Data Structures (training)

Colleagues, in the “Data Structures” training program you will acquire high-demand skills in Java, Graph Theory, Data Structures, Algorithms, C++ (Programming Language), Theoretical Computer Science, Debugging, C (Programming Language), and Programming Principles. You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments. This will help you to understand what is going on inside a particular built-in implementation of a data structure and what to expect from it. You will also learn typical use cases for these data structures. This program will address key technical questions involving: What is a good strategy of resizing a dynamic array? How priority queues are implemented in C++, Java, and Python? How to implement a hash table so that the amortized running time of all operations is O(1) on average? What are good strategies to keep a binary tree balanced? You will also learn how services like Dropbox manage to upload some large files instantly and to save a lot of storage space. Skill-based training modules focus on: 1) Basic Data Structures, 2) Dynamic Arrays and Amortized Analysis, 3) Priority Queues and Disjoint Sets, 4) Hash Tables, and 5) Binary Search Trees, and 6) Binary Search Trees 2.

Enroll today (teams & execs welcome): https://imp.i384100.net/e1Pnk6 

Much career success, Lawrence E. Wilson - Data Science Academy (subscribe & share)

Foundations: Data, Data, Everywhere

Colleagues, the “Foundations: Data, Data, Everywhere” course is part of Google Data Analytics Professional Certificate. Learn to: Define and explain key concepts involved in data analytics including data, data analysis, and data ecosystems, Conduct an analytical thinking self assessment giving specific examples of the application of analytical thinking, Discuss the role of spreadsheets, query languages, and data visualization tools in data analytics, and Describe the role of a data analyst with specific reference to jobs. You will gain high-demand skills involving Data Literacy, Data Processing, Data-Driven Decision-Making, SQL, Data Visualization, Query Languages, Exploratory Data Analysis, Data Cleansing, Data Visualization Software, Data Collection, Spreadsheet Software, Data Ethics, Business Analytics, Data Analysis, and Analytics. Skill-based training lessons address: 1) Introducing Data Analytics and Analytical Thinking, 2) The Wonderful World of Data, 3) Set Up Your Data Analytics Toolbox, and 4) Become a Fair and Impactful Data Professional. By the end of this course you will: Gain an understanding of the practices and processes employed by a junior or associate data analyst in their day-to-day job, Learn about key analytical skills (data cleaning, data analysis, data visualization) and tools (spreadsheets, SQL, R programming, Tableau) that you can add to your professional toolbox, Discover a wide variety of terms and concepts relevant to the role of a junior data analyst, such as the data life cycle and the data analysis process, Evaluate the role of analytics in the data ecosystem, Conduct an analytical thinking self-assessment, and Explore job opportunities available to you upon program completion, and learn about best practices you can leverage during your job search.

Enroll today (teams & execs welcome): imp.i384100.net/je6dXn 

Much career success, Lawrence E. Wilson - Data Science Academy (subscribe & share)

Data Structures, Algorithms and Machine Learning Optimization (training)

Colleagues, the “Data Structures, Algorithms, and Machine Learning Optimization” program provides you with a functional, hands-on understanding of the essential computer science for machine learning applications. Learn "big O" notation to characterize the time efficiency and space efficiency of a given algorithm,  use Python data structures, including list-, dictionary-, tree-, and graph-based structures, understand the essential algorithms for working with data, including those for searching, sorting, hashing, and traversing, implement statistical and machine learning approaches to optimization differ, and why you would select one or the other for a given problem you're solving, grasp versatile (stochastic) gradient descent optimization algorithm works, and familiarize yourself with the "fancy" optimizers that are available for advanced machine learning approaches. Skill-based training modules cover: 1) Orientation to Data Structures and Algorithms - Machine Learning Foundations Series, A Brief History of Data and Algorithms, and their Applications to Machine Learning; 2) "Big O" Notation - Constant, Linear and Polynomial  Time, Common Runtimes, Best versus Worst Case scenarios; 3) List-Based Data Structures - Lists, Arrays, Linked Lists, Doubly-Linked Lists, Stacks, Queues, Deques; 4) Searching and Sorting - Binary Search, Bubble-Merge-Quick Sorts; 5) Sets and Hashing - Maps and Dictionaries, Sets, Hash Functions, Collisions, Load Factor, Hash Maps, String Keys, Hashing in ML; 6) Trees - Decision Trees, Random Forests, XGBoost: Gradient-Boosted Trees; 7) Graphs - Directed versus Undirected Graphs, DAGs: Directed Acyclic Graphs, Pandas DataFrames; 8) Machine Learning Optimization - Statistics versus Machine Learning - Objective Functions, Mean Absolute Error, Mean Squared Error, Minimizing Cost with Gradient Descent, Gradient Descent from Scratch with PyTorch, Critical Points, Stochastic Gradient Descent, Learning Rate Scheduling, Maximizing Reward with Gradient Ascent; and 9) Fancy Deep Learning Optimizers - Jacobian Matrices, Second-Order Optimization and Hessians, Momentum, and Adaptive Optimizers.

Enroll today (teams & execs welcome): https://tinyurl.com/yc2dfb8f 

Much career success, Lawrence E. Wilson - Data Science Academy (subscribe & share)