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About this Course

Fundamentals of Data Science is a three day overview course which blends discussion and group exercises to explore the field of data science with applied real world examples and projects.
Teaching begins with a conceptual introduction to science, data science, big data and machine learning; followed by a litany of real-world data science and machine learning examples.
The remainder is divided into two parts: introduction to Python, and the application of the language in building a Machine Learning model (including data preparation, statistical tests, feature engineering, modelling and evaluation).

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Locations

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  • London

    London

    International House

  • Manchester

    Manchester

    Oxford Street

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Course Information

Intro to Science, Data Science and Big Data
  • Introduction to Data Science
  • What is Science?
  • What is the Scientific Method?
  • What is Data?
  • How is Data Structured?
  • What is Big Data?
  • What is Data Science?
  • What is the Method of Data Science?
  • What is Machine Learning?
  • What skills does a Data Scientist have?
Intro to Statistics
  • Probability
  • Population and Samples
  • Summary Statistics
  • Sampling Error
  • Statistical Distributions
  • Null-Hypothesis and Significance Tests
  • Student’s T-Test
Intro to Python
  • Overview
  • History & Philosophy
  • Installation via Anaconda
  • Language Characteristics
  • Anatomy of an Python Program
  • Anatomy of an REPL Session
  • Help
  • Getting Started with Python: Data
  • Getting Started with Python: Calling Functions
  • Getting Started with Python: Packages
Intro to Python for Data Science
  • Jupyter Notebook
  • Python is Slow
  • Numpy Arrays
  • Data Type
  • Slice, cherry-picking and filtering
  • Operations on Numpy Arrays
  • Universal Functions
  • Summary Statistics
  • Pandas package
  • Series
  • DataFrame and table manipulation
  • ETL with Pandas
  • Visualisation with Matplotlib and Seaborn
Intro to Machine Learning
  • What is Machine Learning
  • CRISP-DM
  • Learning Process
  • Types of problems
  • Classification
  • Regression
  • Clustering
  • Recommender systems
  • Statistical Tests
Machine Learning with Python
  • Real World Data
  • Overview
  • Case Study
  • Understanding the Problem
  • Exploratory Analysis
  • Preparing Data for Learning
  • Cleaning
  • Transforming
  • Scaling
  • Learning a Model
  • Selecting
  • Training
  • Tuning
  • Evaluating a Model

Delegates attending this course should have technical skills such as application or SQL programming – expose to Python or R would be advantageous.