Data Structures and Algorithms Full Tutorial in 9 Hours | [Complete Course] Beginner to Advance

In this video, We are explaining Data Structures and Algorithms Full Tutorial. Please do watch the complete video for in-depth information.

Breakdown of Content in this Video :

• 04:20 - introduction data structure & Algorithm
• 08:41 - Types of Data Structure
• 25:33 - Asymptotic Notations
• 54:58- Array in dsa
• 01:15:04 - Concepts of the stack
• 01:39:13 - Tower of Hanoi
• 01:53:01 - evaluation of postfix & infix
• 02:09:10 - infix to postfix conversion
• 02:22:43 - infix to postfix conversion with help of stack concepts
• 02:36:17 -queue
• 02:49:54 - circulate queue
• 03:04:12 - linked list in dsa
• 03:23:22 - circulate linked list in dsa
• 03:34:13 - doubly linked list in dsa
• 03:47:21 - tree in dsa
• 04:10:07 - binary tree
• 04:22:57 - representation of a binary tree
• 04:36:00 - preorder traversals
• 04:47:36 - in order traversal
• 04:57:15 - post order traversal
• 05:07:25 - binary search tree
• 05:21:39 – Deletion into Binary Search tree
• 05:34:24 - AVL tree in dsa
• 05:53:08 - AVL tree insertion
• 06:14:43 - AVL tree rotation
• 06:32:28 - AVL tree Examples
• 06:46:57 - insertion in heap tree
• 07:03:09 - deletion in heap tree
• 07:13:04 - B tree insertion
• 07:28:43 - introduction to graph
• 07:51:25 - representation of a graph
• 08:05:27 - spanning tree
• 08:23:36 - prim’s algorithm
• 08:33:39 - shortest path algorithm
• 08:53:44 - graph traversal
• 09:04:19 - graph traversal Depth-first search

#algorithms #data-structures #developer

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Data Structures Easy to Advanced Course - Full Tutorial from a Google Engineer

Learn and master the most common data structures in this full course from Google engineer William Fiset. This course teaches data structures to beginners using high quality animations to represent the data structures visually.

You will learn how to code various data structures together with simple to follow step-by-step instructions. Every data structure presented will be accompanied by some working source code (in Java) to solidify your understanding.
⭐️ Course Contents ⭐️
⌨️ (0:00:00) Abstract data types
⌨️ (0:04:28) Introduction to Big-O
⌨️ (0:17:00) Dynamic and Static Arrays
⌨️ (0:27:40) Dynamic Array Code
⌨️ (0:35:03) Linked Lists Introduction
⌨️ (0:49:16) Doubly Linked List Code
⌨️ (0:58:26) Stack Introduction
⌨️ (1:09:40) Stack Implementation
⌨️ (1:12:49) Stack Code
⌨️ (1:15:58) Queue Introduction
⌨️ (1:22:03) Queue Implementation
⌨️ (1:27:26) Queue Code
⌨️ (1:31:32) Priority Queue Introduction
⌨️ (1:44:16) Priority Queue Min Heaps and Max Heaps
⌨️ (1:49:55) Priority Queue Inserting Elements
⌨️ (1:59:27) Priority Queue Removing Elements
⌨️ (2:13:00) Priority Queue Code
⌨️ (2:28:26) Union Find Introduction
⌨️ (2:33:57) Union Find Kruskal’s Algorithm
⌨️ (2:40:04) Union Find - Union and Find Operations
⌨️ (2:50:30) Union Find Path Compression
⌨️ (2:56:37) Union Find Code
⌨️ (3:03:54) Binary Search Tree Introduction
⌨️ (3:15:57) Binary Search Tree Insertion
⌨️ (3:21:20) Binary Search Tree Removal
⌨️ (3:34:47) Binary Search Tree Traversals
⌨️ (3:46:17) Binary Search Tree Code
⌨️ (3:59:26) Hash table hash function
⌨️ (4:16:25) Hash table separate chaining
⌨️ (4:24:10) Hash table separate chaining source code
⌨️ (4:35:44) Hash table open addressing
⌨️ (4:46:36) Hash table linear probing
⌨️ (5:00:21) Hash table quadratic probing
⌨️ (5:09:32) Hash table double hashing
⌨️ (5:23:56) Hash table open addressing removing
⌨️ (5:31:02) Hash table open addressing code
⌨️ (5:45:36) Fenwick Tree range queries
⌨️ (5:58:46) Fenwick Tree point updates
⌨️ (6:03:09) Fenwick Tree construction
⌨️ (6:09:21) Fenwick tree source code
⌨️ (6:14:47) Suffix Array introduction
⌨️ (6:17:54) Longest Common Prefix (LCP) array
⌨️ (6:21:07) Suffix array finding unique substrings
⌨️ (6:25:36) Longest common substring problem suffix array
⌨️ (6:37:04) Longest common substring problem suffix array part 2
⌨️ (6:43:41) Longest Repeated Substring suffix array
⌨️ (6:48:13) Balanced binary search tree rotations
⌨️ (6:56:43) AVL tree insertion
⌨️ (7:05:42) AVL tree removals
⌨️ (7:14:12) AVL tree source code
⌨️ (7:30:49) Indexed Priority Queue | Data Structure
⌨️ (7:55:10) Indexed Priority Queue | Data Structure | Source Code

📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=RBSGKlAvoiM&list=PLWKjhJtqVAblfum5WiQblKPwIbqYXkDoC&index=3
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#data structures #data structures easy to advanced course #google engineer #william fiset #data structures easy to advanced course - full tutorial from a google engineer #advanced course

Learn Data Science Tutorial - Full Course for Beginners. DO NOT MISS!!!

Learn Data Science is this full tutorial course for absolute beginners. Data science is considered the “sexiest job of the 21st century.” You’ll learn the important elements of data science. You’ll be introduced to the principles, practices, and tools that make data science the powerful medium for critical insight in business and research. You’ll have a solid foundation for future learning and applications in your work. With data science, you can do what you want to do, and do it better. This course covers the foundations of data science, data sourcing, coding, mathematics, and statistics.
⭐️ Course Contents ⭐️
⌨️ Part 1: Data Science: An Introduction: Foundations of Data Science

• Welcome (1.1)
• Demand for Data Science (2.1)
• The Data Science Venn Diagram (2.2)
• The Data Science Pathway (2.3)
• Roles in Data Science (2.4)
• Teams in Data Science (2.5)
• Big Data (3.1)
• Coding (3.2)
• Statistics (3.3)
• Business Intelligence (3.4)
• Do No Harm (4.1)
• Methods Overview (5.1)
• Sourcing Overview (5.2)
• Coding Overview (5.3)
• Math Overview (5.4)
• Statistics Overview (5.5)
• Machine Learning Overview (5.6)
• Interpretability (6.1)
• Actionable Insights (6.2)
• Presentation Graphics (6.3)
• Reproducible Research (6.4)
• Next Steps (7.1)

⌨️ Part 2: Data Sourcing: Foundations of Data Science (1:39:46)

• Welcome (1.1)
• Metrics (2.1)
• Accuracy (2.2)
• Social Context of Measurement (2.3)
• Existing Data (3.1)
• APIs (3.2)
• Scraping (3.3)
• New Data (4.1)
• Interviews (4.2)
• Surveys (4.3)
• Card Sorting (4.4)
• Lab Experiments (4.5)
• A/B Testing (4.6)
• Next Steps (5.1)

⌨️ Part 3: Coding (2:32:42)

• Welcome (1.1)
• Spreadsheets (2.1)
• Tableau Public (2.2)
• SPSS (2.3)
• JASP (2.4)
• Other Software (2.5)
• HTML (3.1)
• XML (3.2)
• JSON (3.3)
• R (4.1)
• Python (4.2)
• SQL (4.3)
• C, C++, & Java (4.4)
• Bash (4.5)
• Regex (5.1)
• Next Steps (6.1)

⌨️ Part 4: Mathematics (4:01:09)

• Welcome (1.1)
• Elementary Algebra (2.1)
• Linear Algebra (2.2)
• Systems of Linear Equations (2.3)
• Calculus (2.4)
• Calculus & Optimization (2.5)
• Big O (3.1)
• Probability (3.2)

⌨️ Part 5: Statistics (4:44:03)

• Welcome (1.1)
• Exploration Overview (2.1)
• Exploratory Graphics (2.2)
• Exploratory Statistics (2.3)
• Descriptive Statistics (2.4)
• Inferential Statistics (3.1)
• Hypothesis Testing (3.2)
• Estimation (3.3)
• Estimators (4.1)
• Measures of Fit (4.2)
• Feature Selection (4.3)
• Problems in Modeling (4.4)
• Model Validation (4.5)
• DIY (4.6)
• Next Step (5.1)

📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=ua-CiDNNj30&list=PLWKjhJtqVAblfum5WiQblKPwIbqYXkDoC&index=7
🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.
Cryptocurrency trading is VERY risky. Make sure you understand these risks and that you are responsible for what you do with your money
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Thanks for visiting and watching! Please don’t forget to leave a like, comment and share!

#data science #learn data science #learn data science tutorial #beginners #learn data science tutorial - full course for beginners

Data Structures and Algorithms Full Tutorial in 9 Hours | [Complete Course] Beginner to Advance

In this video, We are explaining Data Structures and Algorithms Full Tutorial. Please do watch the complete video for in-depth information.

Breakdown of Content in this Video :

• 04:20 - introduction data structure & Algorithm
• 08:41 - Types of Data Structure
• 25:33 - Asymptotic Notations
• 54:58- Array in dsa
• 01:15:04 - Concepts of the stack
• 01:39:13 - Tower of Hanoi
• 01:53:01 - evaluation of postfix & infix
• 02:09:10 - infix to postfix conversion
• 02:22:43 - infix to postfix conversion with help of stack concepts
• 02:36:17 -queue
• 02:49:54 - circulate queue
• 03:04:12 - linked list in dsa
• 03:23:22 - circulate linked list in dsa
• 03:34:13 - doubly linked list in dsa
• 03:47:21 - tree in dsa
• 04:10:07 - binary tree
• 04:22:57 - representation of a binary tree
• 04:36:00 - preorder traversals
• 04:47:36 - in order traversal
• 04:57:15 - post order traversal
• 05:07:25 - binary search tree
• 05:21:39 – Deletion into Binary Search tree
• 05:34:24 - AVL tree in dsa
• 05:53:08 - AVL tree insertion
• 06:14:43 - AVL tree rotation
• 06:32:28 - AVL tree Examples
• 06:46:57 - insertion in heap tree
• 07:03:09 - deletion in heap tree
• 07:13:04 - B tree insertion
• 07:28:43 - introduction to graph
• 07:51:25 - representation of a graph
• 08:05:27 - spanning tree
• 08:23:36 - prim’s algorithm
• 08:33:39 - shortest path algorithm
• 08:53:44 - graph traversal
• 09:04:19 - graph traversal Depth-first search

#algorithms #data-structures #developer

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Data Visualization in R with ggplot2: A Beginner Tutorial

A famous general is thought to have said, “A good sketch is better than a long speech.” That advice may have come from the battlefield, but it’s applicable in lots of other areas — including data science. “Sketching” out our data by visualizing it using ggplot2 in R is more impactful than simply describing the trends we find.

This is why we visualize data. We visualize data because it’s easier to learn from something that we can see rather than read. And thankfully for data analysts and data scientists who use R, there’s a tidyverse package called ggplot2 that makes data visualization a snap!

In this blog post, we’ll learn how to take some data and produce a visualization using R. To work through it, it’s best if you already have an understanding of R programming syntax, but you don’t need to be an expert or have any prior experience working with ggplot2

#data science tutorials #beginner #ggplot2 #r #r tutorial #r tutorials #rstats #tutorial #tutorials