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Data SCIENCE

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Specialization
Key Features
Videos

About Specialization:

Data Science

Data science is a multidisciplinary field that uses scientific methods, algorithms, processes, and systems to extract insights and knowledge from structured and unstructured data. The primary goal of data science is to uncover patterns, trends, correlations, and other useful information from data to aid in decision-making, problem-solving, and strategic planning. Data scientists utilize a combination of programming skills, statistical knowledge, and domain expertise to analyze large datasets, extract meaningful insights, build predictive models, and communicate their findings to stakeholders.

Key Features/Course content

  1. Introduction:
    1. Definition of Data Science
    2. Big Data and Data Science hype – and getting past the hype
    3. Datafication
    4. Current landscape of perspectives
    5. Statistical Inference
    6. Populations and samples
    7. Statistical modeling
    8. Probability distributions
    9. Fitting a model
    10. Over fitting
  2. Basics of R:
    1. Introduction
    2. R-Environment Setup
    3. Programming with R
    4. Basic Data Types
  1. Types of Data:
    1. Attributes and Measurement
    2. What is an Attribute?
    3. The Type of an Attribute
    4. The
      Different Types of Attributes
    5. Describing Attributes by the Number of Values
    6. Asymmetric Attributes
    7. Binary Attribute
    8. Nominal Attributes
    9. Ordinal Attributes
    10. Numeric Attributes
    11. Discrete Vs Continuous
      Attributes
  2. Basic Statistical Descriptions of Data:
    1. Measuring the Central Tendency: Mean, Median, and Mode
  3. Measuring the Dispersion of Data:
    1. Range, Quartiles, Variance, Standard Deviation, and Interquartile Range, Graphic Displays of Basic Statistical Descriptions of Data
  1. Vectors:
    1. Creating and Naming Vectors
    2. Vector Arithmetic
    3. Vector sub setting
  2. Matrices:
    1. Creating and
      Naming Matrices
    2. Matrix Sub setting
    3. Arrays
    4. Class.
  3. Factors and Data Frames:
    1. Introduction to Factors
    2. Factor Levels, Summarizing a Factor
    3. Ordered Factors
    4. Comparing Ordered Factors
    5. Introduction to Data Frame
    6. subsetting of Data Frames
    7. Extending Data Frames
    8. Sorting Data Frames
  4. Lists:
    1. Introduction
    2. Creating a List
    3. Creating a Named List
    4. Accessing List Elements
    5. Manipulating List Elements
    6. Merging Lists
    7. Converting Lists to Vectors
  1. Conditionals and Control Flow:
    1. Relational Operators
    2. Relational Operators and Vectors
    3. Logical
      Operators
    4. Logical Operators and Vectors
    5. Conditional Statements
  2. Iterative Programming in R:
    1. Introduction
    2. While Loop
    3. For Loop
    4. Looping Over List
  3. Functions in R:
    1. Introduction
    2. Writing a Function
      in R
    3. Nested Functions
    4. Function Scoping
    5. Recursion
    6. Loading an R Package
    7. Mathematical Functions
      in R
  1. Data Reduction:
    1. Overview of Data Reduction Strategies
    2. Wavelet Transforms
    3. Principal Components
      Analysis
    4. Attribute Subset Selection
      1. Regression and Log-Linear Models:
        1. Parametric Data Reduction
        2. Histograms
        3. Clustering
        4. Sampling
        5. Data Cube Aggregation.
  2. Data Visualization:
    1. Pixel-Oriented Visualization Techniques
    2. Geometric Projection Visualization Techniques
    3. Icon-Based Visualization
      Techniques
    4. Hierarchical Visualization Techniques
    5. Visualizing Complex Data and Relations

Videos

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