By booking this course, you agree to our terms and conditions. For any
enquiries, please call 0414 57 33 22. If you prefer, you can pay by
invoice rather than credit card. For instructions, click here.
INTRODUCTION TO PYTHON FOR DATA ANALYSIS PYTHON IS A HIGH LEVEL AND
GENERAL PURPOSE PROGRAMMING LANGUAGE. Millions of PYTHON USERS
CONTRIBUTE TO A THRIVING OPEN-source community that also enjoys
immense commercial use and support. A core set of packages and
interfaces (Juptyer Notebooks, Pandas, Numpy, scikit-learn) presents
analysts and data scientists with an interactive and powerful tool to
perform data mining, statistical ANALYSIS AND VISUALISATION.
Data-science teams usually use at least one of PYTHON AND R IN THEIR
PRODUCTION ENVIRONMENTS OR ANALYSIS PIPELINES. PYTHON IS ALSO THE TOOL
OF CHOICE OF ELITE DATA-mining competition winners and deep-learning
innovations such as Tensorflow. See what former trainees are saying
about this course. Discounts Group discounts also apply during the
earlybird period: 5% for 2–4 people, 10% for 5–6 people, 15% for
7–8 people, and 20% for 9 or more people. Select your desired
quantity of tickets and click “Add to cart” to see the discount
calculated before checkout. Please contact us at
enquiries@presciient.com to find out more about these special rates.
Course Outline This two-day course is an INTRODUCTION TO PYTHON
PROGRAMMING AND JUPYTER NOTEBOOKS, beginning with the most basic
operations of downloading and installing the PYTHON ENVIRONMENT. The
course will use Anaconda, a popular PYTHON DISTRIBUTION FOR DATA
SCIENCE THAT INCLUDES MANY OF THE PACKAGES USED IN THIS COURSE. The
course will also introduce core PYTHON OBJECTS AND OPERATIONS, Numpy
for statistical and matrix operations, matplotlib and Plotly for
visualisations, and Pandas, a comprehensive data manipulation and
ANALYSIS PACKAGE. Participants will learn how to input, read, write,
and manipulate data, primarily using Pandas, and be instructed in all
the aspects of procedural programming in PYTHON, allowing them to
create their own PYTHON MODULES. Jupyter Notebooks will be featured as
the recommended interface to write code, explore and analyse data, and
to document and communicate the results of the data ANALYSIS WITH
INTERACTIVE VISUALISATIONS. The course is focused on providing a
foundation for participants to use PYTHON FOR EXPLORATORY DATA
ANALYSIS AND VISUALISATION, which can be used as a stepping stone to
machine learning using the popular scikit-learn package and
deep-learning packages unique to PYTHON. Familiarity with PYTHON WILL
ALLOW USERS TO USE PACKAGES AND ACCESS DATA AND WEB SERVICES THAT HAVE
EXISTING CONNECTIONS TO PYTHON, e.g. natural language processing,
APIs, and web scraping. Who should attend? This is a practical course,
suitable for existing and prospective data-ANALYSIS PRACTITIONERS IN
GOVERNMENT AND INDUSTRY. Participants will be provided with a range of
programmatic and user-interface options for working with data in
PYTHON. The course assumes no specialised statistical knowledge. Its
focus is developing a practical understanding of PYTHON AS A TOOL FOR
BUSINESS USERS. Course Outcomes Attendees will, by the end of the
course, have the basic skills, resources, guidance and confidence to
immediately and self-sufficiently begin to use PYTHON IN THEIR WORK.
Course instructor Courses are taught by Dr Eugene Dubossarsky and his
hand-picked team of highly skilled instructors. About our training
Eugene Dubossarsky’s courses are unlike those offered in
universities, online, or by private providers. His data-science
classes, in particular, give clients not just knowledge of a process,
but the real power of understanding the underlying concepts, allowing
them to confidently practice, manage, promote and risk-assess data
science. Dr Dubossarsky says “the way many courses teach data
science is like teaching people to memorise and recite poetry in a
language they do not understand”. By contrast, he confers an
understanding of that language, taught in an intuitive, accessible way
that leaves trainees with an instinct for data science. Keeping
formulae and mathematics to a bare minimum and taking an intuitive,
visual approach, Eugene’s courses deliver a compressed mentoring
experience as much as they do content. This is difficult for an
average trainer to replicate. Trainees benefit from his extensive
knowledge and over 20 years of commercial data-science experience, as
well as his unique teaching style. The resulting testimonials speak
for themselves, and candidates come from all walks of life: CEOs,
general managers, salespeople, IT professionals, marketing staff,
public servants and of course people from many functions in the
finance world. These testimonials are extensive, and many more are
available on request. With specific regard to finance, Eugene has
mentored and advised senior leaders and their teams in a number of
major Australian banks. Testimonials Having studied stats at Uni I was
surprised how far the field has progressed in the last few years,
particularly in the area of big data. The great thing about Eugene’s
course is I left with a sense that I was up to date with the latest
big data modelling concepts but more importantly could also deploy
them with some confidence. Eugene also made it clear he was available
to answer questions after the course, so you are not left hanging.
—Damon Rasheed, CEO, Rate Detective Data science can be a
challenging topic but Eugene’s “INTRODUCTION TO MACHINE
LEARNING” course turns complex statistical models into plain
English. The course contents and presentation were accessible and I
enjoyed the mixture of hands-on rattle() exercises, the challenge of
building multiple models with real life data, and the salient theory
whiteboard discussions created many “aha" moments. It was a great
introductory course and it gave me with a better grasp of Machine
Learning in general, a great framework for thinking about it and
practical hands-on skills that I can put to immediate use. I wish I
had done this course sooner. —Charl Swart, Director of Business
Operations, Unisys Credit Services Prerequisites The course assumes no
tertiary level training in statistics. Attendees simply need to be
familiar with working with structured, electronic data. Platform The
course will make use of the Anaconda Distribution of PYTHON AND SOME
OF THE TRAINING MAY BE DEMONSTRATED USING MICROSOFT AZURE NOTEBOOKS OR
ON THE MICROSOFT DATA SCIENCE VIRTUAL MACHINE. Meals and refreshments
Catered morning tea and lunch are provided on both days of the course.
Please notify us at least a week ahead if you have any special dietary
requirements. Feedback Use enquiries@presciient.com to email us any
questions about the course, including requests for more detail, or for
specific content you would like to see covered, or queries regarding
prerequisites and suitability. If you would like to attend but for any
reason cannot, please also let us know. Variation Course material may
vary from advertised due to demands and learning pace of attendees.
Additional material may be presented, along with or in place of
advertised. Cancellation and refunds You can get a full refund if you
cancel 2 weeks or more before the course starts. No refunds will be
issued for cancellations made less than 2 weeks before the course
starts. Frequently asked questions (FAQ) Do I need to bring my own
computer? There’s no need to bring your own laptop or PC. Our
courses take place in modern, professional training facilities that
have all the computing equipment you’ll need. I'm lost! How do I
find the venue? Please call 04 1457 3322 or email
dHJhaW5pbmcgfCBwcmVzY2lpZW50ICEgY29t if you can’t find the venue.
Presciient Training Coaching, Mentoring and Capability Development for
Analytics Please ask about tailored, in-house training courses,
coaching analytics teams, executive mentoring and strategic advice and
other services to build your organisation's strategic and operational
analytics capability. Our courses include: Predictive Analytics,
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Introduction to R and Data Visualisation Introduction to Python for
Data Analysis Forecasting and Trend Analytics Advanced Machine
Learning Masterclass Advanced Masterclass 2: Random Forests Advanced R
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Detection Introduction to Machine Learning Introduction to Data
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24/11/2019 Last update