"Excel/VBA for Creative Problem Solving, Part 1" is aimed at learners who are seeking to augment, expand, optimize, and increase the efficiency of their Excel spreadsheet skills by tapping into the powerful programming, automation, and customization capabilities available with Visual Basic for Applications (VBA). This course is the first part of a three-part series and Specialization that focuses on the application of computing techniques in Excel/VBA to solve problems. In this course (Part 1), you will: 1) create macros to automate procedures in Excel; 2) define your own user-defined functions; 3) create basic subroutines to interface with the user; 4) learn the basic programming structures in VBA; and 5) automate Excel’s Goal Seek and Solver tools and use numerical techniques to create “live solutions” to solve targeting and optimization problems. New to computer programming? The extremely intuitive and visual nature of VBA lends itself nicely to teaching and learning - what a fun way to learn to code! No prior knowledge in programming nor advanced math skills are necessary yet seasoned programmers will pick up new and creative spreadsheet problem solving strategies. After you have learned the basics of VBA, each module will introduce foundational and broad problems inspired by situations that you might encounter in the real world. To pass each module, you'll need to pass a mastery quiz and complete a problem solving assignment. This course is unique in that the weekly assignments are completed in-application (i.e., on your own computer in Excel), providing you with valuable hands-on training.

By learning this course, you will get a comprehensive grasp of vector and list and the ability to use them in solving real problems. By the end of this course, you will be able to evaluate data structures and algorithms in terms of asymptotic complexity, analyze storage/time complexity of iterative/recursive algorithms, implement vector and list, understand/implement basic sorting algorithms such as Bubblesort, Insertionsort, and Selectionsort, understand/implement search algorithms such as Binary Search, Fibonacci Search, and Interpolation Search, use vector and list in problem-solving. 通过学习本课程，你将得到向量和列表的全面理解以及使用向量和列表解决实际问题的能力。 在本课程结束时，你将能够根据渐进复杂度评估数据结构和算法，分析迭代/递归算法的空间/时间复杂度，实现向量和列表， 了解/实现基本排序算法（例如冒泡排序、插入排序和选择排序），理解/实现搜索算法（例如二分搜索、斐波那契搜索和插值搜索）， 使用向量和列表解决问题。

In Spring 2011, thousands of people in Germany were hospitalized with a deadly disease that started as food poisoning with bloody diarrhea and often led to kidney failure. It was the beginning of the deadliest outbreak in recent history, caused by a mysterious bacterial strain that we will refer to as E. coli X. Soon, German officials linked the outbreak to a restaurant in Lübeck, where nearly 20% of the patrons had developed bloody diarrhea in a single week. At this point, biologists knew that they were facing a previously unknown pathogen and that traditional methods would not suffice – computational biologists would be needed to assemble and analyze the genome of the newly emerged pathogen. To investigate the evolutionary origin and pathogenic potential of the outbreak strain, researchers started a crowdsourced research program. They released bacterial DNA sequencing data from one of a patient, which elicited a burst of analyses carried out by computational biologists on four continents. They even used GitHub for the project: https://github.com/ehec-outbreak-crowdsourced/BGI-data-analysis/wiki The 2011 German outbreak represented an early example of epidemiologists collaborating with computational biologists to stop an outbreak. In this Genome Assembly Programming Challenge, you will follow in the footsteps of the bioinformaticians investigating the outbreak by developing a program to assemble the genome of the E. coli X from millions of overlapping substrings of the E.coli X genome.

"Excel/VBA for Creative Problem Solving, Part 2" builds off of knowledge and skills obtained in "Excel/VBA for Creative Problem Solving, Part 1" and is aimed at learners who are seeking to augment, expand, optimize, and increase the efficiency of their Excel spreadsheet skills by tapping into the powerful programming, automation, and customization capabilities available with Visual Basic for Applications (VBA). In Part 2 of the course, learners will: 1) learn how to work with arrays and import/export arrays from/to Excel using VBA code; 2) learn how to work with text strings and write data to .txt files and import information from .txt files; 3) automate the import, modification, and consolidation of information from multiple worksheets into a central worksheet as well as the import of information from multiple workbooks to a central workbook; and 4) gain experience with creating professional user forms to interface with the user, perform advanced calculations, and manipulate data on the spreadsheet. Learners who have a foundational understanding of VBA code and programming structures can jump right into Part 2 of the course without taking Part 1 and use the screencasts in Part 1 as reference. Each module will introduce foundational and broad problems inspired by situations that you might encounter in the real world. To pass each module, you'll need to pass a mastery quiz and complete a problem solving assignment. This course is unique in that the weekly assignments are completed in-application (i.e., on your own computer in Excel), providing you with valuable hands-on training.

Updated in November 2018 with brand new section on Dynamic Programming! This course crams months of computer science and interview prep material into 20 hours of video. The content is based directly on last semester of my in-person coding bootcamps , where my students go on to land 6-figure developer jobs . I cover the exact same computer science content that has helped my students ace interviews at huge companies like Google, Tesla, Amazon , and Facebook . Nothing is watered down for an online audience; this is the real deal :)   We start with the basics and then eventually cover “advanced topics” that similar courses shy away from like Heaps, Graphs , and Dijkstra’s Shortest Path Algorithm . I start by teaching you how to analyze your code’s time and space complexity using Big O notation .  We cover the ins and outs of Recursion .  We learn a 5-step approach to solving any difficult coding problem. We cover common programming patterns. We implement popular searching algorithms . We write 6 different sorting algorithms : Bubble, Selection, Insertion, Quick, Merge, and Radix Sort.   Then, we switch gears and implement our own data structures from scratch, including linked lists, trees, heaps, hash tables , and graphs .  We learn to traverse trees and graphs, and cover Dijkstra's Shortest Path Algorithm .  The course also includes an entire section devoted to Dynamic Programming . Here's why this course is worth your time: It's interactive -  I give you a chance to try every problem before I show you my solution. Every single problem has a complete solution walkthrough video as well as accompanying solution file. I cover helpful "tips and tricks" to solve common problems, but we also focus on building an approach to ANY problem. It's full of animations and beautiful diagrams! Are you looking to level-up your developer skills? Sign up today!

"Excellent! Thank you for all your hard work." - Mammoth Interactive student Inderpal "Great! Well explained and the instructor provides clear examples" - Mark T. Dive into a world of data science and analysis with a wide range of examples including the CIFAR 100 image dataset, Xcode development for Apple, Swift coding, CoreML, image recognition, and structuring data with pandas. This Mammoth Interactive course was funded by a #1 project on Kickstarter Learn Android Studio, Java, app development, Pycharm, Python coding, Tensforflow and more with Mammoth Interactive. Build advanced projects using machine learning including advanced the MNIST database with neuron functions. Build a text summarizer and learn object localization, object recognition and Tensorboard. Machine learning is a machine’s ability to make decisions or predictions based on previous exposure to data and extensive training. In other words, if a machine (program, app, etc.) improves its prediction accuracy through training then it has “learned”. Learn How Models Work Computational graphs consist of a network of connected nodes (often called neurons). Each of these nodes typically has a weight and a bias that helps determine, given an input, which path is the most likely. There are 4 main components to building a machine learning program: data gathering and formatting, model building, training, and testing and evaluating Data Gathering and Formatting You will learn to gather plenty of data for the model to learn from. All data should be formatted pretty much the same (images same size, same color scheme, etc.) and should be labelled. Also divide data into mutually exclusive training and testing sets. Model Building You will learn to figure out which kind of model scheme works best and what kinds of algorithms work best for the problem you’re trying to solve. Training, Testing and Evaluating The model can choose paths through the neural network or computational graph based upon the inputs for a particular run, as well as the weights and biases of neurons in the network. In supervised learning, we show the model what the correct outputs are for a given set of inputs and the model alters the weights and biases of neurons to minimize the difference between its output and the correct answer. Enroll Now to Learn with Mammoth Interactive

Computational thinking is the process of approaching a problem in a systematic manner and creating and expressing a solution such that it can be carried out by a computer. But you don't need to be a computer scientist to think like a computer scientist! In fact, we encourage students from any field of study to take this course. Many quantitative and data-centric problems can be solved using computational thinking and an understanding of computational thinking will give you a foundation for solving problems that have real-world, social impact. In this course, you will learn about the pillars of computational thinking, how computer scientists develop and analyze algorithms, and how solutions can be realized on a computer using the Python programming language. By the end of the course, you will be able to develop an algorithm and express it to the computer by writing a simple Python program. This course will introduce you to people from diverse professions who use computational thinking to solve problems. You will engage with a unique community of analytical thinkers and be encouraged to consider how you can make a positive social impact through computational thinking.

Programming is an increasingly important skill, whether you aspire to a career in software development, or in other fields. This course is the first in the specialization Introduction to Programming in C, but its lessons extend to any language you might want to learn. This is because programming is fundamentally about figuring out how to solve a class of problems and writing the algorithm, a clear set of steps to solve any problem in its class. This course will introduce you to a powerful problem-solving process—the Seven Steps—which you can use to solve any programming problem. In this course, you will learn how to develop an algorithm, then progress to reading code and understanding how programming concepts relate to algorithms.

Counting is one of the basic mathematically related tasks we encounter on a day to day basis. The main question here is the following. If we need to count something, can we do anything better than just counting all objects one by one? Do we need to create a list of all phone numbers to ensure that there are enough phone numbers for everyone? Is there a way to tell that our algorithm will run in a reasonable time before implementing and actually running it? All these questions are addressed by a mathematical field called Combinatorics. In this course we discuss most standard combinatorial settings that can help to answer questions of this type. We will especially concentrate on developing the ability to distinguish these settings in real life and algorithmic problems. This will help the learner to actually implement new knowledge. Apart from that we will discuss recursive technique for counting that is important for algorithmic implementations. One of the main `consumers’ of Combinatorics is Probability Theory. This area is connected with numerous sides of life, on one hand being an important concept in everyday life and on the other hand being an indispensable tool in such modern and important fields as Statistics and Machine Learning. In this course we will concentrate on providing the working knowledge of basics of probability and a good intuition in this area. The practice shows that such an intuition is not easy to develop. In the end of the course we will create a program that successfully plays a tricky and very counterintuitive dice game. As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.

This course is for all those people who want to learn data structure and Algorithm from absolute basic to Intermediate level. We don't expect you to have any prior knowledge on Data Structure or Algorithm, but a basic prior knowledge of any Programming Language(preferably C++) will be helpful. Thanks for stopping by !