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 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, seaborn and plotnine libraries, introduction to hypothesis testing with correlations, t-tests and essentials of predictive modelling using multiple linear regression methods with SciPy, pingouin, statsmodels and scikit-learn packages.
Who is this course for?
This course is recommended for anyone interested in applied data science and Python language who is looking for a thorough, structured and tutor-led online training provided by a recognised and experienced organisation.
The course will be particularly of interest for the following groups and categories of learners:
social researchers and social scientists with psychology, social sciences, medical science, biomedical science and similar background,
statisticians and data scientists who would like to add Python to their set of data science tools,
undergraduate and postgraduate students,
business analysts, marketing analysts and data analysts requiring a thorough training in applied data science in Python.
This training course is tutor-led – all online tutorials are presented live by our expert instructor, you can ask questions, discuss the topic and interact with other learners,
In total, this course includes 15 hours of live teaching which equates to a 3-day classroom-based training course with exclusion of tea/coffee and lunch breaks,
Exercises and tasks completed by you between the live tutorial sessions will help you in better material retention and will enhance your learning progress,
Small group size (up to 15 learners) allows easy interaction and stimulating environment for successful learning,
Email supervision and support provided during the course period maximises learning outcomes and improves your learning experience,
You will have access to additional self-paced online learning materials e.g. tutorial videos, exercises and quizzes, Python code scripts used during tutorials, example datasets, optional and mandatory reading (e.g. blog articles, academic papers, industry reports), and other external recommended resources e.g. online books,
You will receive a course attendance certificate which can be upgraded to a course completion certificate upon successful submission of a short data analysis report (up to 2,000 words) and Python code scripts showing all your workings,
You will be encouraged to network with other learners enrolled on the course – you will automatically become a member of the course forum/message board where you can ask general course questions and you will maintain your learner’s profile in which you can share your bio and social media links.
Your course instructor
Your instructor for this course will be Simon Walkowiak. Simon is a director at Mind Project Limited and a Ph.D. researcher in Artificial Intelligence at the Bartlett Centre for Advanced Spatial Analysis (University College London) and the Alan Turing Institute in London. Simon holds BSc (First Class Honours) in Psychology with Neuroscience and MSc (Distinction) in Big Data Science. He conducts and manages research projects on implementation and computational optimisation of novel AI approaches applicable to large-scale datasets to predict human behaviour and spatial cognition. Simon is the author of “Big Data Analytics with R” (2016) – a widely used textbook on high-performance computing with R language and its compatibility with ecosystem of Big Data tools e.g. SQL/NoSQL databases, Spark, Hadoop etc. Apart from research and data management consultancy, during the past several years, Simon has taught at more than 150 in-house or open-to-public statistical training courses in the UK, Europe, Asia and USA. His major clients include organisations from finance and banking (HSBC, RBS, GE Capital, European Central Bank, Credit Suisse etc.), research and academia (GSMA, CERN, UK Data Archive, Agri-Food Biosciences Institute, Newcastle University etc.), health (NHS), and government (Home Office, Ministry of Justice, Government Actuary’s Department etc.).
This instructor-led course duration is planned over 6 teaching weeks (to qualify for the Course Attendance Certificate) plus an additional 1 calendar month for the completion of the data science project (to obtain the graded Course Completion Certificate).
In between the six weekly online live tutorials (2.5 hours long each) you will improve your skills working through set tasks and homework exercises which will require 4-6 hours of your time commitment per week (24-36 hours). We estimate that the total time commitment for the Course Attendance Certificate is 40-50 hours over 6 teaching weeks, and for the Course Completion Certificate it will equate to 70-80 hours (over 2.5-month period) including the project report writing time.
Start date: Wednesday, 28th of October 2020 @14:30 London (UK) time
Schedule of sessions: Every Wednesday at 14:30 London (UK) time for 6 weeks
Deadline for registrations: Monday, 26th of October 2020 @ 17:00 London (UK) time
Week 1: Principles of Python for data analysis
- Overview of Python scripting tools and IDEs: IPython, Spyder, PyCharm, Jupyter Notebooks,
- Introduction to Python language: built-in types, data structures, mathematical and logical operations,
- Multidimensional arrays in NumPy: features of ndarrays, basic methods and attributes, universal functions, broadcasting,
- Series and DataFrame in pandas: features of Series and DataFrames, basic methods and attributes,
- Data import/export to/from various file formats.
Week 2: Data wrangling with Python
- Working with Series and DataFrames in pandas,
- Converting data between different types and classes; creating and working with categorical data,
- Essential data wrangling operations in pandas: e.g. subsetting, filtering, renaming variables, recoding values and creating new data,
- Introduction to working with strings, dates and time stamps.
Week 3: Exploratory data analysis with Python
- Measures of central tendency, dispersion/variability and other basic descriptive and summary statistics,
- Value counts, cross-tabulations and data aggregations with pandas,
- Plotting descriptives with matplotlib and seaborn libraries: basic examples of bar plots, line graphs and boxplots,
- Grouped and aggregated plots; multiplots (multiple plots on the same page); additional graphical settings, grid layouts and themes of plots produced with matplotlib, seaborn, plotnine and other Python data visualisation libraries.
Week 4: Inferential statistics and hypothesis testing with Python – Part 1
- Understanding hypothesis testing and traditional test assumptions; introduction to probability distributions,
- Parametric tests of differences,
- Parametric tests of relationships,
- Power and effect size calculation for inferential tests.
Week 5: Inferential statistics and hypothesis testing with Python – Part 2
- Testing nominal variables,
- Non-parametric tests of differences,
- Non-parametric tests of relationships,
- Introduction to linear and non-linear models.
Week 6: Linear and non-linear models with Python
- Analysis of Variance (ANOVA),
- Main effects, random effects and interactions,
- Understanding multiple linear regression,
- Non-linearity in regression models.
Additionally, in order to receive the full Course Completion Certificate, you will have to submit a short data analysis report (up to 2,000 words) along with Python data processing and analysis scripts within one calendar month from the last day of Week 6. The project will be assessed and graded. You will also receive a formal written feedback about your project.
Course pre-requisites and further instructions
We recommend that all attendees have the most recent version of Anaconda Individual Edition of Python 3.8 (or at least Python 3.5) installed on their PCs (any operating system). Anaconda’s Python is a free and fully-supported distribution and you can download it directly from https://www.anaconda.com/products/individual#Downloads. Please contact us should you have any questions or issues with the installation process.
No prior knowledge of Python language is required from delegates enrolling on this course, however a keen interest in data analysis and some experience with data processing is assumed.
Your PC needs to be connected to a stable WiFi/Internet network (either home or office-based) and have Zoom video-conferencing application installed.
You will need at least one commonly used web browser installed on your PC (e.g. Chrome, Safari, Firefox, Edge etc.) to access our Mind Project Learning Platform.
The primary spoken and written language of the course is English.
You are encouraged to complete the online Learner’s Skills Inventory to allow Mind Project and our course tutors to customise the course teaching style depending on the level of attendees’ knowledge and their areas of interest. The data obtained through the Learner’s Skills Inventory will be held fully-confidential and will only be used to provide a quality statistical computing and data science training.
Discounts and multiple bookings
Early Bird Offer allows you to save up to 30% off the total course enrolment price (on top of other discounts). This offer is usually valid up until 3-4 weeks before the start of the course.
We offer 2 types of enrolment options:
- Regular Fee – full-priced enrolment for learners representing commercial organisations or self-funded individuals who do not meet our eligibility criteria for discounted rates (please see below),
- Discounted Fee – applicable to undergraduate and postgraduate students as well as representatives of registered charitable organisations and non-governmental organisations (NGOs) – this category also includes employees of the National Health Service (NHS).
Students and individuals eligible for the Discounted Fee should submit a copy of their student or organisation ID card (with their name and card expiry date visible) when making the purchase of their place on the course for the discount eligibility verification purposes. Alternatively, the discount eligibility can be verified by submitting either i.) a copy of a letter from the university registrar or student’s department confirming your status, or ii.) a copy of a letter from your employer (on a company letter-headed paper with a charity/NGO registration number) which confirms your current position within the organisation.
Apart from the Early Bird Offer and discounted fees for students or employees of charitable organisations and NGOs, 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.
- If you book 3 or 4 tickets on any of our tutor-led open-to-public online training courses, you will receive 5% discount on the total price of your booking.
- If you book 5 or more tickets on any of our tutor-led open-to-public online training courses, you will receive 10% discount on the total price of your booking.
All 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 your delegates at a discounted rate.
Arrange this course at your organisation
This open-to-public online course is a more generalised version of our fully-customisable in-house / online training course “Python for Data Analysis”. If your delegates cannot attend this public course, or you are interested in arranging this training course explicitly for your delegates (or at your premises) or simply you need a bespoke, made-to-measure training solution, please request a quote for the in-house version of this course based on your specific needs and desired outcomes of the training.
You may 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 online sessions (or half-days / full days for on-site in-house course) you would like to arrange the course for (including additional support/project guidance if needed),
location of the training venue if not online,
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.
If you don’t know the answers to questions above or you are at early stages of course planning, we would be happy to arrange an informal chat and help you choose the most suitable and budget-efficient option.