I have obtained an AUC score of 0.809 on the test dataset, using Gradient Boosted Trees. Nearly all concepts are accompanied by a worked-out example. Dictionary A dictionary holds a combination of key value pairs which can contain any kind of object (even a list or another dictionary). Uses Python. If nothing happens, download the GitHub extension for Visual Studio and try again. What could be the most useful data structure in Python for Data Sci ? The final objective was to correctly identify all of the different date variants encoded in this dataset and to properly normalize and sort the dates. coursera-Applied-Data-Science-with-Python, download the GitHub extension for Visual Studio, Applied-Plotting-Charting-And-Data-Representation-in-Python, Applied-Social-Network-Analysis-In-Python, add solutions to weekly programme assignments, Assignments, Slides, Certificate and Readme. So in order to succeed in interviews for data science roles, it is important to have a clear idea about the kind of questions to expect. This assignment required to identify at least two publicly accessible datasets that are consistent across a meaningful dimension. This blog is the perfect guide for you to learn all the concepts required to clear a Data Science interview. It also serves me as a quick reminder of some awesome tricks that I have used for data cleaning and analysis. We're Hiring. Taking all three courses would be too in depth for the purpose of this guides. It is recommended that you should solve the assignments amd quizes by yourself honestly then only it makes sense to complete the course. After completing those, courses 4 and 5 can be taken in any order. In the second part of the assignment, I have created a spelling recommender function that uses nltk to find words similar to the misspelling. I chose the 'Even Harder' option in which I had to make the plot interactive, allowing the user to click on the y axis to set the value of interest. I also had to justify how the visual addresses my research question. github repo for rest of specialization: Data Science Coursera. For this assignment, my task was to predict whether a given blight ticket will be paid on time. Top 50 Data Science Interview Questions and Answers for 2021 Lesson - 9. I had implemented the bar coloring as described in the paper, where the color of the bar is actually based on the amount of data covered (a gradient ranging from dark blue for the distribution being certainly below this y-axis, to white if the value is certainly contained, to dark red if the value is certainly not contained as the distribution is above the axis). An optional refresher on Python is also provided. This assignment was split in two parts. They were really awesome as it required a lot of individual learning which makes the learning more memorable for future tasks. In this assignment I was asked to use pandas to answer more difficult questions. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. Learn. In the first part I've fitted the data using Linear Regression and Lasso Regression. Data Science Salary Report [Updated 2020] Lesson - 13. Designed with beginners in mind, this workshop helps you make the most of Python libraries and the Jupyter Notebook’s functionality to understand how data science can be applied to solve real-world data … Introduction to Python Introduction to R Introduction to SQL Data Science for Everyone Introduction to Tableau Introduction to Data Engineering. It gave me a glimpse on how I could run inferential statistical analyses. Embed Embed this gist in your website. Dr. Gregory Watson . Dictionaries are also super fast at doing lookups. If nothing happens, download GitHub Desktop and try again. This course focused on the fundamentals of machine learning. After completing those, courses 4 and 5 can be taken in any order. An optional refresher on Python is also provided. Nikhil2919 / Assignment-3.py. The quiz and programming homework is belong to coursera and edx and solutions to me. To help you breeze past your interview I have compiled a list of Python Data Science questions along with their model answers that you are most likely to face in your interview. This repository is aimed to help Coursera and edX learners who have difficulties in their learning process. During this course I've learned the principles of network analysis through tutorials using the NetworkX library. To pass this course, I had to complete 4 assignments. In Unit I, students gain a comprehensive introduction to scientific computing, Python, and the related tools data scientists use to succeed in their work. My solutions to the 'Applied Data Science with Python' specialization held by University of Michigan on Coursera. (2014). For this assignment I had to implement a visualization of sample data as described in Ferreria et al. Work fast with our official CLI. Photo by Derick David on Unsplash. ... DataCamp Signal accurately measures your data science skill level for free in just 10 minutes. We strongly recommend taking the Python for Data Science course before starting this course to get familiar with the Python programming language, Jupyter notebooks, and libraries. You’re working on a large project trying to predict diversity hotspots. Here is a short description of each assignment in part: In this assignment, I had to use Pandas and regex to clean and extract relevant information from medical notes. You signed in with another tab or window. A comprehensive overview of object-oriented programming in Python—the use of graphics is sure to … Next, we will apply our knowledge in combinatorics to study basic Probability Theory. Use Git or checkout with SVN using the web URL. Applied Machine Learning in Python Kevyn Collins Thompson week1 quiz answers These solutions are for reference only. Here is a short description of each assignment in part: In this assignment, I worked with real world CSV weather data. If nothing happens, download Xcode and try again. This specialization is a series of five courses, each of which focuses on some aspect of using Python for data-science … Introduction to Data Science in Python Assignment-3 - Assignment-3.py. Any ideal session in this course would dedicate a good amount of time to understanding the theoretical part after which we will be moving on to the application of theoretical concepts by doing hands-on these statistical techniques. In this assignment, I've created a model to predict if a text message is spam or not. I had to complete three different assignments during this course. Lab: Thu 7:45 pm - 8:35 pm, 60 Fifth Ave, 110. Tracks. This repository aims to be a portfolio of my work during the 'Applied Data Science With Python' specialization held by the University of Michigan on Coursera. I've also learned about more advanced topics such as network generation and link prediction. Description. THIS IS A COMPLETE DATA SCIENCE TRAINING WITH PYTHON FOR DATA ANALYSIS: It's A Full 12-Hour Python Data Science BootCamp To Help You Learn Statistical Modelling, Data Visualization, Machine Learning & Basic Deep Learning In Python! In this course I've learned how to use regular expressions to search for text and how to clean and prepare it for use by machine learning algorithms. Quiz & Assignment of Coursera View project on GitHub. Basics of this topic are critical for anyone working in Data Analysis or Computer Science. The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. Successful completion of Unit I is a prerequisite for enrollment in Unit II. See all courses . Syllabus - Advanced Python for Data Science DS-GA 3001, 3 Credits, Syllabus and Schedule, Spring 2017 Professor. Give a brief description of that data structure. The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This made my knowledge of this library stronger as I had spent quite a few time analysing its different functions. Meeting Times & Location. If nothing happens, download GitHub Desktop and try again. Which of the following are courses in the Data Science Specialization? This assignment required knowledge on both text mining as well as machine learning. The final week of the course focused on exploring more advanced techniques such as detecting topics in documents and topic modelling. In this Data Science Interview Questions blog, I will introduce you to the most frequently asked questions on Data Science, Analytics and Machine Learning interviews. Applied Data Science I: Scientific Computing & Python. This assignment was split in two parts. Created Feb 5, 2017. Question 1. The gold standard in applied data skill assessment. Check out my scripts on GitHub. In this assignment I have trained several models and evaluated how effectively they predict instances of fraud, using Support Vector Machines and Logistic Regression. I have finished the following assignments during the practical sessions of this course: In this assignment, I've acquired experience in creating bipartite graphs, how to add node attributes and how to find a weighted projection of the graph. Use Git or checkout with SVN using the web URL. Logistic Regression in R: The Ultimate Tutorial with Examples Lesson - 10. All 5 are required to earn a certificate. It has taught me how to identify which machine learning algorithm is more suitable for a particular dataset and how to engineer features and write Python code to build, train and test ML models. I've also learned how to apply basic natural language processing methods to text, and demonstrate how text classification is accomplished. This course showed me how to take tabular data, clean it, manipulate it, and run inferential statistical analyses on it. Learn more. We will illustrate new knowledge, for example, by counting the number of features in data or by estimating the time required for a Python program to run. Courses . For the second part of this assignment I've worked with a company's email network to predict salaries and new connections. Lesson - 14. These exercises assume that you are working in pairs to add and modify files in a common repository. The files are available in the data and code directories of the course repository. It also introduced me to the best practices when creating new charts and how to realize design decisions in the framework. Learn more. All gists Back to GitHub Sign in Sign up Sign in Sign up {{ message }} Instantly share code, notes, and snippets. Thanks for creating a data science specialization that guides the student to find his own tools for solving real data science problems! Work fast with our official CLI. What is Hierarchical Clustering and How Does It Work Lesson - 11. The key can be an unique id or unique tuple. After completing those, courses 4 and 5 can be taken in any order. Embed. Additionally, I overlayed a scatter plot of record breaking data for a particular year. How to Become a Data Scientist? The specialization consists of the following courses: For each course in part, I have condensed all the assignments in one major notebook for easier visualization. In the first part of the assignment I've analyzed randomly generated graphs and determined which algorithm created them. I recently completed Coursera's Applied Data Science with Python specialization, and received the accompanying certificate.This is a review of my experience with the online courses. In this assignment I was asked to conduct a hypothesis testing using three different datasets. What would you like to do? It also serves me as a quick reminder of some awesome tricks that I have used for data cleaning and analysis. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. You signed in with another tab or window. This course introduced me to information visualization basics, with a focus on reporting and charting using the matplotlib library. If nothing happens, download the GitHub extension for Visual Studio and try again.