A database is any collection of related information. DBMS is special software that helps the user to create and maintain a database.
Transaction control language to manage transactions in a database using DML(…
Machine Learning is about Creating an algorithm for which the computer finds a model to fit the data as best as possible and accurately predict.
Object-Oriented Programming refers to the programming paradigm defined using objects instead of only functions and methods. The objects contain data, called attributes and methods (behaviors).
A class is a code template or a blueprint to create an object. The class provides attributes and methods to the object created. A class is a logical entity and does not consume memory at run time.
An object is referred to as a run-time instance created from a class during execution. Objects are considered real-world entities. Object consumes memory when created.
Attribute: Attributes are variable of a class that is shared between all instances
Python is a high-level, general-purpose programming language. Python is used for web development, AI, machine learning, operating systems, mobile application development, and video games.
For the most part, Python is an interpreted language and not a compiled one, although compilation is a step. Python code, written in a .py file, is first compiled to a bytecode stored with a .pyc or .pyo format.
The compiler converts the high-level language to machine-readable code (bytecode)
My journey to Data Science(part 3.1)
Samples are drawn because it would take a lot of time and money to collect the entire population data; before we can analyze and get inferences from the sample data, statistical tests are performed to check whether the sample drawn is from the population under study.
Correlation is used to test relationships between variables, and it is a measure of how things are related.
It measures the direction and the magnitude of the relationship between two variables. …
My journey to Data Science (Part 3)
Statistics is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data.
Data gathered are all raw data, and raw data do not provide meaningful information. That's why we need statistics to collect, organize and analyze data. With statistics, basic questions like which observation is the most occurring? Is there a difference between the two experiments? Is the collected sample a representation of the population? Is the result obtained significant enough to make a difference? These questions can be answered by statistics and transform the raw data into meaningful information.
The field of data science revolves around Probability and statistics. Hence, it is crucial to have a solid understanding of these concepts.
Probability is the science of uncertainty. Whenever there is a doubt of an event occurring, probability concepts are used to estimate the likelihood of the event.
Machine learning involves lots of uncertainties.
This is My Journey to data science, How I Learned
Data science is an interdisciplinary field that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insight that analysts and business users can translate into tangible business value.