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Fundamentals of Data Science

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.

Pre-Requisites

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

Course Content

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 Data Science Methodology •• Methods of Data Science •• Methods of Machine Learning •• Data Preparation •• Gathering Data •• Storing Data •• Cleaning Data

•• Types of problems •• Classification •• Regression •• Clustering •• Recommender systems

Data Science Python Primer

Intro to Python •• 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 •• 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 •• 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

Data Science R Primer

Intro to R for Data Preparation •• 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 •• 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 •• 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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