Data Science and AI/ML (Python) training course

A comprehensive introduction to Data Science, AI and ML with Python - including basic concepts, statistical computing libraries, Artificial Intelligence and Machine Learning

Next 1 February (Remote)
5 days £2,995.00 + VAT

JBI training course London UK

  • Explore Python core concepts and best practices
  • Learn Python virtual environment set-up
  • Explore the notions of object-oriented programming and functional programming, as applied to Python applications
  • Use Python and its statistical computing libraries to analyse and visualise your data, and to gather actionable insights
  • Gain an overview of Artificial Intelligence, Machine Learning and Big Data
  • Learn how to implement Machine Learning systems to perform predictions on data
  • Build your AI capability
  • Familiarise yourself with the automation in the workplace
  • Explore the future of the workplace
  • Learn about Machine Learning tools for data scientists and non-data scientists
  • Learn more about chatbots and feature engineering 


This training course provides an introduction to the core concepts of the Python language, ultimately focusing on Big Data Analytics and Machine Learning applications.

The first three days of the course introduce you to Python tools for data science, including how best to manipulate and visualise your data with Python's excellent library support.

The last two days move one step forward, providing an overview of Artificial Intelligence and Machine Learning with the purpose of implementing predictive analytics applications.

Practical exercises and interactive walk-throughs are used throughout, so attendees have the opportunity to apply the proposed concepts on real data science applications, from exploratory data analysis to predictive analytics.

JBI training course London UK
JBI training course London UK

Software developers and software engineers with a basic knowledge of Python. Data Scientists, Data analysts and Business Intelligence professionals who are new to Python.

Developers, engineers, researchers and analysts who want to start learning about Artificial
Intelligence and related concepts, including Data Science, Data Mining, Machine Learning and
Deep Learning. Some background in Mathematics (e.g. Statistics and Probability, Linear Algebra,
Calculus, etc) will be beneficial, but not strictly required.





Related Courses

Environment Set-up

• The Anaconda distribution as Python Data Science platform
• Overview on Python virtual environment set-up
• Running Python code in Jupyter notebook

Python core concepts

• Built-in data types in Python
• Working with strings, numbers, lists, tuples and dictionaries
• Control flow statements
• Conditional execution with if statements
• Conditional loops with where statements
• Looping over a sequence with for statements
• Defining and using custom functions
• Working with dates and times
• Accessing data on file (CSV, JSON, ...)

Python Data Science libraries

• Working with data in pandas

  • Working with table-like data in pandas
  • Creating Series and DataFrame objects
  • Loading data from file into DataFrame objects
  • Adding, removing and updating databases

• Retrieving data in pandas

  • Querying data to extract specific rows and columns
  • Filtering data on specific conditions
  • Understanding pandas indexing

• Data manipulation in pandas

  • Data transformation
  • Applying functions to transform individual values
  • Applying functions to aggregate values by rows and columns

 • Handling missing data in pandas

  • Identifying missing data points
  • Filtering out missing data
  • Filling missing data with given values, interpolation and other filling strategies

• Data Analysis in pandas

  • Extracting summary statistics over DataFrame objects
  • Performing data aggregation queries (groupby() method)
  • Aggregating multiple columns in the same query
  • Exploratory analysis of new datasets

• Data Visualisation in pandas

  • Plotting data from a Series or DataFrame object
  • Bar plots, line plots, scatter plots, histograms and other common charts
  • Basic customisation of charts

• Working with multiple tables

  • Concatenation of multiple tables based on structure/schema
  • Join/merge operations with DataFrame objects based on values
  • Reindexing operations, dealing with duplicate labels in the index
  • Dealing with duplicate records
  • Renaming columns
• Time Series with pandas
  • Working with date and time data types in pandas
  • Creating ranges of date/time data points
  • Indexing by time
  • Resampling: data aggregation over time
  • Moving window operations, e.g. moving average
  • Defining custom calendars, custom business days, custom holidays

• Working with text data in DataFrames

  • Using the str attribute in pandas objects
  • String manipulation functions in pandas
  • Filtering data with string pattern matching

• SQL databases

  • Connecting to SQL databases with SQLAlchemy
  • Loading data from SQL to pandas
  • Sending SQL queries to a database and retrieving the results in Python and pandas

• NumPy

  • Working with multi-dimensional arrays with NumPy
  • Arithmetic operations with NumPy arrays
  • Vectorised operations with NumPy arrays
  • Stats and linear algebra with NumPy
  • Slicing and indexing NumPy arrays

• Data Visualisation with matplotlib and plotly

  • Overview on the basic types of charts available with the Python libraries
  • Bar plots, line plots, histograms, scatter plots, pie charts
  • Customising the layout and format of a chart
  • Examples of static visualisation with matplotlib
  • Examples of interactive visualisation with plotly

Accessing & Preparing Data

  • Data can come in multiple formats and from multiple sources. We'll examine how to read and write data from local files in different formats, and how to access data from remote source.
  • Data cleaning and data preparation are the first steps in a data analysis project, so we'll discuss how to perform data transformation to get ready for further analysis.

Data Analysis

  • With our data in the right shape, we're ready to analyse them in order to extract useful insights.
  • We'll perform the computation of summary information and basic statistics from data sets. We'll approach split-apply-combine operations with Data Frames, in order to perform advanced transformations and reshaping our data with pandas.
  • We'll query our Data Frames using the powerful group-by method.

 Data Visualisation

  • Data analysis benefits from the visualisation of data. If a picture if worth a thousand words, complex data structures can be easier to understand and analyse using effective visualisation techniques. Communicating the results with non-technical users is also a challenge that visualisation techniques help to overcome. We'll showcase how to easily produce beautiful visualisations with matplotlib.


  • Python packaging: using and creating custom libraries
  • Unit testing: tools to perform unit testing in Python
  • Interaction with web services


• What is Artificial Intelligence? What's up with the hype?
• Data Science vs. Data Mining vs. Machine Learning
• Machine Learning Problems and Applications
• Python Environment Set-up with Anaconda Python
◦ Jupyter Notebooks
◦ Python Ecosystem for Data Science and Machine Learning

Machine Learning Overview

• Learning and Prediction
• Feature Engineering
• Training data and Test data
• Cross-validation
• Underfitting and Overfitting

Supervised Learning Problems

• Classification: predicting a label
• Algorithms for classification: k-Nearest Neighbours, Support Vector Machine and Naive Bayes
• Regression: predicting a quantity
• Algorithms for regression: Linear Regression and Polynomial Regression

Unsupervised Learning Problems

• Clustering: grouping similar items
• Algorithms for clustering: k-Means, Hierarchical Clustering and DBSCAN
• Dimensionality Reduction
• Algorithms for dimensionality reduction: Principal Component Analysis

Evaluation of Machine Learning algorithms

• Evaluation metrics for machine learning
• Planning an evaluation campaign on your data

Deep Learning & Neural Network Overview

• Intro to Artificial Neural Networks
• Neural Network concepts
◦ Neural Network Types
◦ Gradient Descend
◦ Back-propagation
◦ Activation Functions
◦ Loss Functions
◦ Hyper-parameters
• Neural Networks in the Wild: examples of successful applications
• Deep Network Architectures
• Deep Learning Libraries

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