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

Date:

28/10/2020 - 02/12/2020

Organised by:

Mind Project Ltd

Presenter:

Simon Walkowiak MSc, MBPsS

Level:

Entry (no or almost no prior knowledge)

Contact:

Mind Project Ltd
Simon Walkowiak MBPsS
Phone: 02033223786
Email: info@mindproject.co.uk

video conference logo

Venue: Online

Description:

1. Course description.

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.

 

2. Course programme.

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 R 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.

 

3. Course pre-requisites and further instructions

  • We recommend that all attendees have the most recent version of Anaconda Individual Edition of Python 3.7 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.anaconda.com/distribution/. 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) during the tutor-led video sessions.
  • You will need at least one commonly used web browser installed on your PC (e.g. Chrome, Safari, Firefox, Edge etc.) in order to attend the video-streamed tutorials. You may also use your mobile phone (Android or iOS) to connect to our tutor-led video sessions.
  • The primary spoken and written language of the course is English.

 

Should you have any questions please contact Mind Project Ltd at info@mindproject.co.uk or by phone on 0203 322 3786. Please visit the course website at https://www.mindproject.io/product/python-for-data-analysis-tutor-led-online-course-oct20/.

Cost:

By 7th of October 2020 (Early Bird offer):
£345 (normally £420) per person for the whole course (regular fee).
£210 (normally £270) per person for the whole course applicable to undergraduate and postgraduate students, representatives of registered charitable organisations and NHS employees only (discounted fee).
Additional discounts available for multiple bookings and groups.

Website and registration:

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

Related publications and presentations:

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

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