Short and Crispy

While descriptive statistics summarize the characteristics of a data set, inferential statistics help you come to conclusions and make predictions based on your data.

Inferential statistics have two main uses:

• making estimates about populations (for example, the mean SAT score of all 11th graders in the US).
• testing hypotheses to draw conclusions about populations (for example, the relationship between SAT scores and family income).

3.1 Probability Distribution Function

A probability distribution is a mathematical function that describes the probability of different possible outcomes for an experiment. Probability distributions are often depicted using graphs or probability tables.

Probability Distribution Function can be categorized into

• Probability Density Function(PDF)
• Probablity Mass Function(PMF)
• Cumulative Distribution Function(CDF)

2.1 Covariance

Covariance and Correlation are very helpful in understanding the relationship between two continuous variables.

Covariance tells whether both variables vary in the same direction (positive covariance) or in the opposite direction (negative covariance).

Covariance(x, y) $$ Cov(x, y) = \sum_{i=1}^{n} \frac{(x_i - \bar{x})(y_i - \bar{y})}{n-1} $$

1.1 Statistics

• Statistics is the science of collecting, organizing and analyzing the data.
• Used for decision making process
• Data - facts or pieces of information

1.2 Types of Statistics

• Descriptive stats
  • It consists of organizing and summarizing the data.
• Inferential stats
  • It consists of using data you have measured to form conclusion, make predictions.
  • By using sample-data make conclusion on population-data

TL;DR

• Data Acquisition
  • Excel, Statistics,Probability, SQL
• Data Preparation
  • Python, Pandas, Numpy, Matplotlib/Seaborn
• Exploratory data Analysis
  • Linear Algebra, Pandas
• Data Modeling
  • Calculus, Machine learning, TensorFlow
• Visualization
  • PowerBI, Tabelu
• Deployment
  • Heroku, AWS

Flask

Flask is a popular Python web framework, meaning it is a third-party Python library used for developing web applications.

Flask is a lightweight WSGI web application framework. It is designed to make getting started quick and easy with the ability to scale up to complex applications.

Flask is based on the Werkzeug WSGI toolkit and Jinja2 template engine

# Topic covered
* Memory & Storage Systems
  * RAM, ROM, HDD, SSD
* Databases Types
* Database replication
  * Synchronous replication
  * Asynchronous replication
  * Single-Master Replication
  * Multi-Master Replication
* RAID - Redundant Array of Independent Disks

# Topic covered
* Database partitioning
    * Vertical partitioning (aka Normalisation)
    * Horizontal partitioning (aka Database Sharding)
* Hashing
    * Consistent hashing

# Topic covered
* Performance
  * Latency, Throughput, Bandwidth, Response Time
* Consistency, Availability, and Partition Tolerance (CAP)
* CAP Theorem
* Failure & Fault Tolerance

# Topic covered
* Scalability
  * Horizontal, Vertical Scaling
  * Load Balancers
* Caching
* Asynchronism
  * Message/Task Queues
    * Producer consumer model
  * Message Broker
    * Publisher subscribe model