Python for Data Analysis - 6-week tutor-led online course

Description:

1. Course description.

The “Python for Data Analysis” open-to-public tutor-led six-week training course will introduce you to all most essential and practical applications of Python programming language for data wrangling, data management, analysis and fundamentals of graphical 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.), essential and more advanced data transformations and data wrangling techniques, summaries, data aggregations, cross-tabulations, frequency and pivot tables, 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.

A mixture of online pre-recorded instruction videos, weekly live group webinars, additional 1-2-1 check-ins (either via email or on Microsoft Teams) and several homework exercises throughout the duration of the course will ensure you will be able to apply R language to your own data and research questions in a matter of weeks.

 

2. Course structure and programme.

This instructor-led course is planned over six teaching weeks with an additional two-week follow-up period during which you will complete a small piece of work and receive a 1-2-1 feedback from our tutor. During the course, you will attend weekly live webinars (90 minutes each) with our tutors who will explain specific topics, answer your questions and discuss different Python programming language and data science problems.

In between the six weekly online live tutorials (90 minutes long each) you will improve your skills by watching our pre-recorded instruction video tutorials at the Mind Project Learning Platform and working through set tasks (e.g. quizzes) as well as homework coding exercises which will require 4-6 hours of your time commitment per week. We estimate that the total time commitment is 40-50 hours over 6 teaching weeks.

During the course, you will also have weekly 1-2-1 check-ins (either via email or as a 15-minute Microsoft Teams call) with our tutor to supervise your progress and answer your questions.

This training course is tutor-led – online tutorials are presented live by our expert instructor, you can ask questions, discuss the topic and interact with other learners. You can also email the tutor during and after the course if you have any questions related to the material presented during the course.

The course will be recorded – you will have access to the pre-recorded video tutorials, recordings of the course live webinars and additional resources such as datasets, Python code, academic papers and other publications related to the topics of the course, as well as essential and supplementary coding exercises via Mind Project Learning Platform.

Start date: Friday, 3rd of November 2023, 1:00 pm London (UK) time

Schedule of live sessions: Every Friday at 1:00 pm London (UK) time for 6 weeks

Deadline for registrations: Wednesday, 1st of November 2023 @ 17:00 London (UK) time

 

During this course, you will learn a variety of data science approaches for data wrangling, exploratory analysis, visualisations and statistics with Python programming language. The course will be run according to the following schedule:

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,

• Working with Series and DataFrames in pandas,

• Converting data between different types and classes; creating and working with categorical data.

Week 2: Data wrangling and describing data with Python

• 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,

• Measures of central tendency, dispersion/variability and other basic descriptive and summary statistics,

• Value counts, cross-tabulations and data aggregations with pandas.

Week 3: Data visualisations with Python

• From data summaries, exploratory data analysis to visualisations,

• Plotting descriptives with matplotlib and seaborn libraries: examples of bar plots, line graphs and boxplots,

• Introduction to faceting – grouped and aggregated plots, and 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 e.g. normality and homogeneity of variances,

• Parametric and non-parametric tests of differences,

• Power and effect size calculation for inferential tests of differences.

Week 5: Inferential statistics and hypothesis testing with Python – Part 2

• Parametric and non-parametric tests of relationships,

• Introduction to linear and non-linear models,

• Analysis of Variance (ANOVA),

• Main effects, random effects and interactions.

Week 6: Linear and non-linear models with Python

• Understanding multiple linear regression,

• Regression metrics and evaluation of multiple linear regression models,

• Non-linearity in regression models,

• Comparing regression models.

 

Should you have any questions please contact Mind Project Ltd at info@mindproject.co.uk. Please visit the course website at Python for Data Analysis - 6-Week Tutor-Led Training Course - November 2023.

Cost:

By 13th of October 2023 (Early Bird offer): £780 (normally £900) - Commercial fee - individual customers representing commercial/business entities, £630 (normally £750) - NGO/Gov/Academic fee - applicable to representatives of registered charitable organisations, national health service employees (e.g. NHS in the UK), employed academic staff (e.g. research assistants, lecturers, post-docs and above), and employees of governmental departments (e.g. civil servants), £450 (normally £600) - Student fee - applicable to undergraduate and postgraduate students only (confirmation of student status required). Additional discounts available for multiple bookings and groups.

Website and registration:

Register for this course

Region:

Greater London

Keywords:

Descriptive Statistics, Correlation, Effect size , Statistical Theory and Methods of Inference, Parametric statistics, Non-parametric statistics, Regression Methods, Ordinary least squares (OLS), ANOVA, ANCOVA, Linear regression, Python, Data Visualisation, Creating graphs and charts

Related publications and presentations from our eprints archive:

Descriptive Statistics
Correlation
Effect size
Statistical Theory and Methods of Inference
Parametric statistics
Non-parametric statistics
Regression Methods
Ordinary least squares (OLS)
ANOVA
ANCOVA
Linear regression
Python
Data Visualisation
Creating graphs and charts

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