edx big data course

ObjectiveThere is a big data project, you know the problem area (problem domain), you know what infrastructure to use, and maybe even decide which framework to use to process all of this data, but one decision has been delayed: which language should I choose? (or perhaps more specifically, the question is, what language should I force all my developers and

intermediate tool.3, the language is simple to get started quickly, do not need to explicitly define the variable type. For example, the following simple three lines of code, you can define a unary linear regression, is not very cool:X Y Fit At the same time, the R language has a high degree of support to vectorization, and it is an implementation of high parallel computing and avoids the use of many cyclic structures by vectorization, which is not dependent on the

With the concept of big data growing, how to build a system that can collect massive data is put in front of everyone. How to do what you can see is the result of no blocking collection, how to quickly structure and store irregular pages, how to meet more and more data acquisition in a limited time to collect. This art

features from big data, and creating models that can then be used to bring new data to the next generation and predict future data.4 , semantic engineBig data analysis is widely used in network data mining, from the user's search

e-commerce platform, such as recommendation system, Knowledge Base discovery, consumer online shopping behavior analysis. Big Data era will be everyone's entrepreneurial era, who found the demand, who to meet the demand can be profitable, of course, can also be a troubled times, the four, the narrow differentiation of the market into a competitive battlefield. 2

The popularity of big data makes many people want to develop in this direction and do some work such as data mining and data analysis. But where should I start? How can we quickly learn useful knowledge and skills? I think there are three entry points, which can be selected in order based on personal characteristics.1

The mindset to change in the big Data Age: To analyze all data, not a small sample of data To pursue the intricacies of data, not accuracy Be concerned about the relationship of things, not the causal relationship 1. Analyze all

around the consumer as the core. The mall, which is driven by consumer big data, has refined its operations, where consumer data includes the collection, processing, and integration of consumer basic demographic attributes, behaviors, preferences, social interactions, and data generated at various contact points. Shop

use data mining methods to solve practical problems with the help of computer systems and programming tools, in this way, we can mine massive data to boost business growth, and create more value for enterprises in the fierce market competition. Because the business varies with the company, but the technical points are figured out. Here I briefly summarize the technical knowledge that

is not useful at all. Of course, the query speed of statements 1, 2 is the same as the number of entries queried, if all the columns of the composite index are used, and the query results are small, so that will form an "index overlay", thus the performance can be achieved optimally. Also, keep in mind that no matter if you use other columns of the aggregated index frequently, the leading columns must be the most frequently used columns.(iii) Other c

Theory, McKinsey trilogy: McKinsey awareness, McKinsey tools, McKinsey methodologyTools: Mind Mapping, MindManager software(ii) Processing of dataA data analysis project, typically with data processing time of more than 70%, the use of advanced tools to improve efficiency, so as far as possible to learn the latest and most effective processing tools, the following is the most traditional, but very efficien

improved, the data integrity is ensured, and the relationship between the data elements is clearly expressed. In the case of multi-table correlation query (especially big data table), its performance will be reduced, but also improve the programming difficulty of the client program, therefore, the physical design need