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1.1 Data Science

Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. Data science uses complex machine learning algorithms to build predictive models.

Data Science is a field which incorporates Artificial Intelligence, Data Mining, Big Data, Machine Learning, and Deep Learning.

Solopreneur to Entrepreneur

Missing Values

Missing values occurs in dataset when some of the informations is not stored for a variable.

There are 3 mechanisms

• Missing Completely at Random, MCAR
• Missing at Random MAR
• Missing data not at random (MNAR)

1. Understanding DSA:

Data Structures refer to the organization and management of data in a way that enables efficient access and modification.

Algorithms, on the other hand, are step-by-step procedures designed to solve specific problems.

Together, they provide a powerful toolkit for programmers to tackle complex computational challenges

Recursion

The process in which a function calls itself directly or indirectly with a failure condition is called recursion.

Properties of Recursion:

Performing the same operations multiple times with different inputs.
In every step, we try smaller inputs to make the problem smaller.
Base condition is needed to stop the recursion otherwise infinite loop will occur.

How are recursive functions stored in memory?

Recursion uses more memory, because the recursive function adds to the stack with each recursive call, and keeps the values there until the call is finished. The recursive function uses LIFO structure.

Transaction

• A unit of work done against the DB in a logical sequence.
• It is a logical unit of work that contains one or more SQL statements
• All these statements in a transaction either gets
  • Completed successfully (all the changes made to the database are permanent)
  • Or if at any point any failure happens it gets rollbacked (all the changes being done are undone.)
• Sequence is very important in transaction.

Normalisation

• Normalisation is a step towards DB optimisation
• Normalisation is used to minimise the redundancy from a relations.
• It is also used to eliminate undesirable characteristics like Insertion, Update, and Deletion Anomalies.
• Normalisation divides the composite attributes into individual attributes OR larger table into smaller and links them using relationships.

Relational Model

• Data relation represented in the form of table with columns and rows are called Relational Model
• Tuple
  • Each row is known as a tuple
• Columns
  • It represents the attributes of the relation
  • Each attribute, there is a permitted value, called domain of the attribute.
• Cardinality
  • Total number of tuples in a given relation.
• Degree of table
  • Number of attributes/columns in a given table/relation.

Data Model

• Provides a way to describe the design of a DB at logical level.
• Underlying the structure of the DB is the Data Model
  • A collection of conceptual tools for describing data, data relationships, data semantics & consistency constraints.
  • E.g: ER model, Relational Model, object-oriented model, object-relational data model etc.
• Collection of conceptual tools for describing data, data relationships, data semantics, and consistency

What is Data?

• Data is a collection of raw, unorganized, unstructured facts and details
• Like text, observations, figures, symbols, and descriptions of things etc.
• Data doesn’t have any meaning unless processed.
• Types of Data
  • Quantitative
    • Numerical form
    • Weight, volume, cost of an item.
  • Qualitative
    • Descriptive, but not numerical
    • Name, gender, hair color of a person.