, energising lunch on each day of the course,

  • Wi-Fi access,

  • networking opportunity,

  • Mind Project course attendance certificate.

Further instructions

  • In order to benefit from the course, we recommend that all attendees have the most recent version of Anaconda distribution of Python (by Continuum Analytics) installed on their laptops (any operating system). As Anaconda’s Python is a free and fully-supported distribution you can download it directly from https://www.continuum.io/downloads. Please contact us should you have any questions or issues with the installation process.

  • No prior knowledge of Python and its libraries is required from participants enrolling on this course, however a keen interest in data analysis and some experience with data processing is assumed.

  • Attendees are encouraged to complete the online Participant’s Skills Inventory to allow Mind Project and our course tutors to customise the contents of the course depending on the level of attendees’ knowledge and their areas of interest. The data obtained through the Participant’s Skills Inventory will be held fully-confidential and will only be used to provide a quality data analysis training.

  • By purchasing a place on one of our courses you agree to the Terms and Conditions. Please read the Terms and Conditions before making a booking.

  • The deadline for registrations on this training course is Friday, 1st of March 2019 at 16:00 London (UK) time. Mind Project reserves the right to end the registration process earlier if all places are booked before the deadline.

Discounts and multiple bookings

Apart from discounted fees for students or employees of charitable organisations, we are able to offer further discounts on the overall cost of your training if you wish to attend multiple related courses or enrol several delegates on this specific course. Please note that this offer is only available through our website.

Please note that the discounts are calculated automatically when tickets are added to the Cart. For bookings of 6 and more delegates on one course, we recommend that you contact us directly – we may be able to arrange a separate course just for you at our office or your own premises at a discounted rate.

 

Arrange this course at your premises

This open-to-public course is a shortened and more general version of our fully-customisable in-house training course “Applied Data Science with Python”. If your delegates cannot attend this public course, or you are interested in arranging this training course at your premises or simply you need a bespoke, made-to-measure training solution, please visit this page and press Ask For Quote button to enquire about and request a quote for the in-house version of this course based on your specific needs and desired outcomes of the training.

You may also email us directly at info(at)mindproject.io and include the following information in your enquiry:

  • contact details to a person who should receive the quote,

  • number of delegates you would like to train,

  • approximate number of days (or half-days) you would like to arrange the course for (including additional support/project guidance if needed),

  • location of the training venue,

  • any details on course customisation or specific topics you would like the course to address – most importantly, please indicate desired outcomes of the course if different then presented above,

  • any other questions you may have.

Course location

This course will be held at the London Institute of Banking & Finance, 8th Floor, Peninsular House, 36 Monument Street, London, EC3R 8LJ, United Kingdom.
Please see the map below.

Python for Data Analysis – London – March 2019

What did previous delegates say about our courses?

“A very clear and practical overview of R and how it can be used in real world data sets. Learning from an expert!” - G's Group Holdings - Applied Data Science with R
“Excellent course for beginners and not-so-beginners in data analysis with Python. Simon brings real-world experience to the table and is a patient, knowledgeable and personable instructor.” - Roxana Lupu, Medallia - Applied Data Science with Python
“Thank you for sharing great knowledge with us which will be used for and will contribute a lot towards our business.” - Cabot Financial Ltd - Machine Learning with R
“Highly recommended, best place to start for machine learning with R!” - Max Fennelly, Pearson Ham - Machine Learning with R
"Great introduction to Hadoop from a clearly knowledgable demonstrator." - Feargal Egan, G-Research - Introduction to Hadoop with Java
“Simon is very knowledgeable and provided lots of solutions to consider in dealing with Big Data.” - Aidan Mews, Ministry of Justice - Big Data Methods with R
“The course has given me a greater understanding of Python and its capabilities.” - Mike Watts, Adactus Housing - Applied Data Science with Python
“This course was a great way to get an introduction to R and how it can be used to perform statistical analysis and run data visualisations.” - Daria Kuznetsova, Director of Strategy and Research at Teach First - Applied Data Science with R
“A really good, hands-on introduction that left me aware of and confident to use Hadoop stack. The practical writing of MapReduce and querying Hive was extremely useful.” - Chris Mainey, University College London & University Hospital Birmingham - Introduction to Hadoop with Java
“This course teaches you everything you need to know about the basic operations of R and useful exploratory data analysis tools. The content is both challenging and engaging, and the trainer is extremely supportive.” - Thomas Weeks, NHS Improvement - Applied Data Science with R
“The Machine Learning with R course by Mind Project provides a fantastic overview of supervised and unsupervised ML models and their applications. The course offers details on important topics such as advanced model evaluation metrics which are not emphasised on other ML courses.” - James Patten, Deloitte UK - Machine Learning with R
“I don't think I will ever open Excel again. The course has confirmed my perception of the applicability of R in the NHS.” - Paul Stroner, Deputy Director of Information and Analytics, NHS - Applied Data Science with R
“Really enjoyed the course! Simon had a great way of taking us from simple R topics to more complex Hadoop related concepts, which opened the window into Big Data methods.” - Sofia Diamanti, Deloitte UK - Big Data Methods with R
“This was a great course – very practical and taught in a relaxed style.” - Sophie Reid, Design Council - Applied Data Science with R
“The best organised data science course I have found so far.” - David Metcalfe, Doctoral Candidate, University of Oxford - Applied Data Science with Python
“Enjoyed the course. The tutor is in command of his subject and patient with his students.” - Dean Allsopp, DA Consulting - Machine Learning with R
“Simon is a great teacher – his workshops flow well and move at a good pace. I’ve really enjoyed both courses with Mind Project and I would definitely recommend them.” - Ellen Talbot, University of Liverpool - Machine Learning with R and Big Data Methods with R
“Excellent course for the intro to R, I highly recommend it.” - Dr Edward Barker, Institute of Psychiatry, King's College London - Applied Data Science with R
“The course provides a beginner enough knowledge to start analysis using R. I appreciated the instructor’s knowledge and the willingness to give as much explanation as possible.” - Prof. Sunil Sahadev, Salford Business School - Applied Data Science with R
“Informative and very useful, interactive course.” - Kuldeep Sohal, Bradford Institute for Health Research - Applied Data Science with R
“Course excellently laid out. Very rich content. It will provide material and food for thought for years!” - Ernesto Addiego-Guevara, Imperial College London - Applied Data Science with Python
“Simon was exceptional in conducting this course. The course covered a nice mixture of theory and practicals related to R, databases and Hadoop. It has really helped me to understand a lot of concepts and has motivated me to work on Hadoop.” - Sushant Chatufale, University of Portsmouth - Big Data Methods with R
“This is a great course, which I would recommend to anyone with an interest in data science no matter your ability.” - Clair Cooper, Durham University - Applied Data Science with R
“Great course, it covers a lot of material. Good balance between maths, theory, R and stats.” - Gordon Izatt - Machine Learning with R
“Expert tuition in Python and Machine Learning. I am now inspired and equipped to apply what I've learnt.” - John Taylor, TR Control Solutions Ltd - Machine Learning with Python
“This is a unique face-to-face course on data analysis using Python. Very rich learning experience in 3 days.” - Hamad Al Jassmi, Assistant Professor at UAE University - Applied Data Science with Python
“A great starter course. Exercises were provided so that we could practice and we could easily communicate with the course teacher.” - Iqqra Aziz, Get The Data - Applied Data Science with R
“Excellent and thorough intro to R, exceeded my expectations.” - Shahrooz Aliabadi, Lapinco Ltd - Applied Data Science with R
“Very interesting course. Really useful tutorials that progressed understanding of machine learning concepts.” - Talia Beech, Canadian Department of National Defence - Machine Learning with R
“The course provided me with a strong knowledge base to build upon.” - Tobias Koelsch, REWE Group - Machine Learning with Python
“Very informative, made me think of the potential Python holds in many fields. The trainer was excellent, he made everyone comfortable to contribute.” - Naeema Al Darmaki, UAE University - Applied Data Science with Python
“Excellent course. Very well structured and very professional. Simon catered for all abilities. Everyone gained a lot of information. Value for money.” - Dr. Noreen Quinn, Lecturer in Statistics, Cork Institute of Technology - Machine Learning with R
“Excellent course, a lot to cover and take back. Perfectly suited to analysts improving their skills in Python.” - Ben Amos, Argos - Applied Data Science with Python
"The course is very informative and Simon has been a great tutor. The ML steps he taught us is quite clear from setting up the python environment, running the scripts and analysis. He taught us new techniques in ML and how to do it properly." - Farraen Mohd Azmin Senior Project Engineer: Machine Learning Specialist - Machine Learning with Python
"Extremely useful introduction course to ML. It's really great to have the basics so clearly laid out, will definitely make use of the learnings and material in the future!" - Siem van Limpt, Senior Consultant, Element Energy - Machine Learning with Python
"The course provided an incredible overview of regression, clustering and classification methods. The amount of material we covered in three days was substantial, but Simon kept it clear, and the homeworks were a really helpful opportunity to bed in learning" - Harry Lloyd, Marine Data Scientist - Machine Learning with Python
Python for Data Analysis – London – March 2019