Data Science with Python – Classroom

London School of Emerging Technology

LSET Validated Data Science with Python

£100

LSET’s six-module curriculum progresses from Python foundations to data analysis, visualisation, machine learning, deep learning and practical data science workflows.

• Course overview and setup of Python, Jupyter, VS Code and Anaconda
• Variables, data types and operators
• Loops, conditions, lists, tuples and dictionaries
• Functions, modules and file handling
• Using Jupyter Notebook and Colab for data science

• Creating NumPy arrays and performing array operations
• Indexing, slicing, filtering and broadcasting
• Mathematical and statistical operations
• NumPy performance compared with Python lists
• pandas Series and DataFrames
• Importing CSV, Excel and JSON data
• Data cleaning, missing values and replacement
• Grouping, applying functions and mapping data
• Merging, joining and concatenating datasets
• Handling categorical and date/time data

• Line, bar, scatter and histogram plots
• Customising colours, labels and grids
• Seaborn categorical, matrix and regression plots
• Hands-on exercises and solutions
• Interactive scatter, bar, pie and heatmap visualisations
• Dashboard basics with Plotly Express

• Supervised and unsupervised learning
• Linear and logistic regression
• K-nearest neighbours, decision trees and random forests
• K-means clustering
• Training and test splits
• Accuracy, RMSE, precision and recall
• Feature scaling, encoding, pipelines and cross-validation
• Introduction to natural language processing: bag of words and TF-IDF
• Introduction to neural networks

• Installing TensorFlow 2.x and optional GPU support
• Artificial neural networks, tensors and operations
• Keras Functional and Sequential APIs
• Regression and classification with TensorFlow
• Overfitting and regularisation
• Saving, loading and evaluating deep learning models
• Optional introductions to convolutional neural networks and RNN/LSTM models

• Working with messy datasets and exploratory data analysis
• Feature engineering and handling missing values
• Outlier detection and data transformations
• Model selection and tuning with grid search and random search
• Introduction to MLOps and model versioning
• Experiment tracking with an introduction to MLflow
• Deployment basics

Learners explore data science through guided hands-on sessions, practical examples, projects and assignments. Interactive teaching, industry professional mentor support, group activities and presentations form part of the learning experience.

The curriculum is subject to updates. LSET communicates the final curriculum during course induction.

Learners are responsible for obtaining and maintaining any required software subscriptions, licences or tools. These costs are not included in the course fees.

This Data Science with Python course is suitable for:

• Learners with basic programming experience who want to explore data science.
• Experienced developers interested in applying Python to data analysis and machine learning.
• Students seeking a foundation in data science.
• Working professionals exploring a move into the data science field.

Learners should meet LSET’s published programming and English language entry requirements.

• Some prior programming experience.
• Ability to work in a team.
• Access to a personal laptop.
• Applicants whose first language is not English must meet one of LSET’s stated English language requirements: IELTS 5.5, NCC Test or GCE O Level English C6.

LSET identifies related career paths:

• Data Analyst
• Data Engineer
• Database Administrator
• Machine Learning Engineer
• Data Scientist
• Data Architect

Progression depends on individual skills, experience and further professional development.