Course summary

The “Introduction to Data Science with Python” is a streamlined and hands-on 2-day open-to-public instructor-led course which is designed to equip you with fundamentals of Python programming language and to enable you to use it for data analysis purposes in a matter of days.

This remotely-delivered training course covers essential topics of data import/export, most common data structures used during data analysis processes, data wrangling methods and approaches, examples of publication-ready data visualisations, exploratory data analysis e.g. calculating descriptive statistics, pivot tables, cross-tabulations and data aggregations, as well as a selection of hypothesis testing statistical methods including implementations of parametric tests of differences and relationships, and an example of multivariate linear regression. During this course you will use native Python language and a number of industry-standard Python libraries for data science e.g. NumPy, pandas, SciPy, statsmodels, scikit-learn, pingouin, matplotlib, and seaborn.

This course also includes a 1-month follow-up period during which you are encouraged to ask questions directly to the tutor (via email) e.g. to advise you on methods or specific Python functions which may be suitable in your analysis.

Who is this course for?

This course is recommended to anyone interested in data science and Python programming language who is looking for a hands-on, streamlined, structured and tutor-led online training.

The course will be particularly of interest to the following groups and categories of attendees:

• social and operational researchers, social scientists with psychology, social sciences, medical science, biomedical science and similar background,
• statisticians and data scientists who would like to add Python programming language and its libraries to their data science toolkit,
• undergraduate and postgraduate students, post-docs and more senior academic researchers willing to learn Python for their projects,
• business and risk analysts, marketing analysts and data analysts requiring an elementary training in data science with Python,
• data engineers and developers who wish to learn Python in order to implement data science processes in their applications and, potentially, build their capabilities to understand machine learning and AI.

What is included in the course fee?

At Mind Project, we pride ourselves for delivering great quality, goal-directed, interactive and cost-effective training. Our remote, hybrid, and on-premises tutor-led courses are designed to maximise your opportunity to learn complex topics in shortest possible time while being able to discuss and interact with our tutor and other fellow attendees. Therefore, the registration fee for this course includes a number of benefits that many other training providers don’t offer:

• a remotely delivered live training with our expert tutor over 2 days (from 9:30 until 16:30 on both days) – you will be able to ask questions, interact and discuss the topic with other attendees from the comfort of your home/office,
• you will have access to course online materials for 1 year via our Mind Project Learning Platform e.g. course video recordings, exercises and quizzes, Python code scripts used during tutorials and live sessions, example datasets, optional and mandatory reading (e.g. blog articles, academic papers, industry reports), and other external recommended resources e.g. online books,
• one-month post-course follow-up period during which you will be able to ask questions to our tutor related to the material presented during the course and receive our recommendations of Python methods/functions suitable for your analysis and data,
• exercises and tasks completed by you during the live sessions will help you in better material retention and will enhance your learning progress,
• small group size allows easy interaction and stimulating environment for successful learning,
• you will receive a certificate of course completion for attending this course.

Programme outline

During this course, you will learn a number of data science approaches for data wrangling, exploratory analysis, visualisations and statistics with the Python programming language. The course will be structured in four parts (each part will be followed with a short exercise) and will be run according to the following schedule:

Part 1: First steps with Python language and data import/export

• Introduction to Python language, Spyder IDE and Jupyter Notebooks, and the ecosystem of libraries in Python,
• Importing/exporting data,
• A gentle introduction to data structures in Python.

Part 2: Data wrangling and describing data with Python

• Essential data wrangling operations: e.g. subsetting, filtering, renaming variables, recoding values and creating new data,
• Measures of central tendency, dispersion/variability and other basic descriptive and summary statistics,
• Value counts, cross-tabulations and data aggregations with pandas.

Part 3: Data visualisations with Python

• Plotting descriptives matplotlib and seaborn libraries: examples of different static plots/charts,
• Creating a publication-ready plots – adjusting elements of a plot and its graphical settings,
• Examples of more advanced plots e.g. grouped and aggregated plots, grid layouts and themes with seaborn and associated Python libraries.

Part 4: Inferential statistics and hypothesis testing with Python

• Examples of parametric tests of differences and relationships,
• Implementing multiple linear regression with Python – understanding regression metrics, model evaluation, comparing regression models,
• Final comments and questions.