Screening Out People With Disabilities
Employers also violate the ADA if their hiring technologies unfairly screen out a qualified individual with a disability. Employers can use qualification standards that are job-related and consistent with business necessity. But employers must provide requested reasonable accommodations that will allow applicants or employees with disabilities to meet those standards, unless doing so would be an undue hardship. When designing or choosing hiring technologies to assess whether applicants or employees have required skills, employers must evaluate whether those technologies unlawfully screen out individuals with disabilities. 3
Employers should examine hiring technologies before use, and regularly when in use, to assess whether they screen out individuals with disabilities who can perform the essential functions of the job with or without required reasonable accommodations.
For example, if a county government uses facial and voice analysis technologies to evaluate applicants skills and abilities, people with disabilities like autism or speech impairments may be screened out, even if they are qualified for the job.
Some employers try to evaluate their hiring technologies to see how they impact certain groups, like racial minorities. Employers seeking to do the same with respect to people with disabilities must keep in mind that there are many types of disabilities and hiring technologies may impact each in a different way.
What Is The Admission Process For This Data Science Certification Program
There are three manageable phases to the Data Science Certification Program admission:
How Would You In General Terms Describe Dynamic Programming As An Example How Would You Find The Length Of The Longest Common Subsequence Of Elements In Two Arrays By Using This Method
Dynamic programming is a paradigm for solving optimization problems. It consists of finding solutions for intermediate subproblems, which can be stored and reused for solving the actual problem. Dynamic programming is the best approach for difficult problems that always become trivial once we know the solution for a slightly easier instance of that problem – the intermediate subproblem. Dynamic programming solutions are best represented by a recursive relation, and easily implemented.
If the intermediate subproblems are not overlapping, then we have just a case of Divide and Conquer.
Finding the longest common subsequence between two arrays is a classical example of using dynamic programming. Let the arrays have lengths M and N, and stored as variables a and b. Lets use L to mark the length of the LCS for subarrays a and b that is, L == LCS. Lets also visualize what a matrix L would look like for an example pair of bananas and sandal.
If p or q is zero, then L = 0 since we have one empty subarray. All other fields have a simple rule connecting them – L equals to the maximum value of the following options:
- L – the LCS didnt change, we just added one letter to array a to achieve L
- L – analogous for array b
- L+1 – adding the same letter to both a and b, which of course cant happen for every field
The time complexity of this solution is O
Explain How The Encryption Algorithm Works
Encryption is the technique of converting plaintext into a secret code format it is also called as “Ciphertext.” To convert the text, the algorithm uses a string of bits called as “keys” for calculations. The larger the key, the higher the number of potential patterns for Encryption. Most of the algorithm use codes fixed blocks of input that have a length of about 64 to 128 bits, while some uses stream method for encryption.
What Are The Differences Between The B Tree And The B+ Tree
The B tree is a self-balancing m-way tree, with m defining the tree’s order. Depending on the number of m, Btree is an extension of the Binary Search tree in which a node can have more than one key and more than two children. The data is provided in the B tree in a sorted manner, with lower values on the left subtree and higher values on the right subtree.
The B+ tree is an advanced self-balanced tree since every path from the tree’s root to its leaf is the same length. The fact that all leaf nodes are the same length indicates that they all occur at the same level. Specific leaf nodes cant appear at the third level, while others appear at the second level.
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Collecting The Best Resources For Learning And Practice
Our next goal would be to collect the best learning resources: books, blogs, talks, pdf, courses, etc. Internet could be handy for this. We need to follow the authentic sources and organize them according to the above syllabus. Here is the list of some best learning resources:
Data Structures and Algorithms for beginners
- Algorithms Unlocked by Thomas Coreman, MIT Press
- Algorithmic Puzzles by Anany and Maria Levitin, Oxford University Press
- Mathematics for computer science by MIT Open Courseware
Fundamentals of Programming
For data structures and algorithms interview
- Cracking the Coding Interview by Gayle Laakmann McDowell
- Leetcode for the coding problems practice
Merge K Sorted Linked Lists
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Write An Algorithm To Insert A Node In A Link List
- Check If the Linked list does not have any value then make the node as head and return it
- Check if the value of the node to be inserted is less than the value of the head node, then insert the node at the start and make it head.
- In a loop, find the appropriate node after which the input node is to be inserted. To find the just node start from the head, keep forwarding until you reach a node whose value is greater than the input node. The node just before is the appropriate node.
- Insert the node after the proper node found in step 3.
What Is A* What Are Its Implementation Details And What Are Its Advantages And Drawbacks In Regard To Traversing Graphs Towards A Target
A* is an algorithm for pathfinding that doesnt attempt to find optimal solutions, but only tries to find solutions quickly and without wandering too much into unimportant parts of the graph.
It does this by employing a heuristic that approximates the distance of a node from the goal node. This is most trivially explained on a graph that represents a path mesh in space. If our goal is to find a path from point A to point B, we could set the heuristic to be the Euclidean distance from the queried point to point B, scaled by a chosen factor.
This heuristic is employed by adding it to our distance from the start point. Beyond that, the rest of the implementation is identical to Dijkstra.
The algorithms main advantage is the quick average runtime. The main disadvantage is the fact that it doesnt find optimal solutions, but any solution that fits the heuristic.
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What Is The Eligibility Criteria For This Data Science Certification Program
ThisData Science certification program requires the following qualifications:
What Is An Asymptotic Analysis Of An Algorithm
Asymptotic analysis is the technique of determining an algorithm’s running time in mathematical units to determine the program’s limits, also known as “run-time performance.” The purpose is to identify the best case, worst case, and average-case times for completing a particular activity. While not a deep learning training technique, Asymptotic analysis is an essential diagnostic tool for programmers to analyze an algorithm’s efficiency rather than its correctness.
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Who This Course Is For:
- Software Engineers who want to ace coding interviews in top tech firms
- 13,935 Students
I’m a Software Engineer with 10+ years of industry experience in software platform development. I have worked on wide array of technologies among them PHP, Azure , HPC , Databases and Python is where my core strength lies.
Colleagues know me as a team player, technically sound individual who can always be trusted to come up with a out of the box solution. It has been my firm belief that the organizations interest comes first, and I never try to impose my ideas on others. Instead, I spend a considerable of time understanding the business and the audience before suggesting solutions.
The desire to start a course on udemy to share with people the issues I faced with Software so I can help cut the red tape for projects and help increase productivity
Design An Algorithm That Finds The Number Of Ways In Which You Can Traverse N Meters By Doing Jumps Of 1 2 3 4 Or 5 Meter Lengths Assume That N Can Be A Very Large Number What Is The Resulting Complexity
We can use dynamic programming for solving this problem. Lets use n to represent the number of ways we can reach distance k. That distance can be reached by jumping from one of the 5 previous distances. Thus the number of ways in which we can reach this distance is the sum of the ways in which we can reach the previous 5 distances:
n = n + n + n + n + n
The solution is a simple for loop.
FUNCTION waysArray nn = 1FOR k IN n = 0FOR d IN n += nEND FOREND FORRETURN n
This solution has a time complexity of O. But, we can have even better performance. The given sum can be represented as a 1×5 matrix of ones multiplied by a 5×1 matrix of previous elements. If we use the same approach for shifting, we can get the relation B = A * B, where:
B =]]]]]A =
If B = , then * B = n. Now, due to B = A * B, B = A^T * B. With that, the solution of our problem can be represented as a relation n = * A^N * . If we use the previously mentioned optimal approach for calculating pow, this solution has an O time complexity. We have to keep in mind that this does have a high constant bound to this complexity, since matrix multiplication takes time. But, for a large enough N, this solution is optimal.
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How Employers Use Algorithms And Artificial Intelligence
Employers, including state and local government employers, increasingly use hiring technologies to help them select new employees.
For example, employers might use technology:
- to show job advertisements to targeted groups
- to decide if an applicant meets job qualifications
- to hold online video interviews of applicants
- to use computer-based tests to measure an applicants skills or abilities and
- to score applicants resumes.
Many hiring technologies are software programs that use algorithms or artificial intelligence. An algorithm is a set of steps for a computer to accomplish a taskfor example, searching for certain words in a group of resumes. Artificial intelligence generally means that a computer is completing a task that is usually done by a personfor example, recognizing facial expressions during a video interview.
While these technologies may be useful tools for some employers, they may also result in unlawful discrimination against certain groups of applicants, including people with disabilities.
What Is An Avl Tree
An AVL tree is a height balancing binary search tree in which the difference of heights of the left and right subtrees of any node is less than or equal to one. This controls the height of the binary search tree by not letting it get skewed. This is used when working with a large data set, with continual pruning through insertion and deletion of data.
What Are The Major Companies Hiring Data Science Experts In Chicago
The top firms hiring for the job title Data Science Experts are Mattersight, Uptake, and Allstate. Uptake has the highest reported compensation, with an average salary of $100,470. CCC Information Services and Allstate are firms that also pay well for this position. Doing a Data Science certification in Chicago would be a good choice to get a job in this field.
Which Sorting Algorithm Is Considered The Fastest Why
A single sorting algorithm cant be considered best, as each algorithm is designed for a particular data structure and data set. However, the QuickSort algorithm is generally considered the fastest because it has the best performance for most inputs.
Its advantages over other sorting algorithms include the following:
- Cache-efficient: It linearly scans and linearly partitions the input. This means we can make the most of every cache load.
- Can skip some swaps: As QuickSort is slightly sensitive to input that is in the right order, it can skip some swaps.
- Efficient even in worst-case input sets, as the order is generally random.
- Easy adaption to already- or mostly-sorted inputs.
- When speed takes priority over stability.
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Understanding Data Structures And Algorithms Syllabus For Coding Interview
Now our first goal should be to prepare a list of essential topics in data structure and algorithm. We can also focus on real-life applications to understand the use case of algorithms.
Algorithmic and Mathematical Thinking
Blog to explore: How to develop algorithmic thinking?
Fundamentals of Programming
Data types, Variables, Operators, Expressions, Control statements, Loops, Functions, Pointers and References, Arrays, Strings, Memory management, Fundamental of OOPS, etc.
Fundamentals of Algorithms
- Algorithm Introduction: Properties and real-life applications of algorithms
- Complexity Analysis: Input size, Rate of growth, Time complexity, Big-O notations, Worst-case analysis, Best-case analysis, Average-case analysis, Space complexity analysis, etc.
- Array: 1D Array, 2D Array, String, Dynamic Array
- Linked List: Singly linked list, Doubly linked list, Circular linked list
- Stack, Queue, and Dequeue
Non-linear Data Structures
Algorithm Design Techniques
Properties, Patterns of problem-solving and Real-life applications
Coding Interview Preparation
- Company-specific research of interview process
- Behavioral interview preparation
What Are Greedy Algorithms Give Some Example Of It
A greedy algorithm is an algorithmic strategy which is made for the best optimal choice at each sub stage with the goal of this, eventually leading to a globally optimum solution. This means that the algorithm chooses the best solution at the moment without regard for consequences.
In other words, an algorithm that always takes the best immediate, or local, solution while finding an answer.
Greedy algorithms find the overall, ideal solution for some idealistic problems, but may discover less-than-ideal solutions for some instances of other problems.
Below is a list of algorithms that finds their solution with the use of the Greedy algorithm.
- Travelling Salesman Problem
- Job Scheduling Problem
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Why Practicing Algorithms Is Key
If you are relatively new to Python and plan to start interviewing for top companies listen to this: you need to start practicing algorithms right now.
Dont be naive like I was when I first started solving them. Despite I thought that cracking a couple of algorithms every now and then was fun, I never spent too much time to practice and even less time to implement a faster or more efficient solution. Between myself, I was thinking that at the end of the day solving algorithms all day long was a bit too nerdy, it didnt really have a practical use in the real daily work environment and it would not have brought much to my pocket in the longer term.
Knowing how to solve algorithms will give you a competitive advantage during the job search process
WellI was wrong : I still think that spending too much time on algorithms without focusing on other skills is not enough to make you land your dream job, but I understood that since complex problems present themselves in every day work as a programmer, big companies had to find a standardized process to gather insights on the candidates problem solving and attention to detail skills. This means that knowing how to solve algorithms will give you a competitive advantage during the job search process as even less famous companies tend to adopt similar evaluation methods.
Algorithms Artificial Intelligence And Disability Discrimination In Hiring
May 12, 2022
This guidance explains how algorithms and artificial intelligence can lead to disability discrimination in hiring. The Department of Justice enforces disability discrimination laws with respect to state and local government employers. The Equal Employment Opportunity Commission enforces disability discrimination laws with respect to employers in the private sector and the federal government. The obligation to avoid disability discrimination in employment applies to both public and private employers.
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