About the course:
Our instructor-led Data Science with
Python training course is aimed at analysts and software developers who
need to create analysis and data visualisation solutions using the key
functions and libraries available in and around Python.
You
will benefit from extensive hands-on labs, delivered by an expert Data
Science practitioner who can guide you from the basics of data wrangling
with Python to using sophisticated libraries to visualise and make
predictions based on your data.
The Data Science with Python training course comprises two modules - Data Analysis with Python, and Machine Learning with Python, which can be taken individually.
Let us know if you are interested in remote training for your whole team - we're happy to customise a syllabus to meet your project and learning needs, delivered via virtual classroom.
By the end of this course, you will have learnt:
- Anaconda, conda and Jupyter
- Python Data Science tools: NumPy and Pandas
- Data cleaning and preparation
- Data Analysis
- Data Visualisation
- Machine Learning, Big Data and Predictive Analytics
- Data preprocessing and feature engineering with Python
- Supervised learning algorithms with scikit-learn
- Unsupervised learning algorithms with scikit-learn
- Evaluation, model introspection and error analysis
Who should attend
Analysts, Data Scientists, and software developers who want to get a practical introduction to Data Science and Machine Learning with Python.
Prerequisites
Delegates should ideally have some prior experience programming and / or of using statistical analysis techniques. Any experience with Python would be beneficial but we will give you "just enough" Python pointers to be able to create real-world workable solutions to data analysis, Machine Learning and visualisation challenges.
Live, instructor-led online and on-site training
We appreciate that you need flexibility to fit in with new working situations - whether you're an individual, part of a distributed team, or simply have projects and deadlines to meet.
Our remote training can take place online in a virtual classroom, with content split into modules to accommodate your scheduling challenges and meet your learning goals. Get in touch today to find out how we can help design a cost-effective, flexible training solution.
As soon as it's safe, we'll return to also offering the on-site custom training courses and programmes upon which we've built our reputation.
Environment Set-up
- The Anaconda distribution as Python Data Science platform
- Overview on Python virtual environment set-up
- Running code in Jupyter notebook
Data Analysis with Python:
Python core concepts
- Core data types in Python
- Control flow statements
- Defining and using custom functions
- The Python standard library
- Working with data:
- Iteration and list comprehensions
- Accessing raw data on file (CSV, JSON, ...)
- Working with dates and times
- Basics of Object-Oriented Programming in Python
Python Data Science libraries
Numpy:
- Working with NumPy arrays
- Essential operations with NumPy arrays
- Stats and linear algebra with NumPy
pandas:
- Working with table-like data in pandas
- Essential operations with Series and DataFrame object
- Loading data from file into DataFrame objects
- Summary statistics over DataFrame objects
- Data aggregation queries (groupby() method)
- Exploratory analysis of new datasets
- Data visualisation over DataFrames
- Join/merge operations with DataFrames
- Working with text data in DataFrames
- Working with Time Series data
Databases:
- Working with relational databases in Python
- Overview on SQLAlchemy for database interaction
- Integration of pandas and SQL
Machine Learning with Python
Overview of Machine Learning
- 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
- The Anaconda Python distribution
- 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
- Regression: predicting a quantity
- Algorithm in depth: Linear Regression and Polynomial Regression
- Classification: predicting a label
- Algorithm in depth: k-Nearest Neighbours
- Algorithm in depth: Support Vector Machine
- Algorithm in depth: Naive Bayes
- Unsupervised Learning Problems
- Clustering: grouping similar items
- Algorithm in depth: k-Means
- Algorithm in depth: Hierarchical Agglomerative Clustering
- Algorithm in depth: DBSCAN
- Dimensionality Reduction
- Algorithm in depth: Principal Component Analysis
- Evaluation of Machine Learning algorithms
Deep Learning & Neural Network Overview
- Intro to Artificial Neural Networks
- Mathematical Concepts required by Deep Learning
- Neural Network concepts
- Neural Network Types
- Gradient Descend
- Back-propagation
- Activation Functions
- Loss Functions
- Hyper-parameters
- Deep Network Architectures
- Deep Learning Libraries