Python for Data Science and Statistical Computing (PYDS01)
Learn Python for data science and statistical computing. Build skills in NumPy, Pandas and visualisation across two days of hands-on training for researchers and analysts.
Duration: 2 Days, 6 hours per day
Next Date: April 7-8, 2026
Format: Live Online Format
UK (GMT+1) local time - All sessions will be recorded and made available to ensure accessibility for attendees across different time zones."
£300Registration Fee
Register NowLike what you see? Click and share!
5.0
from 200+ reviews
- Overview
- Instructors
- Schedule
Course Description
This two-day workshop provides a comprehensive introduction to Python for researchers and data analysts who are new to Python but need to perform data analysis and statistical computing. The course combines Python programming fundamentals with hands-on training in the core Python data science stack. Day 1 covers essential Python programming: installing Python and IDEs, elementary data types and collections (lists, dictionaries, tuples, sets), control flow and loops, functions and comprehensions. Day 2 focuses on the scientific computing ecosystem: numerical computing with NumPy (arrays, operations, indexing, linear algebra), data processing with Pandas (DataFrames, filtering, groupby, aggregations), and data visualization (Matplotlib, Seaborn, plotnine). The course includes an introduction to statistical analysis with statsmodels. Through interactive Jupyter notebooks and real datasets, participants develop practical skills for transitioning to Python from other data analysis tools. This course provides the foundation needed for pursuing machine learning and deep learning with Python.
What You’ll Learn
During the course we will cover the following:
- Install and configure Python environments using virtual environments, pip, and conda for data science work.
- Work with Python’s elementary data types (integers, floats, booleans, strings) and perform operations on them.
- Use Python collections effectively: lists for ordered data, dictionaries for key-value mappings, tuples for immutable sequences, and sets for unique elements.
- Write programs using control flow (if/elif/else), loops (for/while), and loop control statements.
- Create and use functions with parameters, default arguments, and return values.
- Apply list, dictionary, and set comprehensions for concise data transformations.
- Create and manipulate NumPy arrays, understanding their performance advantages over Python lists (often 100-200x faster for numerical operations).
- Perform array operations including broadcasting, reshaping, boolean indexing, and linear algebra computations.
- Load, filter, and manipulate datasets using Pandas DataFrames and Series.
- Select and filter data using loc/iloc, boolean indexing, and column operations.
- Perform split-apply-combine operations with groupby for data aggregation and summary statistics.
- Create statistical visualizations using Matplotlib (low-level control), Seaborn (statistical plots), and plotnine (grammar of graphics).
- Fit basic statistical models using statsmodels and interpret results.
- Navigate the Python data science ecosystem and know where to find additional packages and resources.
- Read, understand, and modify Python code written for data analysis.
Course Format
Interactive Learning Format
Each day features a well-balanced combination of lectures and hands-on practical exercises, with dedicated time for discussing participants’ own data, time permitting.
Global Accessibility
All live sessions are recorded and made available on the same day, ensuring accessibility for participants across different time zones.
Collaborative Discussions
Open discussion sessions provide an opportunity for participants to explore specific research questions and engage with instructors and peers.
Comprehensive Course Materials
All code, datasets, and presentation slides used during the course will be shared with participants by the instructor.
Personalized Data Engagement
Participants are encouraged to bring their own data for discussion and practical application during the course.
Post-Course Support
Participants will receive continued support via email for 30 days following the course, along with on-demand access to session recordings for the same period.
Who Should Attend / Intended Audiences
This course is designed for researchers, data analysts, and professionals who are new to Python and want to use it for data analysis, statistical computing, and scientific computing. It is ideal for those who currently work with tools such as R, MATLAB, Excel, or SPSS and want to transition their workflows to Python, as well as for anyone planning to progress into machine learning or deep learning. No prior Python experience is required, and all programming skills will be taught from the ground up. Participants are expected to have basic computer literacy, including the ability to install software and manage files, along with a general understanding of common data concepts such as variables, datasets, and simple descriptive statistics. Experience with any data-related work, whether through research, spreadsheets, or statistical software, is sufficient preparation for the course.
Equipment and Software requirements
A laptop or desktop computer with a functioning installation of Python is required. Python is a free, open-source software compatible with Windows, macOS, and Linux systems.
A working webcam is recommended to support interactive elements of the course. We encourage participants to keep their cameras on during live Zoom sessions to foster a more engaging and collaborative environment.
While not essential, using a large monitor—or ideally a dual-monitor setup—can significantly enhance your learning experience by allowing you to view course materials and work in R simultaneously.
All necessary Python packages will be introduced and installed during the workshop. A comprehensive list of required packages will also be shared with participants ahead of the course to allow for optional pre-installation.
Dr. Mark Andrews
Mark is a psychologist and statistician whose work lies at the intersection of cognitive science, Bayesian data analysis, and applied statistics. His research focuses on developing and testing Bayesian models of human cognition, with a particular emphasis on language processing and memory. He also works extensively on the theory and application of Bayesian statistical methods in the social and behavioural sciences, bridging methodological advances with real-world research challenges.
Since 2015, Mark has co-led a programme of intensive workshops on Bayesian data analysis for social scientists, funded by the UK’s Economic and Social Research Council (ESRC). These workshops have trained hundreds of researchers in the practical application of Bayesian methods, particularly through R and modern statistical packages.
Education & Career
• PhD in Psychology, Cornell University, New York (Cognitive Science, Bayesian Models of Cognition)
• MA in Psychology, Cornell University, New York
• BA (Hons) in Psychology, National University of Ireland
• Senior Lecturer in Psychology, Nottingham Trent University, England
Research Focus
Mark’s work centres on:
• Bayesian models of human cognition, especially in language processing and memory
• General Bayesian data analysis methods for the social and behavioural sciences
• Comparative studies of Bayesian vs. classical approaches to inference and model comparison
• Promoting reproducibility and transparent statistical practice in psychological research
Current Projects
• Developing Bayesian cognitive models of memory and linguistic comprehension
• Exploring Bayesian approaches to regression, multilevel, and mixed-effects models in psychology and social science research
• Co-leading ESRC-funded workshops on Bayesian data analysis for applied researchers
Professional Consultancy & Teaching
Mark provides expert training and advice in Bayesian data analysis for academic and applied research projects. His teaching portfolio includes courses and workshops on:
• Bayesian linear and generalized linear models
• Multilevel and mixed-effects models
• Cognitive modelling with Bayesian methods
• Applied statistics in R for psychologists and social scientists
He is also an advocate of open science and is experienced in communicating complex statistical methods to diverse audiences.
Teaching & Skills
• Instructor in Bayesian statistics, time series modelling, and machine learning
• Strong advocate for reproducibility, open-source tools, and accessible education
• Skilled in R, Stan, JAGS, and statistical computing for large datasets
• Experienced mentor and workshop leader at all academic levels
Links
• University Profile
• Personal Page
• ResearchGate
Session 1 – 02:00:00 – Getting Started with Python
This session introduces participants to the Python programming environment and elementary data types. We begin with practical matters: installing Python, setting up virtual environments with pip and conda, and choosing an Integrated Development Environment (Jupyter notebooks, Spyder, PyCharm, or Positron). The session then covers Python’s elementary data types including integers, floats, booleans, and strings. Participants learn string operations, methods, and indexing through hands-on practice. The interactive interpreter is introduced as a tool for experimentation and learning. All participants will have a working Python environment by the end of this session
Break – 01:00:00
Session 2 – 01:00:00 – Collections and Data Structures
This session introduces Python’s built-in collection types, which are essential for organizing and manipulating data. We explore lists (ordered, mutable sequences) including creation, indexing, slicing, methods, and nested lists. Dictionaries are introduced for key-value data with iteration and common methods. Tuples provide immutable sequences with unpacking and practical use cases. Sets handle unique elements with mathematical set operations. The session emphasizes choosing the appropriate data structure for different tasks and converting between types. Practical exercises use real data examples to demonstrate when each collection type is most appropriate.
Break – 01:00:00
Session 3 – 01:00:00 – Programming Essentials
This session covers core programming constructs needed for data analysis workflows. We begin with control flow using if/elif/else conditionals for decision-making. Loops are introduced for iteration: for loops for iterating over sequences and while loops for condition-based repetition, with loop control using break and continue. The session emphasizes Pythonic approaches including list comprehensions, dictionary comprehensions, and set comprehensions for concise data transformations. Functions are covered in depth: defining functions, parameters, default arguments, return values, and lambda functions for simple operations. The enumerate and zip functions are introduced for elegant iteration patterns. Hands-on exercises build practical programming skills through data manipulation tasks.
Session 7 – 02:00:00 – Numerical Computing with NumPy
This session introduces NumPy, the foundation of Python’s scientific computing stack. We begin by demonstrating why NumPy matters: performance comparisons show NumPy arrays are often 100-200x faster than Python lists for numerical operations. Participants learn to create arrays from lists, use different data types (int32, float64, etc.), and understand the advantages of homogeneous, contiguous memory layout. Array operations are covered comprehensively: element-wise operations, broadcasting rules, aggregations (sum, mean, max, min), and statistical functions. Indexing and slicing techniques include boolean indexing for filtering, fancy indexing with arrays, and reshaping operations. The session concludes with linear algebra operations including dot products and matrix multiplication. All concepts are demonstrated with scientific computing examples relevant to data analysis.
Break – 01:00:00
Session 8 – 02:00:00 – Data Processing with Pandas
This session introduces Pandas, Python’s primary library for tabular data manipulation. We begin with DataFrames and Series as the fundamental data structures, creating them from CSV files, dictionaries, and NumPy arrays. The session covers essential data manipulation operations: selecting columns and rows using various methods, filtering data with boolean indexing, using loc and iloc for explicit indexing. Data manipulation techniques include adding computed columns, sorting, and handling missing data. The powerful groupby operation is covered in detail, demonstrating the split-apply-combine paradigm for aggregations and transformations. Participants learn to compute summary statistics, perform group-wise operations, and reshape data for analysis. Practical exercises use real research datasets to develop data wrangling workflows typical of scientific data analysis.
Break – 01:00:00
Session 9 – 02:00:00 – Visualization and Statistical Analysis
This session covers data visualization and introduces statistical modeling in Python. We begin with Matplotlib for creating customizable plots with low-level control over figure elements. Seaborn is introduced for high-level statistical visualizations including distributions (histograms, KDE plots), relationships (scatter plots, regression plots), and categorical data (box plots, violin plots). The plotnine library demonstrates the grammar of graphics approach familiar to R users, allowing declarative plot construction. The session then introduces statsmodels for statistical analysis, covering basic linear regression, model fitting, and interpreting results including coefficients, p-values, and R-squared. We also cover modules and packages: importing modules, creating custom modules, and navigating Python’s package ecosystem. The session concludes with an overview of additional data science packages (scikit-learn for machine learning, scipy for scientific computing) and resources for continued learning. Participants learn to create publication-quality visualizations and perform basic statistical analyses typical of empirical research.
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Frequently asked questions
Everything you need to know about the product and billing.
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
Still have questions?
Can’t find the answer you’re looking for? Please chat to our friendly team.
