DEV Community: Suraj Singh

6.0001 Introduction to Computer Science and Programming in Python : Lecture 01 : What is Computation ?

Suraj Singh — Tue, 20 Jun 2023 09:58:19 +0000

Computation refers to the process of using algorithms and computational devices to perform calculations, solve problems, and process information. It involves transforming data or inputs into desired outputs through a series of well-defined steps or instructions.

Type of Knowledge

Declarative Knowledge:

Declarative knowledge focuses on describing "what" needs to be accomplished or the desired outcome, without specifying "how" to achieve it. It emphasizes the use of logic and facts to represent knowledge. Declarative knowledge is commonly associated with declarative programming languages and logic-based systems.
Imperative Knowledge

Imperative knowledge, on the other hand, focuses on providing a step-by-step procedure or a series of commands to achieve a specific goal. It describes "how" to accomplish a task by explicitly stating the sequence of actions or operations to be performed. Imperative knowledge is often more detailed and specific, providing a clear set of instructions for the computer to follow. It is well-suited for tasks that require precise control over the execution flow or complex state manipulation.

Fixed program computer VS Stored program computer

Fixed Program Computer: A fixed program computer, also known as a dedicated computer, is a type of computer that is designed to perform a specific task or set of tasks. The computer's program or instructions are permanently built into its hardware, and they cannot be changed or modified once the computer is manufactured. The program is typically hardwired or implemented using specialized circuits.
Stored Program Computer: A stored program computer, also known as a general-purpose computer, is a type of computer architecture where both the program instructions and data are stored in memory. It allows the computer to store and execute different programs, enabling versatility and flexibility.

Basic Primitives

In programming, "BASIC primitives" refer to the fundamental operations or basic building blocks available in a programming language. These primitives are the simplest and most essential instructions or constructs that form the foundation for writing more complex programs. The specific set of BASIC primitives may vary depending on the programming language. example Variables, Data types, Arithmetic Operations, Conditional Statements etc.

Static Semantic

Static semantics in programming languages refers to the set of rules and constraints that determine the validity and correctness of a program's structure and its components at compile-time, without executing the program. It focuses on the analysis of program elements such as variables, expressions, types, and their relationships to ensure they adhere to the language's specifications. for example

In English: "I are hungry" → syntactically valid but it is static semantic error
In programming language:

3.2*5 → syntactically valid
but 3+"hi" → static semantic error

Program

In computer science, a program refers to a set of instructions or statements written in a programming language that directs a computer to perform a specific task or solve a particular problem. It is a sequence of instructions that defines the algorithmic steps to be executed by a computer or computing device.

Python Program

In python program, everything is a object and each object is a type. There are two type of object in python Scalar object and Non-Scalar object.

In Python, a scalar object refers to a basic or primitive data type that represents a single value. Scalar objects are immutable, meaning their values cannot be changed once assigned. They are atomic and cannot be further divided into smaller components.

Python provides several built-in scalar object types, including:

Integers (int): Integers represent whole numbers without fractional parts. For example, 1, 10, -5 are all integers.
Floating-Point Numbers (float): Floating-point numbers represent numbers with decimal points. For example, 3.14, -0.5, 2.0 are all floating-point numbers.
Booleans (bool): Booleans represent truth values and can have two possible values: True or False. They are often used in logical expressions and control flow statements.
None: None is commonly used to indicate the absence of a return value or to initialize variables when no specific value is assigned. None is a singleton object in Python, meaning there is only one instance of it throughout the program. It is of type NoneType.

Expressions

In programming, an expression is a combination of values, variables, operators, and function calls that can be evaluated to produce a result. Expressions represent computations or operations that manipulate data and produce a value of a certain type.

Here are some example of expressions:

Arithmetic Expression:

result = 2 + 3 * 4

In this expression, 2 + 3 * 4, the operators + and * are applied to the operands 2, 3, and 4 to perform arithmetic calculations. The result will be stored in the variable result.

Comparison Expression:

is_equal = (x == y)

Here, the expression (x == y) compares the values of variables x and y using the equality operator ==. The result will be either True or False depending on whether x and y are equal.

6.0001 Introduction to Computer Science and Programming in Python by The 9-to-5 Scholar : Introduction

Suraj Singh — Sun, 18 Jun 2023 12:44:54 +0000

According to official Website, 6.0001 Introduction to Computer Science and Programming in Python is intended for students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class uses the Python 3.5 programming language.

Although I have experience with programming with JavaScript and C++, I have minimal experience with python. Hence I am taking this class to brush up basic programming course and get hands on with python.

Topic:

Here are topic that will be cover in this course:

Represent knowledge with data structures
Iteration and recursion as computational metaphors
Abstraction of procedures and data types
Organize and modularize systems using object classes and methods
Different classes of algorithms, searching and sorting
Complexity of algorithms

The 9-to-5 Scholar: Completing MIT OCW CSE with a Full time Job

Suraj Singh — Sun, 18 Jun 2023 09:30:56 +0000

Join me on an amazing journey as I, Suraj Singh, a Software Development Engineer, from New Delhi, India, set forth on an ambitious mission—to complete the comprehensive Computer Science and Engineering curriculum offered by the MIT OCW.

I will update progress every day, to keep me motivated till completion of this journey. I will share notes, insights and solution of the Problem sets.

Here is a list of all the course I will try to cover. some of them are optional.

Course Code	Course Name	Link	Tag
6.0001	Introduction to Computer Science and Programming in Python	https://ocw.mit.edu/courses/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016/
6.0002	Introduction to Computational Thinking and Data Science	https://ocw.mit.edu/courses/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016/
6.005	Software Construction	https://ocw.mit.edu/courses/6-005-software-construction-spring-2016/
6.104	Software Studio	https://ocw.mit.edu/courses/6-170-software-studio-spring-2013/pages/syllabus/
6.172	Performance Engineering Of Software Systems	https://ocw.mit.edu/courses/6-172-performance-engineering-of-software-systems-fall-2018/
6.006	Introduction To Algorithms	https://ocw.mit.edu/courses/6-006-introduction-to-algorithms-spring-2020/
6.035	Computer Language Engineering	https://ocw.mit.edu/courses/6-035-computer-language-engineering-spring-2010/pages/lecture-notes/
6.042	Mathematics For Computer Science	https://ocw.mit.edu/courses/6-042j-mathematics-for-computer-science-fall-2010/
6.046	Design And Analysis Of Algorithms	https://ocw.mit.edu/courses/6-046j-design-and-analysis-of-algorithms-spring-2015/
6.854	Advanced Algorithms	https://ocw.mit.edu/courses/6-854j-advanced-algorithms-fall-2005/pages/syllabus/	Optional
6.851	Advanced Data Structures	https://ocw.mit.edu/courses/6-851-advanced-data-structures-spring-2012/video_galleries/lecture-videos/
6.852	Distributed Algorithms	https://ocw.mit.edu/courses/6-852j-distributed-algorithms-fall-2009/
6.849	Geometric Folding Algorithms: Linkages, Origami, Polyhedra	https://ocw.mit.edu/courses/6-849-geometric-folding-algorithms-linkages-origami-polyhedra-fall-2012/pages/class-and-lecture-videos/	Optional
18.404	Theory Of Computation	https://ocw.mit.edu/courses/18-404j-theory-of-computation-fall-2020/
18.405	Advanced Complexity Theory	https://ocw.mit.edu/courses/18-405j-advanced-complexity-theory-spring-2016/	Optional
6.845	Quantum Complexity Theory	Optional
6.858	Computer Systems Security	https://ocw.mit.edu/courses/6-858-computer-systems-security-fall-2014/
6.033	Computer System Engineering	https://ocw.mit.edu/courses/6-033-computer-system-engineering-spring-2018/
6.828	Operating System Engineering	https://ocw.mit.edu/courses/6-828-operating-system-engineering-fall-2012/
6.829	Computer Networks	https://ocw.mit.edu/courses/6-829-computer-networks-fall-2002/
6.83	Database Systems	https://ocw.mit.edu/courses/6-830-database-systems-fall-2010/
6.826	Principles Of Computer Systems	https://ocw.mit.edu/courses/6-826-principles-of-computer-systems-spring-2002/
18.337	Parallel Computing	https://ocw.mit.edu/courses/18-337j-parallel-computing-fall-2011/	Optional
6.254	Game Theory With Engineering Applications	https://ocw.mit.edu/courses/6-254-game-theory-with-engineering-applications-spring-2010/
6.895	Essential Coding Theory	https://ocw.mit.edu/courses/6-895-essential-coding-theory-fall-2004/
6.441	Information Theory	https://ocw.mit.edu/courses/6-441-information-theory-spring-2010/
6.3702	Introduction To Probability	https://ocw.mit.edu/courses/res-6-012-introduction-to-probability-spring-2018/
15.075	Statistical Thinking And Data Analysis	https://ocw.mit.edu/courses/15-075j-statistical-thinking-and-data-analysis-fall-2011/
6.837	Computer Graphics	https://ocw.mit.edu/courses/6-837-computer-graphics-fall-2012/
IDS.333	IDS.333 Risk And Decision Analysis	https://ocw.mit.edu/courses/ids-333-risk-and-decision-analysis-fall-2021/	Optional

Signing off for today, let meet tomorrow, with first lecture of the journey.