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Description

  • Type

    Course

  • Location

    London

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: python-illustrated and r-illustrated. After introducing both languages, the modules cover various applied topics (data preparation, statistics, Markov chains, neural nets) with examples in either python or R.

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Location

Start date

London
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Start date

On request

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Different pricing structures are available including special offers. These include early bird, late availability, multi-place, corporate volume and self-funding rates. Please arrange a discussion with a training advisor to discover your most cost effective option.

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Subjects

  • Anatomy
  • Statistics
  • Algorithms

Course programme

Modules

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Intro to Science, Data Science and Big Data (10 topics)

  • 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 Data Science Methodology (11 topics)

  • Methods of Data Science
  • Methods of Machine Learning
  • Data Preparation
  • Gathering Data
  • Storing Data
  • Cleaning Data
  • Types of problems
  • Classification
  • Regression
  • Clustering
  • Recommender systems

Intro to Python (10 topics)

  • Overview
  • History & Philosophy
  • Installation
  • 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 (11 topics)

  • Python is Slow
  • ndarrays
  • Multiple Dimensions
  • Data Type
  • Slice and Dice
  • Matrices
  • Conversions
  • Operations on ndarray
  • Reduce & Accumulate
  • Summary Statistics
  • Plotting

Intro to Markov Chains with Python (14 topics)

  • Probabilities and Expectations
  • Independence
  • The Independent Future
  • The Dependent Future
  • Markov Chains & Assumptions
  • Transition matrix
  • Monte Carlo
  • One Event
  • State
  • Transition
  • Probability
  • Sample Space
  • Sequencing
  • n Events & Convergence

Intro to R for Data Preparation (20 topics)

  • Overview
  • History & Philosophy
  • CRAN
  • Installation
  • R Studio
  • Language Characteristics
  • Anatomy of an R Program
  • Anatomy of an REPL Session
  • Help
  • Getting Started with R: Data
  • Getting Started with R: Plots
  • Getting Started with R: Calling Functions
  • Getting Started with R: Packages
  • Getting Started: Using the REPL
  • Getting Started: Data Preparation
  • Data selection
  • Data sampling
  • Normalisation
  • Cleansing
  • Missing values

Intro to Statistics with R (15 topics)

  • Introduction to Statistics
  • Overview
  • Observation and Measurement
  • Probabilities and Events
  • Frequencies
  • Populations
  • Samples
  • The Normal Distribution
  • Interpreting the Normal Distribution​
  • Measures of Central Tendency
  • Measures of Spread
  • ​Hypotheses
  • Linear Relationships
  • Correlation
  • Simpson's Paradox

Intro to Learning Algorithms and Deep Learning with R (16 topics)

  • Examples of creating algorithms
  • Overview of Machine Learning algorithms
  • Decision trees
  • Clustering
  • Segmentation
  • Association
  • Classification
  • Sequence analysis
  • Neural nets
  • History
  • Layers
  • Weights
  • Back propagation
  • Deep Learning
  • KNN
  • SVM

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Hadoop Architecture and Administration

Price on request

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