Notes of data science
Unit 1: Data Science
Introduction to Data Science, Definition and description of Data Science, history and development of Data Science, terminologies related with Data Science, basic framework and architecture, importance of Data Science in today’s business world, primary components of Data Science, users of Data Science and its hierarchy, overview of different Data Science techniques.
Unit 2: Data Science
Sample spaces, events, Conditional probability, and independence, Random variables, Discrete and Continuous random variables, densities and distributions, Normal distribution and its properties, Introduction to Markov chains, random walks, Descriptive, Predictive, and prescriptive statistics, Statistical Inference, Populations and samples, Statistical modelling.
Unit 3: Data Science
Exploratory Data Analysis and the Data Science Process - Basic tools (plots, graphs, and summary statistics) of EDA - Philosophy of EDA - The Data Science Process - Case Study
Unit 4: Data Science
Data Visualization Basic principles, ideas and tools for data visualization, Examples of inspiring (industry) projects, Exercise create your own visualization of a complex dataset.
Unit 5: Data Science
NoSQL, use of Python as a data science tool, Python libraries, SciPy and sci-kitLearn, PyBrain, Pylearn, Matplotlib, challenges and scope of Data Science project management.