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ASI Training: From Business Analyst to Data Scientist
This instructor led training program introduces course participants to the world of Data Science. The course is a hands-on introduction to data analysis and machine learning in Python. The course will be run over three Thursday afternoons in April and May, 2pm - 6.30pm. Throughout the duration of the course, you have access to our cutting-edge data science platform SherlockML, that provides the tools and infrastructure to complete complex analysis and build sophisticated models. During our first session, we demystify some of the commonly used concepts in data science and get course participants up to speed with working in Python on the SherlockML platform. In our second session, we cover techniques in supervised machine learning, and you will work through exercises to learn how to build some models yourself. The last session covers techniques in unsupervised learning and data visualization to communicate your findings. The course has a strong focus on gaining practical hands-on experience implementing sophisticated algorithms and building predictive models on real datasets. Once the course has finished you will continue to have access to the learning materials and code via the SherlockML platform.
At the end of this course, participants will be able to:
Better understand concepts of data science and different types of machine learning algorithms
Explore and analyse data using pandas in Python
Gain an understanding of and experience implementing common machine-learning techniques
Gain familiarity with the most commonly used libraries in Python; NumPy, pandas and Scikit-learn
Generate data visualisations to communicate findings in the data
Who Should Attend
This class is intended for:
This course is designed for people who have an analytical mind and want to get better at working with data. It also suitable for people who work in IT and want to get a better understanding of machine learning algorithms.
At the ASI you receive expert tuition from experienced practitioners. Our courses are developed by a team of experienced Machine Learning practitioners, who hold Doctorate Degrees in Science or Computer Science. The ASI has a wealth of experience across different industries, having delivered over 150 commercial data science projects and provided corporate training for three years. This course is taught by a selection of our top data scientists, who work full time in our data science consulting team.
For this course a basic familiarity with the Python programming language is desired. Python is a great language for getting started with machine learning, as it is equipped with a number of useful libraries for data analysis (e.g., pandas) and fast prototyping (e.g., scikit-learn). Python not only allows beginners to develop machine learning projects with ease but also offers a rich framework for advanced users, thanks to a passionate open source community and the availability of libraries such as Theano and TensorFlow. For those less familiar with Python, we strongly urge you to practice working with Python before the start of the course, for example via Codecademy (https://www.codecademy.com/learn/python) or Coursera (e.g. https://www.coursera.org/learn/python/home/welcome).
Please try signing up to SherlockML by using the invite code distributed before the start of the course (https://sherlockml.com/)
Please bring your laptop for this course
First session - 27th April 2017, 2pm - 6.30pm
Introduction to Data Science: what is supervised/unsupervised machine learning, intro to python, getting set-up on SherlockML
Working with real data in Python: exploring and preparing the data using pandas and NumPy
Second session - 4th May 2017, 2pm - 6.30pm
Machine learning: Linear & Logistic Regressions
Machine learning: Decision trees and Random Forests
Third session - 11th May 2017, 2pm - 6.30pm
The K to success: model selection in supervised and unsupervised learning
Data visualisation using Plotly &Matplotlib
Please reach out to email@example.com should you have any questions regarding this course.