13:00-15:00 UTC / 15:00-17:00 CET - Two hours per day over three days.
Have you ever wondered how Python could enhance your research? This course introduces you to the fundamentals of programming in Python and exposes you to the libraries useful for analysing data and visualisations. It will cover aspects of dealing with numerical & text data with examples from the market research type of domains.
Python for Market Research is designed specifically for Market Researchers, giving you the skills to start your Python journey in Insights. This course is perfect for the busy professional looking for a ‘one-stop' shopping experience.
This is the recommended prerequisite for the course:
Machine Learning and its Applications in Market Research
Use code VA_Python25_FDP when buying both for a 25% discount!
The course includes:
Homework starting ten days prior to the course and during the course
Extensive resource listings at your fingertips
Accessible and professional trainer ready for all your Python questions
Confidence in your ability to use Python in your Market Research applications
This course is limited to 30 participants, so get your place now!
In this course, you will cover:
Introduction to the Python programming environment
Understanding different types of data - structured and unstructured
Python concepts for storing and retrieving data
Wrangling data using programming concepts like functions
Python libraries for doing Espoloratory Data Analysis - NumPy, Pandas, Matplotlib, NLTK (Natural Language Toolkit) and Seaborn
After this course, you’ll be able to:
Program in Python from a data science perspective
Do Exploratory Data Analysis (EDA)
Create charts and visualisations to explain your analysis and inferencing
Who should attend?
Anyone interested in developing their programming skills to enhance their research ability perfect for any Market Researcher looking to upgrade their research skills. You could be a beginner in programming or already a Python programmer. There is a lot to take away from both. It is also required for those wanting to take up the follow-up course.
What is the level of learning?
While no prior learning is required, a desire to learn to program and put in the work to complete the exercises will be very important. Exposure to working in excel & writing equations/formulas in it will be helpful. Simple, high school-level mathematics will be used in exercises and programming tasks.
There will be pre-course work material that will be made available to help ease into the course.
Jag RaoProfessor of Practice at University of Georgia
Jagannath Rao runs a complete course on Machine Learning and Data Science as part of Georgia Informatics institute in the engineering school. He retired in 2018 after having done a stint of 33 years at the global conglomerate Siemens and brings rich industry experience in areas ranging from Industrial Automation to Internet of Things. Apart from an education in AI, he has also spent many years in the practical application of these technologies in the business world and that includes the widespread application of "Big Data" technologies in the realm of manufacturing, covering topics such as plant analytics, asset analytics and digital services. In addition to his role at UGA, Jagannath also consults with industry clients in the space of AI & Machine Learning in the context of generating use cases and applying / implementing those technologies.
Jagannath is an Electrical engineer with a graduate degree in Knowledge Engineering (AI) and an MBA. He has worked around the globe in India, Germany, Singapore and the USA. With more than 30 years' experience, including practical hands on implementation, he brings a large body of expertise which he is imparting to the young engineers graduating today and industry professionals.
Jagannath has also served as the Chair of the Advisory Board, the University of Georgia - Athens, College of Engineering, and on the advisory boards of Arizona State University W.P. Carey School of Business, Center for Services Leadership, and the Georgia Institute of Technology, School of Biomedical Engineering, Advisory Board.