- Real Time Signals India
Data Science
Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining.

R Programming
Python Programming
Machine Learning
1. What is Data Science
1. Demand of Data Science
2. Venn Diagram
3. Pipeline
4. Roles
5. Team
6. Knowledge Check
2. Field of study
1. Big Data overview
2. Programming involvement in Data Science
3. Statistics
4. Knowledge check
3. Ethics
1. Ethical issues
2. Knowledge check
4. Data Sources (Getting Data)
1. Data Metrics
2. Existing data
3. APIs
4. Scraping
5. Creating Data
6. Knowledge check
5. Data Exploration (Cleaning Data)
1. Exploratory graphs
2. Exploratory statistics
3. Knowledge check
6. Programming
1. Spreadsheets
2. R programming
3. Python
4. SQL
5. Web formats
6. Knowledge check
7.Mathematics
1. Algebra
2. Systems of equations
3. Calculus
4. Big O
5. Bayes probability
6. Knowledge check
8. Applied Statistics
1. Hypothesis
2. Confidence
3. Problems
4. Validating
5. Knowledge check
9. Machine Learning
1. 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.
1. Cost function
2. Gradient descent
3. Normal Equations
1. 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.
1. Cost Function
2. Gradient descent solution.
1. 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;
1. Back propagation
2. Application of Neural Network
1. 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.
1. Large Margin classification
2. Kernels
1. UNSUPERVISED
1. Clustering
2. Gaussian Mixture Models
3. HMM
10. R Programming
1. Writing code and setting your working directory
2. Getting started and R nuts and Bolts
1. R console Input and evaluation
2. Data types – R Objects and attributes
3. Data types – Vectors and Lists
4. Data types – Matrices
5. Data types – Factors
6. Data types – Missing values
7. Data types – Data frames
8. Data types – Names Attributes
9. Data types – summary
10. Reading Tabular Data
11. Reading large tables
12. Textual data formats
13. Connections: Interfaces to outside world
14. Subsettings – Basics
15. Subsettings – Lists
16. Subsettings – Matrices
17. Subsettings – Partial Matching
18. Subsettings – Removing Missing values
19. Vectorized Operations
11. Communicating
1. Interpretability
2. Actionable insights
3. Visualization for presentation
4. Reproducible research
5. Knowledge check
Conclusion and final test