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Hello All,I am very very new to big data. I am exploring a commercial solution to traffic/congestion problem. To that effect, I am looking for your tips and suggestions on where I can find the following1) Raw anonymized/non-personal location tracking data on cell-phone subscribers. This is to understand traffic/commute patters. Ideally this data should be resolvable to a commute starting address and a commute ending address. I think cellular network carriers sell this data. Not sure how to get samples of this. I also understand Census survey contains some of this this data but to the best of my understanding it only has city of residence and city of work so it is somewhat non-specific.2) Any pointers on tools/algorithms to resolve what would be I suppose GPS coordinate data to shortest distance and shortest time routes that are typically seen in map guidance systems. I am assuming that technology is common enough that there should be standard algorithms/tools for it.Again, I appreciate any suggestions and pointers in advance.See More

Here is our selection of featured articles, resources and forum questions posted since Monday:Technical ResourcesFree Book: Foundations of Data Science (from Microsoft Research Lab) Deep Learning Explainability: Hints from Physics 29 Statistical Concepts Explained in Simple English - Part 13 A Complete Machine Learning Project Walk-Through in Python Free Textbook: Probability Course, Harvard University (Based on R) 19 Courses (MOOC) on Math & Stats for Data Science & Machine Learning An Introduction to Python Virtual Environment Forum QuestionsQuestion: Traffic/commute data and processingQuestion: Moments of Order Statistics Question about the big O notation Question: Data science audio book for layperson ArticlesShould You Be Recommending Deep Learning Solutions in Your Company? Implementing Knowledge Graphs in Enterprises - Some Tips and Trends Price Forecasting: Electricity, Flights, Hotels, Real Estate, and Stock Pricing Technology Use as a Function of Device Type Unsupervised learning and its role in the knowledge discovery process Prediction of Customer Churn with Machine Learning UAE Making Strides in Blockchain Applied AI in Aviation Enjoy the reading!See More

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This article was written by Swati Kashyap. Swati is a data science & analytics enthusiast. Currently,she is learning data science at Analytics Vidhya.Mathematics & Statistics are the founding steps for data science and machine learning. Most of the successful data scientists I know of, come from one of these areas – computer science, applied mathematics & statistics or economics. If you wish to excel in data science, you must have a good understanding of basic algebra and statistics.However, learning Maths for people not having background in mathematics can be intimidating. First, you have to identify what to study and what not. The list can include Linear Algebra, calculus, probability, statistics, discrete mathematics, regression, optimization and many more topics. What do you do? How deep to you want to get in each of these topics? It is very difficult to navigate through this by yourself.If you have faced this situation before – don’t worry! You are at the right place now. I have done the hard work for you. Here is a list of popular open courses on Maths for Data science from Coursera, edX, Udemy and Udacity. The list has been carefully curated to give you a structured path to teach you the required concepts of mathematics used in data science.Which course is suitable for you?To help you navigate through the courses, I have divided the article into beginners, intermediate and advanced section. Choose your level of expertise in mathematics before delving further. Further, I have added the pre-requisites for each course. You can check if you know these topics before starting the course.Few courses may require you to finish the preceding course for better understanding. So, make sure that you either know the subject or have undergone these courses.Read on to find out the right course for you!Beginners Mathematics / StatisticsData Science Maths SkillsIntro to Descriptive StatisticsIntro to Inferential StatisticsIntroduction to Probability and DataMath is Everywhere: Applications of Finite MathProbability: Basic Concepts & Discrete Random VariablesMathematical Biostatistics Boot Camp 1Applications of Linear Algebra Part 1Introduction to Mathematical ThinkingIntermediate Mathematics / StatisticsBayesian Statistics: From Concept to Data AnalysisGame Theory 1Game Theory II: Advanced ApplicationsAdvanced Linear Models for Data Science 1: Least SquaresAdvanced Linear Models for Data Science 2: Statistical Linear ModelsIntroduction to Linear Models and Matrix AlgebraMaths in SportsAdvanced Mathematics / StatisticsDiscrete OptimizationStatistics for Genomic Data ScienceBiostatistics for Big Data ApplicationsTo check out all this information, click here.See More

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