Investigate A Dataset Udacity Github

Let's take a look at the data. I complete the entire data analysis process, starting by posing a question and finishing by. Openness, curiosity to investigate and the desire to learn and contribute drives me to work on amazing plans. First, let’s look what the dataset looks like for preceding investigating. Identifying Fraud from Emails using Machine Learning. Simple data wrangling is conducted on the dataset. This project aimed to investigate WeRateDogs Twitter account. SYLLABUS Learn to Code with R and SQL. Strong analytical skills to mine raw data and unearth meaningful insights. Overview In this post, I share my Exploratory Data Analysis conducted on the TMDb dataset (a subset of IMDb dataset on Kaggle). Udacity Data Analysis Nanodegree About. Analysis a web dog rating data set. You may be asking, “Wait, I thought you were trying to get rid of perspective transformation?” And that’s true. What motivated you to share this dataset with the community on Kaggle? I didn't even know about the prize when I posted the Electoral Donations Dataset. Anscombe's Quartet was devised in 1973 as an example of how summary statistics can be misleading, but today we can even do one better: the Datasaurus Dozen is a set of twelve datasets, all extremely visually distinct, that have the same summary stats as a source dataset that, there's no other way to put this, looks like a dinosaur. For this project, we will conduct data analysis and create a file to share that documents our findings. Here is the situation: once every few days, Starbucks will send out promotions to customers on the mobile app. K-means modified inter and intra clustering (KM-I2C) All techniques used to cluster datasets using the K-means algorithm for estimating the number of clusters suffer from deficiencies of cluster similarity measures in forming distinct clusters. Vehicle Detection Project Vehicle detection is a quite highly researched area with open datasets like KITTI and others from Udacity all over the web. The dataset is downloaded from here. The Enron email + financial dataset, along with several provisional functions used in this report, is available on Udacity’s Machine Learning Engineer GitHub. By 2002, it had collapsed into bankruptcy due to widespread corporate fraud. It contains data from about 150 users, mostly senior management of Enron, organized into folders. According to the financial data set. Answer: Given that we wish to place students into discrete categories (pass, fail), we would want to use a classification learning algorithm for this task. Also, I've just graduated as a Data Analyst from Udacity and this is where my passion lies. Used Python to investigate a dataset containing demographics and passenger information from passengers and crew on board the Titanic. Message-Based Security Model for Grid Services ICCEE December 1, 2009. There is 156 people in this dataset each one identified by their last name and the first letter of their first name. Questions that will be investigated:. a total of 146 employees (i. See the complete profile on LinkedIn and discover Ankit’s connections and jobs at similar companies. I had to be careful to not go looking deep into the characteristics of each feature since there was no explicit hold-out testing set, and any record could be included in both training and testing depending on how each split was made in cross. Summary¶In 2000, Enron was one of the largest companies in the United States. Udacity will not be responsible for any credits, benefit or liability to candidates/students. Investigate_in_Movies_Dataset ‏مارس 2019 – ‏مارس 2019. This repository contains all of my core and optional projects done for Udacity's Data Analyst Nanodegree (short: DAND) programme. Introduction to TensorFlow TensorFlow is a deep learning library from Google that is open-source and available on GitHub. I recently developed a model for Kaggle's Two Sigma Financial Modeling Challenge training dataset using XGBoost regressor that cross-validation performance achieves the level of top 5% from the leaderboard. View Nirupama Puthur Venkataraman’s profile on LinkedIn, the world's largest professional community. A side by side comparison given the two datasets would help to corroborate my claims. My github repository has the code necessary to replicate each of the figures above—most look quite similar, though this data set contains much more expensive diamonds than the original. Flexible Data Ingestion. A regression algorithm, on the other hand, would be used for when we want to predict some value along a continuous scale, such as height, weight, score. Final Project (10+ hours) You've explored simulated Facebook user data and the diamonds data set. View Shubhra Aich’s profile on LinkedIn, the world's largest professional community. The datasets under investigation contain information about all US Flights in 2008 and other years. Furthermore, I will investigate if the successful strategies from above have broader applicability, by trying them on unrelated audio datasets. Student to investigate complex problems in the medical imaging domain. I used spreadsheets to make my analysis easier. It was one of Udacity’s first four Nanodegrees. As part of this program worked on projects in SQL and Python. It is part of the Udacity Data Scientist Nanodegree's capstone project and…. India's Trillion Dollar Opportunity in SaaS. We employ a state-of-the-art deconvolutional network for segmentation and convolutional architectures, with residual and Inception-like layers, to estimate traits via high dimensional nonlinear regression. From the initial 3-month phase I distinguished myself was granted a scholarships to the Mobile Web Specialist Nanodegree Program. The specifications for this project was to build an iPhone app which can take an image from the camera and album and add text to the top and bottom of the image. The dataset, feature list and algorithm were exported using the poi_id. Hi, I'm David, a Full Stack Software Engineer who loves building applications. pynb is the final submission for Udacity's DAND third assignment. View Inessa Prokofyeva’s profile on LinkedIn, the world's largest professional community. The sinking resulted in the deaths of more than 1,500 passengers and crew, making it one of the deadliest commercial peacetime maritime disasters in modern history. Focus on applying (predictive) analytics to data to support growth. Exploratory Data Analysis using R: Using either a Udacity-provided dataset, or your own, you are asked to perform exploratory data analysis while documenting all your thoughts and decisions, while creating "quick and dirty" visualizations. This project is an investigation of the Titanic passenger dataset using NumPy and Pandas. has 4 jobs listed on their profile. Sehen Sie sich das Profil von Gauthier Riou auf LinkedIn an, dem weltweit größten beruflichen Netzwerk. For full project reports, codes and dataset files, see my Github repository. The first supplementary dataset is a spreadsheet listing the stocks currently listed on the London Stock Exchange with information such as what each listed company’s stock symbol is and which sector they belong to. Recently, I've set up a company with a friend and I'm developing an online office using MERN Stack. Number of churners might seem small relative to the total number of customers but when you think of it it's large. Exploratory data analysis (EDA) is a very important step which takes place after feature engineering and acquiring data and it should be done before any modeling. The degree taught via theory and real life examples the following: Descriptive and Inferential Statistics; Data Cleaning. In the final section, you are asked to highlight your main findings, and most important aspects of the. You can even try out some of the free courses. Flexible Data Ingestion. Enron email data set is a large database of about 0. Principal Component Analysis (PCA) is used to decompose a multivariate dataset in a set of successive orthogonal components that explain a maximum amount of the variance. View Inessa Prokofyeva’s profile on LinkedIn, the world's largest professional community. The email dataset is in the maildir directory. Additionally, I created 2 buttons on the upper-left corner to enable users to switch back and forth the performance metric they want to investigate, the functionality, which helps them gain more understanding about the data. Top KDnuggets tweets, Jul 23-24: 81% of retail firms gather #BigData, only 34% use analytics - Jul 25, 2014. View Siân N. It was one of Udacity’s first four Nanodegrees. , for part 1 detail, see. For ordinal variables, although the values might not be linearly related, I assume the ordinal variables as interval variables here. To take advantage of this opportunity, fill out the career section of your Udacity professional profile, so we know more about you and your career goals! If all else fails, you can always default to emailing the career team at [email protected] Therefore, the dataset does not fully represent all the quality scores and this limits the extent of the data exploration in this project. They do that, as earlier said, the company has create a cartel and monopolize the diamonds in South Africa. arXiv:1705 10998v1 [cs AI] 31 May. 6 Jobs sind im Profil von Jose Horas aufgelistet. - Brainstormed some questions for observing the data set and understand the relationship between multiple variables. You will also learn Python library's NumPy, Pandas and matplotlib for writing data analysis code. Udacity - Data Analyst Nanodegree nd002 v8. Consequently, we compare Naive Bayes and Logistic Regression. Enron email data set is a large database of about 0. Flexible Data Ingestion. an increase in player speed, managers calling for more stolen base attempts based on strategy change, etc. Know how to investigate problems in a dataset and wrangle the data into a format that can be used. Hey @sonamgupta131, the Geocoder datasets are separately priced from the desktop installation. For ordinal variables, although the values might not be linearly related, I assume the ordinal variables as interval variables here. g emails sent from_this_person_to_poi. Feedback --Resources -- 1)Udacity Forum; 2) dimplejs. We Rate Dogs - Data Wrangling April 2019 - April 2019. 5/2015 – 4/2017- focused on data analytics and predictive modeling Conducting various data analyses in SPSS Modeler and R in order to improve internal processes and support decision makers, communicating with different roles in order to understand defined problem and identifying potential input variables for modeling, validation and transformation of raw data, analyzing relationships. 4 وظيفة مدرجة على الملف الشخصي عرض الملف الشخصي الكامل على LinkedIn وتعرف على زملاء James C. Using a combination of machine learning, data optimization, and graphics card power, the experiment is able to run efficiently on your phone's web browser without a need for backend servers. View Paras Jain's profile on LinkedIn, the world's largest professional community. 5 GB data set was composed of frames collected from two of videos while driving the Udacity car around Mountain View area in heavy traffic. The data set contains information about 10,000 movies collected from The Movie Database (TMDb), including user ratings and revenue. This dataset contains 20 million ratings and 465,000 tag applications applied to 27,000 movies by 138,000 users and was released in 4/2015. Printing a Single Row This page describes how to use Python’s break statement, which might be helpful for printing only a single problem record. After some investigation of the TensorFlow documentation, I found the definition to the concatenate() method. See the complete profile on LinkedIn and discover Rahul's connections and jobs at similar companies. We used annotated vehicle data set provided by Udacity. Both undergraduates and graduate students are welcome to take the course. I am Rahul Saxena from Delhi,India. This project involves the use of NumPy, Pandas, MatPlotlib, Seaborn and Python to analyse a dataset. Udacity also provides job placement opportunities with many of our industry partners. Upon request, students also have access to Udacity Coaches a. In a quantitative assessment by human evaluators our CIFAR10 samples were mistaken for real images around 40% of the time, compared to 10% for GAN samples drawn from a GAN baseline model. 's connections and jobs at similar companies. This plot presents the mean daily price paid per liter of diesel in the Brazilian government purchases in 2012. This investigation was not able to find a robust generalized model that would consistently be able to predict wine quality with any degree of certainty. The Rise of Diamonds. Printing a Single Row This page describes how to use Python’s break statement, which might be helpful for printing only a single problem record. It illustrates the entire data analysis process, from posing the relevant questions to finishing by sharing my findings and conclusions. Run data quality checks, track data lineage, and work with data pipelines in production. Exploratory Analysis to Find Trends in Average Movie Ratings for different Genres Dataset The IMDB Movie Dataset (MovieLens 20M) is used for the analysis. Udemy is an online learning and teaching marketplace with over 100,000 courses and 24 million students. ¶ This analysis is comprehensive and effective. Here, I build a supervised learning algorithm to identify fraudulent employees using Enron dataset. To be part of a team of high caliber professionals for challenging assignments and responsibilities, with a focused vision along with a strong and open minded thinking, wherein I can utilize my technical skills and sharpen them further so as to become a better resource in achievement of organizational goals and objectives. Predicting Boston House Prices - to build an optimal model based on a statistical analysis with the tools available. Udacity Data Analyst Nanodegree: Project 2: Investigate a Dataset (Data Analysis) - a repository on GitHub eminnett/da-nanodegree-investigate-a-dataset. If successful, then I propose a pipeline of steps that can be adopted by anyone to develop an accurate audio classifier. If you're at all like me, we had you at "play detective!". Investigate a Database In this project, you'll work with a relational database while working with PostgreSQL. Enron email data set is a large database of about 0. Have practice communicating the results of the analysis. We used annotated vehicle data set provided by Udacity. This project involves the use of NumPy, Pandas, MatPlotlib, Seaborn and Python to analyse a dataset. - As a Udacity mentor for the Data Analyst Nanodegree program, I help and guide students with their queries on introduction to data analysis including subjects such as SQL, Python, Numpy, Pandas, Scikit-Learn, Matplotlib, Practical Statistics, Hypothesis Testing, Data Wrangling, Data Analysis and Analytics using visualizations. -Cleaned the dataset and performed Exploratory Data Analysis(EDA). Top KDnuggets tweets, Jul 23-24: 81% of retail firms gather #BigData, only 34% use analytics - Jul 25, 2014. Movies-Dataset-Analysis April 2019 - April 2019. 3 hours ago · Hi! I am trying to join together data that has duplicate values of one column. View Ewa Tymoszewska’s profile on LinkedIn, the world's largest professional community. Before the creation of final poi_id. note: The Travel Agency in the Park was found after the fact but not removed since data snooping might have potentially played a role in this decision. Hi, I'm David, a Full Stack Software Engineer who loves building applications. In the resulting Federal investigation, a significant amount of typically confidential information entered into the public record, including tens of thousands of emails and detailed financial data for top executives. Exploratory Data Analysis using R: Using either a Udacity-provided dataset, or your own, you are asked to perform exploratory data analysis while documenting all your thoughts and decisions, while creating “quick and dirty” visualizations. I have downloaded a CASS dataset (last refreshed on Aug 15) and I was wondering where this dataset needs to be saved on my machine? I ran a workflow in Alteryx using CASS and in the results dialog box, it says US Database = 2018-07-15, so it's not using the dataset from August (the one I downloaded. DAND: Udacity Data Analyst Nanodegree Project 4 By Michael Lazarou 14 Mar 2017 The piecemeal review of the Udacity Data Analyst Nanodegree (DAND) continues with project 4, which focuses on exploratory data analysis (EDA), a technique associated with mathematician John Tukey. Feedback --Resources -- 1)Udacity Forum; 2) dimplejs. In this project I implemented three different, basic, convolutional neural network architectures inspired by the most state of the art models for the purpose of classifying images into four categories using the Pascal VOC 2007 data set. In this project I investigated the Enron email data. To predict those in the Enron scandal who were under some form of investigation and deemed the title, person of interest. Paras has 3 jobs listed on their profile. Next, we will investigate outliers and try to remove them. Investigate a Dog Rate Data Set February 2019 - March 2019. In this course you will learn old data analysis process like creating a problem, wrangling your data, exploring data, depict the conclusion and prediction and then present your findings. Exploratory Data Analysis using R: Using either a Udacity-provided dataset, or your own, you are asked to perform exploratory data analysis while documenting all your thoughts and decisions, while creating "quick and dirty" visualizations. 0 (2018) Part 03-Module 04-Lesson 01_Investigate a Dataset Part 12-Module 01-Lesson 01_GitHub Review. Realtime models like Yolo to better accuracy models like R-CNN to more complicated models have made this topic more and more accessible with pre-trained models. The objectives of the analysis was to summarize the data to determine (1) the relationship between the various variables of interest and (2) how the interest rates for individuals loans can be predicted with the available data. In this project, we have to analyze a dataset and then communicate our findings about it. Simple data wrangling is conducted on the dataset. Large-scale, Diverse, Driving, Video: Pick Four. They do that, as earlier said, the company has create a cartel and monopolize the diamonds in South Africa. Here, I build a supervised learning algorithm to identify fraudulent employees using Enron dataset. Explore and Summarize Data Posted on February 3, 2016 January 6, 2016 by Aishwarya Venketeswaran In this post, I will be briefly explaining how I investigated a data set (containing various features and qualities of red wines) using R and exploratory data analysis techniques, to understand the relationships between variables. Montreal, Canada Area. Dataset is copyrighted, License: Bertelsmann partners AZ Direct and Arvato Financial Solution To find potential customers in population to target for marketing campaigns by performing unsupervised learning on the general population and mail order company's existing customer dataset. Project Overview To choose any area of the world in https://www. Discovered that, while the overall chances of survival for passengers on the Titanic was low at 38. • Investigate a challenging dataset on fuel economy and learn more about problems and strategies in data analysis with Python. The dataset used in this analysis was generated by Udacity and is a dictionary with each person's name in the dataset being the key to each data dictionary. India's Trillion Dollar Opportunity in SaaS. Load a dataset and understand it’s structure using statistical summaries and data visualization. Blog of our latest news, updates, and stories for developers Udacity Mobile Web Development course now live Monday, January 6, 2014. George has 6 jobs listed on their profile. You'll complete the entire data analysis process, starting by posing a question, running appropriate SQL queries to answer your questions, and finishing by sharing your findings. Inessa has 6 jobs listed on their profile. In the resulting Federal investigation, a significant amount of typically confidential information entered into the public record, including tens of thousands of emails and detailed financial data for top executives. Flexible Data Ingestion. The classes in preparation for this project focus on the data analysis process which includes the below: Wrangling the data and posing questions usually take place at the same time or will be a back and forth process. Summary¶In 2000, Enron was one of the largest companies in the United States. As an entry-level Data Scientist, you should use and exploit GitHub to, primarily: Show your code and projects in an organized and efficient manner in. The section of the course is a Project where we perform our own data analysis to determine whether a web-site should change their page design from and old page to a new page, based on the results of an AB test on a subset of users. You'll complete the entire data analysis process, starting by posing a question, running appropriate SQL queries to answer your questions, and finishing by sharing your findings. Investigate-a-Dataset Udacity Data Analysis Project 2 Introduction. You can look for these on edX by searching for keywords data science, R, data analytics, or Spark. There is 156 people in this dataset each one identified by their last name and the first letter of their first name. Python, SQL, the Terminal, Git and Github are all taught to learn how to manipulate large datasets, perform version control, and access modern databases Data Science program designed to teach the programming fundamentals required for a career in data science. See the complete profile on LinkedIn and discover Shilpa's connections and jobs at similar companies. P5: Identify Fraud from Enron Email. Also, I've just graduated as a Data Analyst from Udacity and this is where my passion lies. View Om Prakash’s profile on LinkedIn, the world's largest professional community. Join LinkedIn Summary. The data set contained a label file with bounding boxes marking other cars, trucks and pedestrians. "No analysis is perfect, and mine could have been more insightful had I used the alternate dataset offered by Udacity in conjunction with this analysis. Openness, curiosity to investigate and the desire to learn and contribute drives me to work on amazing plans. Overview In this post, I share my Exploratory Data Analysis conducted on the TMDb dataset (a subset of IMDb dataset on Kaggle). Udacity will not be responsible for any credits, benefit or liability to candidates/students. Movies dataset analysis for Udacity's Investigate a Dataset project - Data Analyst Nanodegree. Look what we have for you! Another complete project in Machine Learning! In today's tutorial, we will be building a Credit Card Fraud Detection System from scratch! It is going to be a very. It was one of Udacity's first four Nanodegrees. That said, I had a hard time finding best practices on data augmentation and the associated pipeline using the Dataset API. 「人とつながる、未来につながる」LinkedIn (マイクロソフトグループ企業) はビジネス特化型SNSです。ユーザー登録をすると、Marinos Stathopoulosさんの詳細なプロフィールやネットワークなどを無料で見ることができます。. Python, SQL, the Terminal, Git and Github are all taught to learn how to manipulate large datasets, perform version control, and access modern databases Data Science program designed to teach the programming fundamentals required for a career in data science. What follows is my own Data science Curriculum. The degree taught via theory and real life examples the following: Descriptive and Inferential Statistics; Data Cleaning. The sinking resulted in the deaths of more than 1,500 passengers and crew, making it one of the deadliest commercial peacetime maritime disasters in modern history. Project Overview To choose any area of the world in https://www. Kenneth has 8 jobs listed on their profile. The training of the entrire network was done from scratch and carried out in a Kaggle Kernel. Import the necessary package and use pd. The degree taught via theory and real life examples the following: Descriptive and Inferential Statistics; Data Cleaning. 's connections and jobs at similar companies. The classes in preparation for this project focus on the data analysis process which includes the below: Wrangling the data and posing questions usually take place at the same time or will be a back and forth process. This project involves the use of NumPy, Pandas, MatPlotlib, Seaborn and Python to analyse a dataset. You can even try out some of the free courses. We will learn a little bit about the experiment, create a hypothesis regarding the outcome of the task, then go through the task. See the complete profile on LinkedIn and discover Paras. We used annotated vehicle data set provided by Udacity. The main emphasis of this project is to provide exploratory and explanatory analysis through stunning visualisations in the form of charts, figures, and heatmaps. openstreetmap. This project involves the use of NumPy, Pandas, MatPlotlib, Seaborn and Python to analyse a dataset. Google BigQuery Public Datasets - Feb 20, 2015. The classes in preparation for this project focus on the data analysis process which includes the below: Wrangling the data and posing questions usually take place at the same time or will be a back and forth process. , rows/observations) in the data set; with 21 features (incl. Exploratory data analysis. 4 وظيفة مدرجة على الملف الشخصي عرض الملف الشخصي الكامل على LinkedIn وتعرف على زملاء James C. A side by side comparison given the two datasets would help to corroborate my claims. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. See the complete profile on LinkedIn and discover Fábio’s connections and jobs at similar companies. K-means modified inter and intra clustering (KM-I2C) All techniques used to cluster datasets using the K-means algorithm for estimating the number of clusters suffer from deficiencies of cluster similarity measures in forming distinct clusters. See the complete profile on LinkedIn and discover James C. For this project, we will conduct data analysis and create a file to share that documents our findings. It helped Moe Alsumidaie get insight of which contributing factors that lead to poor operational design of an experiment for his clinical trial article. In it, students develop real-world data skills and learn by doing. In this project, we have to analyze a dataset and then communicate our findings about it. -Imported the TMDb movie dataset using pandas for analysis. The Boston housing data was collected in 1978 and each of the 506 entries represent aggregated data about 14 features for homes from various suburbs in Boston, Massachusetts. See the complete profile on LinkedIn and discover Ronald’s connections and jobs at similar companies. The function parseOutText() is created to parse out all text below the metadata block at the top of each email. In most programs, students have assigned mentors and communicate with them through a private chat channel that is always available in the Udacity classroom. Udacity - Data Analyst Nanodegree nd002 v8. 「人とつながる、未来につながる」LinkedIn (マイクロソフトグループ企業) はビジネス特化型SNSです。ユーザー登録をすると、Marinos Stathopoulosさんの詳細なプロフィールやネットワークなどを無料で見ることができます。. Learn the programming fundamentals required for a career in data science. Udacity's Data Analyst Nanodegree is an online-based curriculum designed to promote hands-on data analysis skills such as finding, retrieving, wrangling and delivering insights from data. The data set contained a label file with bounding boxes marking other cars, trucks and pedestrians. 0 (2018) HI-SPEED DOWNLOAD Free 300 GB with Full DSL-Broadband Speed!. 81% of retail firms gather #BigData, only 34% use analytics to drive pricing optimization; Google Brain project: Google is not really a search company. This data was collected in 1978 and each of the 506 entries represents aggregate information about 14 features of homes from various suburbs located in Boston. They do that, as earlier said, the company has create a cartel and monopolize the diamonds in South Africa. We Rate Dogs - Data Wrangling April 2019 – April 2019. What follows is my own Data science Curriculum. All Nanodegrees are built in cooperation with leading tech companies and adopt a project-based teaching approach to reflect the particular demands of the. Welcome to dennymarcels' page! View My GitHub Profile. of points) 3. As part of this program worked on projects that required programming skills on SQL and Python. Sehen Sie sich auf LinkedIn das vollständige Profil an. This repository contains: copy of the dataset "titanic_data. I'm an Aeronautical Engineer and Ph. We will learn a little bit about the experiment, create a hypothesis regarding the outcome of the task, then go through the task. ipynb is the jupyter script located in github repo subfolder here. This will give us a better understanding of where the bulk of our distribution lies. I am also working as a Student Innovation and Business Analyst at Innovation Enterprise to design some custom Queries, Analyses, Reports, and Dashboards using Inteum Analytics. Missing Engagement Records. By the end of the program, you will be able to use R, SQL, Command Line, and Git. The hypothesis is that higher class passengers, females, and younger people had a higher chance of surviving. Promoted by John Tukey, exploratory data analysis focuses on exploring data to understand the data's underlying structure and variables, to develop intuition about the data set, to consider how that data set came into existence, and to decide how it can be investigated with. • Allow the network to focus on certain words in question with “high resolution” and the rest at “low resolution”. I will investigate how residual. 84 which shows that 84% of the people idenitified as a POI were actually POI's and 114 predicitons were incorrect. This project involves the use of NumPy, Pandas, MatPlotlib, Seaborn and Python to analyse a dataset. "No analysis is perfect, and mine could have been more insightful had I used the alternate dataset offered by Udacity in conjunction with this analysis. Overall, the Udacity Data Analyst Nanodegree is a great program to enroll in because of the community, pricing and quality of materials. , for part 1 detail, see. The purpose of this data investigation is to determine what factors (if any) made it more likely that a person survived on the Titanic. If I were to continue further into this specific dataset, I would aim to train a classifier to correctly predict the wine category, in order to better grasp the minuteness of what makes a good. This data was collected in 1978 and each of the 506 entries represents aggregate information about 14 features of homes from various suburbs located in Boston. In this step-by-step tutorial you will: Download and install Python SciPy and get the most useful package for machine learning in Python. Understand the big data ecosystem and how to use Spark to work with massive datasets. In 2000, Enron was one of the largest companies in the United States. All code for this project can be found in the GitHub Repo and a copy of the report can be found at Project Report. We develop models and extract relevant features for automatic text summarization and investigate the performance of different models on the DUC 2001 dataset. I am also working as a Student Innovation and Business Analyst at Innovation Enterprise to design some custom Queries, Analyses, Reports, and Dashboards using Inteum Analytics. Investigate A Dataset Udacity Github. See the complete profile on LinkedIn and discover Debasree’s connections and jobs at similar companies. In this process, try to analyze a dataset and then communicate some findings of it. Sehen Sie sich auf LinkedIn das vollständige Profil an. By the end of the program, you will be able to use R, SQL, Command Line, and Git. Download Open Datasets on 1000s of Projects + Share Projects on One Platform. 5 GB data set was composed of frames collected from two of videos while driving the Udacity car around Mountain View area in heavy traffic. It is part of the Udacity Data Scientist Nanodegree's capstone project and…. See the complete profile on LinkedIn and discover Ronald’s connections and jobs at similar companies. Learn More. Udacity Data Analyst Nanodegree Project 5: Identify Fraud from Enron Email-by Tran Buu Thong-Project Overview. Principal Component Analysis (PCA) is used to decompose a multivariate dataset in a set of successive orthogonal components that explain a maximum amount of the variance. Sehen Sie sich das Profil von Gauthier Riou auf LinkedIn an, dem weltweit größten beruflichen Netzwerk. Udacity Nanodegree programs represent collaborations with our industry partners who help us develop our content and who hire many of our program graduates. لدى James C. Enron email data set is a large database of about 0. First, let’s look what the dataset looks like for preceding investigating. openstreetmap. Ankit has 8 jobs listed on their profile. Explore our catalog of online degrees, certificates, Specializations, & MOOCs in data science, computer science, business, health, and dozens of other topics. See the complete profile on LinkedIn and discover Prasad’s connections and jobs at similar companies. Manish has 1 job listed on their profile. These exports can be verified using the tester. Recommendation engine for case. A cursory investigation of the dataset will determine how many individuals fit into either group, and will tell us about the percentage of these individuals making more than 50,000 USD. Review: Udacity Data Analyst Nanodegree Program. 6 Jobs sind im Profil von Jose Horas aufgelistet. Openness, curiosity to investigate and the desire to learn and contribute drives me to work on amazing plans. See the complete profile on LinkedIn and discover Debasree’s connections and jobs at similar companies. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. From this labeled dataset, we will be able to build a decision tree to help the car make it's decision : "Should I go slow or fast?". [2] Unfortunately, there were no examples of how to construct a pipeline for augmentation, thus will use this. • Applying programming skills to work with messy, complex datasets. 4%, the groups that had the greatest chances of survival were females (74. We start by taking a look at the dataset and brainstorming what questions we could answer using it. These include dropping duplicate rows and removing rows with null values in certain columns. dataset and a POI classifier is to be created which would help predict person of interest. The data set contains 113,937 loans and 84 variables. Review: Udacity Data Analyst Nanodegree Program. Udacity, Data Analyst Nanodegree, Project 3/6, Summarize Data, Exploratory Data Analysis for the White wines dataset July 2018 Achieving with descriptive and explorative methods the analysis of the Portuguese white wine variants "Vinho Verde" by applying univariate, bivariate, multivariate and linear regression. Continuing the DAND track on Udacity I am about to submit my completed project 2: Investigating a Dataset. Udacity Data Analyst Nanodegree: Project 2: Investigate a Dataset (Data Analysis) - a repository on GitHub eminnett/da-nanodegree-investigate-a-dataset. The Project. I've supplemented the material in our program with Udemy and Udacity courses and am preparing to compete in my first Kaggle competition as part of a Udacity capstone project. K-means modified inter and intra clustering (KM-I2C) All techniques used to cluster datasets using the K-means algorithm for estimating the number of clusters suffer from deficiencies of cluster similarity measures in forming distinct clusters. India's Trillion Dollar Opportunity in SaaS. The Boston housing data was collected in 1978 and each of the 506 entries represent aggregated data about 14 features for homes from various suburbs in Boston, Massachusetts. In 2000, Enron was one of the largest companies in the United States. 6 Jobs sind im Profil von Jose Horas aufgelistet. This project involves the use of NumPy, Pandas, MatPlotlib, Seaborn and Python to analyse a dataset. Movies dataset analysis for Udacity's Investigate a Dataset project - Data Analyst Nanodegree. The fact-checkers, whose work is more and more important for those who prefer facts over lies, police the line between fact and falsehood on a day-to-day basis, and do a great job. Today, my small contribution is to pass along a very good overview that reflects on one of Trump’s favorite overarching falsehoods. Namely: Trump describes an America in which everything was going down the tubes under  Obama, which is why we needed Trump to make America great again. And he claims that this project has come to fruition, with America setting records for prosperity under his leadership and guidance. “Obama bad; Trump good” is pretty much his analysis in all areas and measurement of U.S. activity, especially economically. Even if this were true, it would reflect poorly on Trump’s character, but it has the added problem of being false, a big lie made up of many small ones. Personally, I don’t assume that all economic measurements directly reflect the leadership of whoever occupies the Oval Office, nor am I smart enough to figure out what causes what in the economy. But the idea that presidents get the credit or the blame for the economy during their tenure is a political fact of life. Trump, in his adorable, immodest mendacity, not only claims credit for everything good that happens in the economy, but tells people, literally and specifically, that they have to vote for him even if they hate him, because without his guidance, their 401(k) accounts “will go down the tubes.” That would be offensive even if it were true, but it is utterly false. The stock market has been on a 10-year run of steady gains that began in 2009, the year Barack Obama was inaugurated. But why would anyone care about that? It’s only an unarguable, stubborn fact. Still, speaking of facts, there are so many measurements and indicators of how the economy is doing, that those not committed to an honest investigation can find evidence for whatever they want to believe. Trump and his most committed followers want to believe that everything was terrible under Barack Obama and great under Trump. That’s baloney. Anyone who believes that believes something false. And a series of charts and graphs published Monday in the Washington Post and explained by Economics Correspondent Heather Long provides the data that tells the tale. The details are complicated. Click through to the link above and you’ll learn much. But the overview is pretty simply this: The U.S. economy had a major meltdown in the last year of the George W. Bush presidency. Again, I’m not smart enough to know how much of this was Bush’s “fault.” But he had been in office for six years when the trouble started. So, if it’s ever reasonable to hold a president accountable for the performance of the economy, the timeline is bad for Bush. GDP growth went negative. Job growth fell sharply and then went negative. Median household income shrank. The Dow Jones Industrial Average dropped by more than 5,000 points! U.S. manufacturing output plunged, as did average home values, as did average hourly wages, as did measures of consumer confidence and most other indicators of economic health. (Backup for that is contained in the Post piece I linked to above.) Barack Obama inherited that mess of falling numbers, which continued during his first year in office, 2009, as he put in place policies designed to turn it around. By 2010, Obama’s second year, pretty much all of the negative numbers had turned positive. By the time Obama was up for reelection in 2012, all of them were headed in the right direction, which is certainly among the reasons voters gave him a second term by a solid (not landslide) margin. Basically, all of those good numbers continued throughout the second Obama term. The U.S. GDP, probably the single best measure of how the economy is doing, grew by 2.9 percent in 2015, which was Obama’s seventh year in office and was the best GDP growth number since before the crash of the late Bush years. GDP growth slowed to 1.6 percent in 2016, which may have been among the indicators that supported Trump’s campaign-year argument that everything was going to hell and only he could fix it. During the first year of Trump, GDP growth grew to 2.4 percent, which is decent but not great and anyway, a reasonable person would acknowledge that — to the degree that economic performance is to the credit or blame of the president — the performance in the first year of a new president is a mixture of the old and new policies. In Trump’s second year, 2018, the GDP grew 2.9 percent, equaling Obama’s best year, and so far in 2019, the growth rate has fallen to 2.1 percent, a mediocre number and a decline for which Trump presumably accepts no responsibility and blames either Nancy Pelosi, Ilhan Omar or, if he can swing it, Barack Obama. I suppose it’s natural for a president to want to take credit for everything good that happens on his (or someday her) watch, but not the blame for anything bad. Trump is more blatant about this than most. If we judge by his bad but remarkably steady approval ratings (today, according to the average maintained by 538.com, it’s 41.9 approval/ 53.7 disapproval) the pretty-good economy is not winning him new supporters, nor is his constant exaggeration of his accomplishments costing him many old ones). I already offered it above, but the full Washington Post workup of these numbers, and commentary/explanation by economics correspondent Heather Long, are here. On a related matter, if you care about what used to be called fiscal conservatism, which is the belief that federal debt and deficit matter, here’s a New York Times analysis, based on Congressional Budget Office data, suggesting that the annual budget deficit (that’s the amount the government borrows every year reflecting that amount by which federal spending exceeds revenues) which fell steadily during the Obama years, from a peak of $1.4 trillion at the beginning of the Obama administration, to $585 billion in 2016 (Obama’s last year in office), will be back up to $960 billion this fiscal year, and back over $1 trillion in 2020. (Here’s the New York Times piece detailing those numbers.) Trump is currently floating various tax cuts for the rich and the poor that will presumably worsen those projections, if passed. As the Times piece reported: