This course covers how to use & program in R for effective data analysis. It covers practical issues in statistical computing: programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, & organizing and commenting R code.
Statement of Accomplishment: Data Science Specialization II - R ProgrammingFolco Bombardieri
Statement of Accomplishment for the "R Programming" Course from Coursera - 2nd Course of the Data Science Specialization series (offered by Johns Hopkins University)
Duration: 4 weeks
In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
Statement of Accomplishment: Data Science Specialization V - Reproducible Res...Folco Bombardieri
Statement of Accomplishment for the "R Programming" Course from Coursera - 5th Course of the Data Science Specialization series (offered by Johns Hopkins University)
Duration: 4 weeks
This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available. This course will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results.
Write a document using R markdown, integrate live R code into a literate statistical program, compile R markdown documents using knitr and related tools, and organize
a data analysis so that it is reproducible and accessible to others.
Obtain data from the web, APIs, databases, and colleagues in various formats, as well as the basics of cleaning and “tidying” data. It also covers the components of a complete data set: raw data, processing instructions, code-books, &
processed data.
Coding the Matrix: Linear Algebra through Computer Science ApplicationsVassilios Rendoumis
In this class, you have learned key concepts and methods of linear algebra, using them to think about problems in computer science. You have implemented basic matrix and vector functionality and algorithms, and used them to process real-world data.
Statement of Accomplishment: Data Science Specialization II - R ProgrammingFolco Bombardieri
Statement of Accomplishment for the "R Programming" Course from Coursera - 2nd Course of the Data Science Specialization series (offered by Johns Hopkins University)
Duration: 4 weeks
In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
Statement of Accomplishment: Data Science Specialization V - Reproducible Res...Folco Bombardieri
Statement of Accomplishment for the "R Programming" Course from Coursera - 5th Course of the Data Science Specialization series (offered by Johns Hopkins University)
Duration: 4 weeks
This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available. This course will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results.
Write a document using R markdown, integrate live R code into a literate statistical program, compile R markdown documents using knitr and related tools, and organize
a data analysis so that it is reproducible and accessible to others.
Obtain data from the web, APIs, databases, and colleagues in various formats, as well as the basics of cleaning and “tidying” data. It also covers the components of a complete data set: raw data, processing instructions, code-books, &
processed data.
Coding the Matrix: Linear Algebra through Computer Science ApplicationsVassilios Rendoumis
In this class, you have learned key concepts and methods of linear algebra, using them to think about problems in computer science. You have implemented basic matrix and vector functionality and algorithms, and used them to process real-world data.
Content of slide
Tree
Binary tree Implementation
Binary Search Tree
BST Operations
Traversal
Insertion
Deletion
Types of BST
Complexity in BST
Applications of BST
Covers exploratory data summarization techniques that are
applied before modeling to inform development of complex
models. Topics include plotting in R, principles of constructing graphics, and common multivariate techniques used for high dimensional data visualization.
Basics of creating data products using Shiny, R packages, and interactive graphics. Focuses on the statistical fundamentals of creating a data product that can be used to tell a story about data to a mass audience.
Go Packages, Imports, Function & Return Types, Slices and Slice Range Syntax, Custom Type Declaration, Receiver Functions, Multiple Return Types, Byte Slices, Joins on Slices of Strings, Error Handling, Testing, Random Number Generation, Element Assertion in Slices, Structs (Declaration, Definition), Updating and Embedding of Structs, Structures with Receiver Functions, Pass by Value, Structs with Pointers, Pointer Operations, Pointer Shortcuts, Reference vs Value Types, Maps, Manipulating Maps, Iterating over Maps, Maps vs Structs, Interfaces, Reader and Writer Interface, Go Routines, Channels, Channel Implementation, Blocking Channels, Repeating Routines, Alternative Loop Syntax, Sleeping a Routine, Function Literals
Configuring applications on Kubernetes, Running jobs on Kubernetes, Managing application health on Kubernetes,
Monitoring and troubleshooting applications on Kubernetes, Helm deep dive, Kubernetes under the hood, Spinning a
Kubernetes cluster, Managing resources on a Kubernetes cluster, Role-based Access Control (RBAC) on a Kubernetes
cluster, Kubernetes Networking, Microservice Architecture and development with Docker, Kubernetes blackbelt
Write C programmes independently with in-depth understanding of POINTERS, dynamic memory allocation, recursions, Arrays, Strings, functions, file handling, command line arguments, bitwise operators. Complete command over branching using if-else statement and loops (while, for and do-while) with extensive practical examples and assignments. Understand clearly Arrays and Strings, sorting arrays using bubble sort and various standard string functions. Write your own FUNCTIONS and create custom User Defined Library. Learn all about POINTERS, functions to allocate dynamic memory - malloc, calloc and realloc and free functions. Relationship between arrays and pointers. Array of pointers and simulating a dynamic 2D array using array of pointer. Command line parameter passing. File handling in details. Bitwise operators. Recursion - how it works, recursion vs iteration in depth discussion.
This course teaches basic arithmetic and variables, how to handle data structures such as Python lists, Numpy arrays and Pandas DataFrames. It introduces the Python functions and control flow as well as data visualization with Python in order to create visualizations on real data.
This course introduces the underlying statistical and algorithmic principles required to develop scalable real-world machine learning pipelines. We present an integrated view of data processing by highlighting the various components of these pipelines, including feature extraction, supervised learning, model evaluation, and exploratory data analysis. Students will gain hands-on experience applying these principles by using Apache Spark to implement several scalable learning pipelines.
In this course students learned what the expected output of Data Scientist is and how they can use PySpark (part of Apache Spark) to deliver against these expectations. The course assignments included Log Mining, Textual Entity Recognition, Collaborative Filtering exercises that teach students how to manipulate data sets using parallel processing with PySpark.
This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects.
This course covered a broad set of topics critical to practical data science: relational databases, MapReduce, NoSQL, selected topics
in statistical modeling, selected topics in machine learning, and information visualization, and a variety of algorithmic topics.
This course provided an introduction on how to think using
models. Specific topics included, among others, decision-making, tipping points, economic models, crowd dynamics, Markov processes, game theory and predictive thinking.
This introductory course taught students the basics of interactive programming in Python. Students built a collection of simple interactive games to solidify their understanding of the material.
This advanced undergraduate course treats basic principles on linear programming like the simplex algorithm, its complexity, and duality. Furthermore it gives an introduction on discrete optimization via bipartite matchings, shortest paths and the primal/dual method.
Social physics is a big data science that models how networks of people behave and uses these network models to create actionable intelligence. It is a quantitative science that can accurately predict patterns of human behavior and guide how to influence those patterns to (for instance) increase decision making accuracy or productivity within an organization. Included in this course is a survey of methods for increasing communication quality within an organization, approaches to providing greater protection for personal privacy, and general strategies for increasing resistance to cyber attack.
This course is part II of an introduction to the theory and practice of financial engineering and risk management. The course focused on portfolio optimization and the CAPM as well as the mechanics and pricing of derivative securities in various asset classes.
This graduate-level course introduces students to a variety of models and techniques for analyzing social and economic networks, including random graph models, statistical models, and game theoretic models of network formation, diffusion, learning, and peer effects.
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).
This course is an introduction to the theory and practice of
financial engineering and risk management. The course focused on the mechanics and pricing of derivative securities in various asset classes.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Unit 8 - Information and Communication Technology (Paper I).pdf
R Programming
1. coursera.org
Statement of Accomplishment
WITH DISTINCTION
MAY 09, 2014
VASSILIOS RENDOUMIS
HAS SUCCESSFULLY COMPLETED THE JOHNS HOPKINS UNIVERSITY'S OFFERING OF
R Programming
This course covers how to use & program in R for effective data
analysis. It covers practical issues in statistical computing:
programming in R, reading data into R, accessing R packages,
writing R functions, debugging, profiling R code, & organizing and
commenting R code.
ROGER D. PENG, PHD
DEPARTMENT OF BIOSTATISTICS, JOHNS HOPKINS
BLOOMBERG SCHOOL OF PUBLIC HEALTH
BRIAN CAFFO, PHD, MS
DEPARTMENT OF BIOSTATISTICS, JOHNS HOPKINS
BLOOMBERG SCHOOL OF PUBLIC HEALTH
JEFFREY LEEK, PHD
DEPARTMENT OF BIOSTATISTICS, JOHNS HOPKINS
BLOOMBERG SCHOOL OF PUBLIC HEALTH
PLEASE NOTE: THE ONLINE OFFERING OF THIS CLASS DOES NOT REFLECT THE ENTIRE CURRICULUM OFFERED TO STUDENTS ENROLLED AT
THE JOHNS HOPKINS UNIVERSITY. THIS STATEMENT DOES NOT AFFIRM THAT THIS STUDENT WAS ENROLLED AS A STUDENT AT THE JOHNS
HOPKINS UNIVERSITY IN ANY WAY. IT DOES NOT CONFER A JOHNS HOPKINS UNIVERSITY GRADE; IT DOES NOT CONFER JOHNS HOPKINS
UNIVERSITY CREDIT; IT DOES NOT CONFER A JOHNS HOPKINS UNIVERSITY DEGREE; AND IT DOES NOT VERIFY THE IDENTITY OF THE
STUDENT.