Pinned Repositories
Cleaning-Data-in-R_Datacamp
It's commonly said that data scientists spend 80% of their time cleaning and manipulating data and only 20% of their time actually analyzing it. For this reason, it is critical to become familiar with the data cleaning process and all of the tools available to you along the way. This course provides a very basic introduction to cleaning data in R using the tidyr, dplyr, and stringr packages. After taking the course you'll be able to go from raw data to awesome insights as quickly and painlessly as possible!
datasciencecoursera
Data Science Specialization: Course created by Johns Hopkins University; About This Specialization Ask the right questions, manipulate data sets, and create visualizations to communicate results. This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
datasharing
The Leek group guide to data sharing
deeplearning-models
A collection of various deep learning architectures, models, and tips
dlaicourse
Notebooks for learning deep learning
ExData_Plotting1
Plotting Assignment 1 for Exploratory Data Analysis
Exploratory-Data-Analysis
Course Description When your dataset is represented as a table or a database, it's difficult to observe much about it beyond its size and the types of variables it contains. In this course, you'll learn how to use graphical and numerical techniques to begin uncovering the structure of your data. Which variables suggest interesting relationships? Which observations are unusual? By the end of the course, you'll be able to answer these questions and more, while generating graphics that are both insightful and beautiful.
GettingandCleaningData_Project_Coursera_JHU
About this Course Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data.
Importing-cleaning-data-in-R-case-studies_Datacamp
Running exciting analyses on interesting datasets is the dream of every data scientist. But first, some importing and cleaning must be done. In this series of four case studies, you'll revisit key concepts from our courses on importing and cleaning data in R.
Introduction-to-Data-Science-in-Python
Coursera solutions - www.coursera.org/learn/python-data-analysis
fnazifa's Repositories
fnazifa/Cleaning-Data-in-R_Datacamp
It's commonly said that data scientists spend 80% of their time cleaning and manipulating data and only 20% of their time actually analyzing it. For this reason, it is critical to become familiar with the data cleaning process and all of the tools available to you along the way. This course provides a very basic introduction to cleaning data in R using the tidyr, dplyr, and stringr packages. After taking the course you'll be able to go from raw data to awesome insights as quickly and painlessly as possible!
fnazifa/datasciencecoursera
Data Science Specialization: Course created by Johns Hopkins University; About This Specialization Ask the right questions, manipulate data sets, and create visualizations to communicate results. This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
fnazifa/datasharing
The Leek group guide to data sharing
fnazifa/deeplearning-models
A collection of various deep learning architectures, models, and tips
fnazifa/dlaicourse
Notebooks for learning deep learning
fnazifa/ExData_Plotting1
Plotting Assignment 1 for Exploratory Data Analysis
fnazifa/Exploratory-Data-Analysis
Course Description When your dataset is represented as a table or a database, it's difficult to observe much about it beyond its size and the types of variables it contains. In this course, you'll learn how to use graphical and numerical techniques to begin uncovering the structure of your data. Which variables suggest interesting relationships? Which observations are unusual? By the end of the course, you'll be able to answer these questions and more, while generating graphics that are both insightful and beautiful.
fnazifa/GettingandCleaningData_Project_Coursera_JHU
About this Course Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data.
fnazifa/Importing-cleaning-data-in-R-case-studies_Datacamp
Running exciting analyses on interesting datasets is the dream of every data scientist. But first, some importing and cleaning must be done. In this series of four case studies, you'll revisit key concepts from our courses on importing and cleaning data in R.
fnazifa/Introduction-to-Data-Science-in-Python
Coursera solutions - www.coursera.org/learn/python-data-analysis
fnazifa/Introduction-to-the-Tidyverse_Datacamp
Course Description This is an introduction to the programming language R, focused on a powerful set of tools known as the "tidyverse". In the course you'll learn the intertwined processes of data manipulation and visualization through the tools dplyr and ggplot2. You'll learn to manipulate data by filtering, sorting and summarizing a real dataset of historical country data in order to answer exploratory questions. You'll then learn to turn this processed data into informative line plots, bar plots, histograms, and more with the ggplot2 package. This gives a taste both of the value of exploratory data analysis and the power of tidyverse tools. This is a suitable introduction for people who have no previous experience in R and are interested in learning to perform data analysis.
fnazifa/MySQL-Snippets
⌗ Learn MySQL.
fnazifa/problemsolving
fnazifa/ProgrammingAssignment2
Repository for Programming Assignment 2 for R Programming on Coursera
fnazifa/Python-Email
Simple Example of SMTP Protocol for Emailing
fnazifa/Random_Scripts
My Own Creations & Stack Overflow Answers To Questions
fnazifa/SQL_Quiz_Stanford_University
This course provides comprehensive coverage of the long-accepted standard query language for relational database systems. The content will be available indefinitely. You will be notified by email of any changes to content availability beforehand.
fnazifa/Working-with-data-in-the-tidyverse-Datacamp
Course Description In this course, you'll learn to work with data using tools from the tidyverse in R. By data, we mean your own data, other people's data, messy data, big data, small data - any data with rows and columns that comes your way! By work, we mean doing most of the things that sound hard to do with R, and that need to happen before you can analyze or visualize your data. But work doesn't mean that it is not fun - you will see why so many people love working in the tidyverse as you learn how to explore, tame, tidy, and transform your data. Throughout this course, you'll work with data from a popular television baking competition called "The Great British Bake Off."
fnazifa/Working-with-Dates-and-Times-in-R_Datacamp
Course Description Dates and times are abundant in data and essential for answering questions that start with when, how long, or how often. However, they can be tricky, as they come in a variety of formats and can behave in unintuitive ways. This course teaches you the essentials of parsing, manipulating, and computing with dates and times in R. By the end, you'll have mastered the lubridate package, a member of the tidyverse, specifically designed to handle dates and times. You'll also have applied your new skills to explore how often R versions are released, when the weather is good in Auckland (the birthplace of R), and how long monarchs ruled in Britain.