Data Structures and Algorithms Specialization

Start Date: 06/27/2021

Course Type: Specialization Course

Course Link: https://www.coursera.org/specializations/data-structures-algorithms

About Course

This specialization is a mix of theory and practice: you will learn algorithmic techniques for solving various computational problems and will implement about 100 algorithmic coding problems in a programming language of your choice. No other online course in Algorithms even comes close to offering you a wealth of programming challenges that you may face at your next job interview. To prepare you, we invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs. Sorry, we do not believe in multiple choice questions when it comes to learning algorithms...or anything else in computer science! For each algorithm you develop and implement, we designed multiple tests to check its correctness and running time — you will have to debug your programs without even knowing what these tests are! It may sound difficult, but we believe it is the only way to truly understand how the algorithms work and to master the art of programming. The specialization contains two real-world projects: Big Networks and Genome Assembly. You will analyze both road networks and social networks and will learn how to compute the shortest route between New York and San Francisco (1000 times faster than the standard shortest path algorithms!) Afterwards, you will learn how to assemble genomes from millions of short fragments of DNA and how assembly algorithms fuel recent developments in personalized medicine.

Course Syllabus

Algorithmic Toolbox
Data Structures
Algorithms on Graphs
Algorithms on Strings

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Course Introduction

Master Algorithmic Programming Techniques. Learn algorithms through programming and advance your software engineering or data science career Data Structures and Algorithms Specialization The Data Structures and Algorithms Specialization builds upon the data structures and algorithms covered in the Introduction to Data Structures and Programming course. Through this course, you will learn the fundamentals of data structures and algorithms. This includes data alignment and alignments, hash tables, and other data structures that are used for hierarchical data processing, as well as basic hash algorithms such as MD5 and sha256. You will also learn the basics of hashing, passwords, and hash chains. You will use the most common hash functions for hashing data, such as PBKDF2, MD5, and PBKDF3, as well as a variety of random hash functions. You will also learn basic randomness and random accesses, including hash functions using DES, BER, and AES. You will also learn the basics of hashing algorithms using PBKDF2/PBKDF3, PBKDF2/SHA256, MD5, and PBKDF3, and SHA512.Introduction to Data Structures Hash Functions Hashing, Password, and Hash Chains Hashing, Password, and Hash Chains Data Analysis Project The Data Analysis Project will challenge you to solve a set of simple computational problems in Python that are aligned to a course of study. The topics will include basic data analysis, choosing an appropriate data set, making basic assumptions, choosing an appropriate visualization, and choosing an appropriate

Course Tag

Debugging Software Testing Algorithms Data Structure Computer Programming

Related Wiki Topic

Article Example
List of terms relating to algorithms and data structures It defines a large number of terms relating to algorithms and data structures. For algorithms and data structures not necessarily mentioned here, see list of algorithms and list of data structures.
Dictionary of Algorithms and Data Structures The Dictionary of Algorithms and Data Structures is a dictionary style reference for many of the algorithms, algorithmic techniques, archetypal problems, and data structures found in the field of computer science. The dictionary is maintained by Paul E. Black and Vreda Pieterse, and is hosted by the Software and Systems Division, Information Technology Laboratory, a part of the National Institute of Standards and Technology. The new host is the FASTAR research group. It was created in September 1998.
List of data structures This is a list of data structures. For a wider list of terms, see list of terms relating to algorithms and data structures. For a comparison of running time of subset of this list see comparison of data structures.
Algorithms + Data Structures = Programs is a 1976 book written by Niklaus Wirth covering some of the fundamental topics of computer programming, particularly that algorithms and data structures are inherently related. For example, if one has a sorted list one will use a search algorithm optimal for sorted lists.
List of terms relating to algorithms and data structures The NIST Dictionary of Algorithms and Data Structures is a reference work maintained by the U.S. National Institute of Standards and Technology.
Data structure Data structures provide a means to manage large amounts of data efficiently for uses such as large databases and internet indexing services. Usually, efficient data structures are key to designing efficient algorithms. Some formal design methods and programming languages emphasize data structures, rather than algorithms, as the key organizing factor in software design. Data structures can be used to organize the storage and retrieval of information stored in both main memory and secondary memory.
Linked data structure Linked data structures include linked lists, search trees, expression trees, and many other widely used data structures. They are also key building blocks for many efficient algorithms, such as topological sort and set union-find.
Retroactive data structures At first glance the notion of a retroactive data structures seems very similar to persistent data structures since they both take into account the dimension of time. The key difference between persistent data structures and retroactive data structures is "how" they handle the element of time. A persistent data structure maintains several versions of a data structure and operations can be performed on one version to produce another version of the data structure. Since each operation produces a new version, each version thus becomes an archive that cannot be changed (only new versions can be spawned from it). Since each version does not change, the dependence between each version also does not change. In retroactive data structures we allow changes to be made directly to previous versions. Since each version is now interdependent, a single change can cause a ripple of changes of all later versions. Figures 1 and 2 show an example of this rippling effect.
Linked data structure Linked data structures may also incur in substantial memory allocation overhead (if nodes are allocated individually) and frustrate memory paging and processor caching algorithms (since they generally have poor locality of reference). In some cases, linked data structures may also use more memory (for the link fields) than competing array structures. This is because linked data structures are not contiguous. Instances of data can be found all over in memory, unlike arrays.
Component-based software engineering , modern reusable components encapsulate both data structures and the algorithms that are applied to the data structures.
Library of Efficient Data types and Algorithms The Library of Efficient Data types and Algorithms (LEDA) is a proprietarily-licensed software library providing C++ implementations of a broad variety of algorithms for graph theory and computational geometry. It was originally developed by the Max Planck Institute for Informatics Saarbrücken. Since 2001, LEDA is further developed and distributed by the Algorithmic Solutions Software GmbH.
Program optimization Given an overall design, a good choice of efficient algorithms and data structures, and efficient implementation of these algorithms and data structures comes next. After design, the choice of algorithms and data structures affects efficiency more than any other aspect of the program. Generally data structures are more difficult to change than algorithms, as a data structure assumption and its performance assumptions are used throughout the program, though this can be minimized by the use of abstract data types in function definitions, and keeping the concrete data structure definitions restricted to a few places.
Order-maintenance problem many areas, including data structure persistence, graph algorithms and fault-tolerant data structures.
List of data structures In these data structures each tree node compares a bit slice of key values.
List of data structures These are data structures used for space partitioning or binary space partitioning.
Algorithms Unlocked Algorithms Unlocked is a book by Thomas H. Cormen about the basic principles and applications of computer algorithms. The book consists of ten chapters, and deals with the topics of searching, sorting, basic graph algorithms, string processing, the fundamentals of cryptography and data compression, and an introduction to the theory of computation.
Computer science Algorithms and data structures is the study of commonly used computational methods and their computational efficiency.
3D city models Real-time rendering algorithms and data structures are listed by the virtual terrain project.
List of important publications in theoretical computer science Description: An early, influential book on algorithms and data structures, with implementations in Pascal.
Timothy M. Chan Chan has published extensively. His research covers Data Structures, Algorithms and Computational geometry.