Business Analyst to Data Scientist

Course description

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 afternoons, with sessions typically lasting for 4.5 hours.

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. You’ll continue to have free access to SherlockML for 90 days after the course has ended.

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. The second session covers techniques in supervised and unsupervised learning and data visualisation to communicate your findings. In our last session, we cover techniques in machine learning regressions and decision trees, and you will work through exercises to learn how to build some models yourself.

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.

Learning objectives

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:

  • Data analysts

  • Business analysts

  • IT engineers

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 ASI Data Science 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 ( or Coursera (e.g.


  • Please try signing up to SherlockML by using the invite code distributed before the start of the course (

  • Please bring your laptop for this course


Each module runs for about 4.5 hours and we typically spread the modules over a period of three weeks, to allow you to work on your own on the materials, absorb what you've learned and come up with new questions.

First session

  • 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

  • The K to success: model selection in supervised and unsupervised learning
  • Data visualisation using Plotly & Matplotlib

Third session

  • Machine learning: Linear & Logistic Regressions

  • Machine learning: Decision trees and Random Forests

Any questions?