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CCNA 200-301 Pearson uCertify Network Simulator
Cisco 200-301-SIMULATOR.AB1
CCNA 200-301 Pearson uCertify Network Simulator
ISBN: 978-1-61691-837-8Cisco 200-301-SIMULATOR.AB1
Algorithms For Beginners
Discover the fundamentals of Data Structure and Algorithms for upgrading your problem-solving skills.
(ALGO.AE1) / ISBN : 978-1-64459-539-8This course includes
Interactive Lessons
Lab
About This Course
This is a basic Algorithms for beginner’s course that teaches you the fundamentals to design, analyze, and implement effective algorithm techniques for solving real-world problems. Explore the core algorithmic concepts including sorting techniques, mapping time-complexities and resolving issues in a systematic manner. Learn about different types of algorithms, and how to analyze and implement them using Python. Additionally, this beginner’s guide to Algorithms is equipped with interactive lessons, hands-on exercises, and practical assignments that enable the learners to practice their knowledge for enhanced learning and better skill development.
Skills You’ll Get
- Create algorithms to solve computational problems
- Design, analyze, and implement efficient algorithms
- Utilize data structures like arrays, linked lists, stacks, queues, trees, graphs
- Ability to present and manipulate data effectively
- Skilled in Python programming
- Understanding of Python syntax, control flow, functions, and object-oriented programming concepts
- Expertise in identifying and resolving problems systematically
Interactive Lessons
23+ Interactive Lessons | 40+ Exercises | 150+ Quizzes | 116+ Flashcards | 116+ Glossary of terms
1
Introduction
- About This Course
- False Assumptions
- Icons Used in This Course
- Where to Go from Here
2
Introducing Algorithms
- Describing Algorithms
- Using Computers to Solve Problems
- Distinguishing between Issues and Solutions
- Structuring Data to Obtain a Solution
3
Considering Algorithm Design
- Starting to Solve a Problem
- Dividing and Conquering
- Learning that Greed Can Be Good
- Computing Costs and Following Heuristics
- Evaluating Algorithms
4
Working with Google Colab
- Defining Google Colab
- Working with Notebooks
- Performing Common Tasks
- Using Hardware Acceleration
- Executing the Code
- Getting Help
5
Performing Essential Data Manipulations Using Python
- Performing Calculations Using Vectors and Matrixes
- Creating Combinations the Right Way
- Getting the Desired Results Using Recursion
- Performing Tasks More Quickly
6
Developing a Matrix Computation Class
- Avoiding the Use of NumPy
- Understanding Why Using a Class is Important
- Building the Basic Class
- Manipulating the Matrix
7
Structuring Data
- Determining the Need for Structure
- Stacking and Piling Data in Order
- Working with Trees
- Representing Relations in a Graph
8
Arranging and Searching Data
- Sorting Data Using Merge Sort and Quick Sort
- Using Search Trees and the Heap
- Relying on Hashing
9
Understanding Graph Basics
- Explaining the Importance of Networks
- Defining How to Draw a Graph
- Measuring Graph Functionality
- Putting a Graph in Numeric Format
10
Reconnecting the Dots
- Traversing a Graph Efficiently
- Sorting the Graph Elements
- Reducing to a Minimum Spanning Tree
- Finding the Shortest Route
11
Discovering Graph Secrets
- Envisioning Social Networks as Graphs
- Navigating a Graph
12
Getting the Right Web page
- Finding the World in a Search Engine
- Explaining the PageRank Algorithm
- Implementing PageRank
- Going Beyond the PageRank Paradigm
13
Managing Big Data
- Transforming Power into Data
- Streaming Flows of Data
- Sketching an Answer from Stream Data
14
Parallelizing Operations
- Managing Immense Amounts of Data
- Working Out Algorithms for MapReduce
15
Compressing and Concealing Data
- Making Data Smaller
- Hiding Your Secrets with Cryptography
16
Working with Greedy Algorithms
- Deciding When It Is Better to Be Greedy
- Finding Out How Greedy Can Be Useful
17
Relying on Dynamic Programming
- Explaining Dynamic Programming
- Discovering the Best Dynamic Recipes
18
Using Randomized Algorithms
- Defining How Randomization Works
- Putting Randomness into your Logic
19
Performing Local Search
- Understanding Local Search
- Presenting Local Search Tricks
- Solving Satisfiability of Boolean Circuits
20
Employing Linear Programming
- Using Linear Functions as a Tool
- Using Linear Programming in Practice
21
Considering Heuristics
- Differentiating Heuristics
- Routing Robots Using Heuristics
- Explaining Path Finding Algorithms
22
Ten Algorithms That Are Changing the World
- Using Sort Routines
- Looking for Things with Search Routines
- Shaking Things Up with Random Numbers
- Performing Data Compression
- Keeping Data Secret
- Changing the Data Domain
- Analyzing Links
- Spotting Data Patterns
- Dealing with Automation and Automatic Responses
- Creating Unique Identifiers
23
Ten Algorithmic Problems Yet to Solve
- Solving Problems Quickly
- Solving 3SUM Problems More Efficiently
- Making Matrix Multiplication Faster
- Determining Whether an Application Will End
- Creating and Using One-Way Functions
- Multiplying Really Large Numbers
- Dividing a Resource Equally
- Reducing Edit Distance Calculation Time
- Playing the Parity Game
- Understanding Spatial Issues
Performing Essential Data Manipulations Using Python
- Performing Logical Operations
- Using Comparison Operators
- Performing Data Manipulations
- Finding the Factorial of a Number Using Recursion
Developing a Matrix Computation Class
- Using Matrix Operations
- Flattening a Matrix
Structuring Data
- Dealing with Missing Values
- Removing the Duplicate Records
- Creating and Traversing a Binary Tree
Arranging and Searching Data
- Implementing Quick Sort
- Implementing Merge Sort
- Building and Searching a Binary Heap
Understanding Graph Basics
- Implementing a Graph
- Adding a Graph to a Matrix
- Using Sparse Representations
Reconnecting the Dots
- Creating a Minimum Spanning Tree
- Adding a Negative Edge to a Graph
- Using the Floyd-Warshall Algorithm
Getting the Right Web page
- Creating a Network with a Spider Trap
Managing Big Data
- Demonstrating the Bloom Filter
Parallelizing Operations
- Using the lambda Function
- Setting up a MapReduce Simulation
Compressing and Concealing Data
- Using Compression
- Using Encryption
Working with Greedy Algorithms
- Using the change Function
Relying on Dynamic Programming
- Printing a Fibonacci Sequence Using Recursion
- Using Dynamic Programming
Using Randomized Algorithms
- Creating a Histogram
- Creating a Monte Carlo Simulation
- Implementing the Quick Select Algorithm
- Computing the Median of a Series
Employing Linear Programming
- Visualizing Data Using Python Plotting Functions
Considering Heuristics
- Creating a Maze
Any questions?Check out the FAQs
Still have unanswered questions and need to get in touch?
Contact Us NowIt is a beginner-friendly course that does not really need any prior experience. However, it is recommended to have basic programming skills in Python and knowledge of fundamental data structures and algorithms concepts for fast learning.
Algorithms are the basis of programming that provide a step-by-step structure to resolve a problem or achieve a specified task. It is an essential tool for programmers as it helps them to create a systematic approach, improve code efficiency, and enable the development of complex software applications.
Absolutely Yes! Learning Algorithms and Data Structure is totally worth your time and investment as a beginner. Both the courses go hand-in-hand. Algorithms usually depend on specific data structures for storing and manipulation. Additionally, this will also give you a solid foundation for learning advanced-level courses on machine learning, artificial intelligence, and systems design.
You’ll learn Python in this beginner’s Algorithms course.
This course is ideal for all those wanting to learn Data Structure and Algorithms, one of the most sought-after skills in IT. Especially those in the field of computer science, software development, data science, research, and problem-solving will benefit greatly from this course.
