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Course Details

1. What is Data Science

  • Demand of Data Science

  • Venn Diagram

  • Pipeline

  • Roles

  • Team

  • Knowledge Check

    2. Field of study
  • Big Data overview

  • Programming involvement in Data Science

  • Statistics

  • Knowledge check

    3. Ethics
  • Ethical issues

  • Knowledge check

    4. Data Sources (Getting Data)
  • Data Metrics

  • Existing data

  • APIs

  • Scraping

  • Creating Data

  • Knowledge check

    5. Data Exploration (Cleaning Data)
  • Exploratory graphs

  • Exploratory statistics

  • Knowledge check

    6. Programming
  • Spreadsheets

  • R programming

  • Python

  • SQL

  • Web formats

  • Knowledge check

    7.Mathematics
  • Algebra

  • Systems of equations

  • Calculus

  • Big O

  • Bayes probability

  • Knowledge check

    8. Applied Statistics
  • Hypothesis

  • Confidence

  • Problems

  • Validating

  • Knowledge check

    9. Machine Learning
  • Linear Regression with one and multiple variables.

    Linear regression predicts a real-valued output based on an input value. We discuss the application of linear regression to housing price prediction, present the notion of a cost function, and introduce the gradient descent method for learning. 

  • Cost function

  • Gradient descent

  • Normal Equations

  • Logistic regression. What if your input has more than one value? In this module, we show how linear regression can be extended to accommodate multiple input features.

  • Cost Function

  • Gradient descent solution.

  • Neural Networks. Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets and understand your voice commands, it is likely that a neural network is helping to understand your speech;

  • Back propagation

  • Application of Neural Network

  • Support Vector Machines (SVM). Support vector machines, or SVMs, is a machine learning algorithm for classification. We introduce the idea and intuitions behind SVMs and discuss how to use it in practice. 

  • Large Margin classification

  • Kernels

  • UNSUPERVISED

  • Clustering

  • Gaussian Mixture Models

  • HMM

    10. R Programming
  • Writing code and setting your working directory

  • Getting started and R nuts and Bolts

  • R console Input and evaluation

  • Data types – R Objects and attributes

  • Data types – Vectors and Lists

  • Data types – Matrices

  • Data types – Factors

  • Data types – Missing values

  • Data types – Data frames

  • Data types – Names Attributes

  • Data types – summary

  • Reading Tabular Data

  • Reading large tables

  • Textual data formats

  • Connections: Interfaces to outside world

  • Subsettings – Basics

  • Subsettings – Lists

  • Subsettings – Matrices

  • Subsettings – Partial Matching

  • Subsettings – Removing Missing values

  • Vectorized Operations

     

    10. Communicating
  • Interpretability

  • Actionable insights

  • Visualization for presentation

  • Reproducible research

  • Knowledge check

    Conclusion and final test

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