Sure, it s entering its second decade coursera now, but there s no denying that Hadoop had a monstro

The secret is out, and the mad rush is on to leverage big data analytics tools and techniques coursera for competitive advantage before they become commoditized. If you re in the market for a big data job in 2015, these are the nine skills that will garner coursera you a job offer.
Sure, it s entering its second decade coursera now, but there s no denying that Hadoop had a monstrous year in 2014 and is positioned for an even bigger 2015 as test clusters are moved into production and software vendors increasingly target the distributed storage and processing architecture. While the big data platform is powerful, Hadoop can be a fussy beast and requires care and feeding by proficient technicians. Those who know there way around the core components of the Hadoop stack such as HDFS, MapReduce, Flume, Oozie, Hive, Pig, HBase, and YARN will be in high demand.
Hadoop现在已经进入第二个10年发展期了, 但不可否认的是, Hadoop在2014年出现了井喷式发展, 由于Hadoop从测试集群向生产和软件供应商方向不断转移, 其越来越接近于分布式存储和处理机架构, 因此, 这一势头在2015年会更加猛烈 由于大数据平台的强大, Hadoop可能是一个挑剔的怪兽, 它需要熟悉的技术人员细心的照顾和喂养 掌握Hadoop最核心技术 (例如, HDFS, MapReduce, Flume, Oozie, Hive, Pig, HBase, and YARN) 的技术人员在职场上的需求将越来越大
If Hadoop is a known quantity in the big data world, then Spark is a black horse candidate that has the raw potential to eclipse its elephantine cousin. The rapid rise of the in-memory stack is being proffered as a faster and simpler alternative to MapReduce-style analytics, either within a Hadoop coursera framework or outside it. Best positioned as one of the components in a big data pipeline, Spark still requires technical expertise to program and run, thereby providing job opportunities for those in the know.
On the operational side of the big data house, distributed, scale-out NoSQL databases like MongoDB and Couchbase are taking over jobs previously handled by monolithic SQL databases like Oracle and IBM DB2. On the Web and with mobile apps, NoSQL databases are often the source of data crunched in Hadoop, as well as the destination coursera for application changes put in place after insight is gleaned from Hadoop. In the world of big data, Hadoop and NoSQL occupy opposite sides of a virtuous cycle.
People coursera have been mining for data as long as they ve been collecting it. But in today s big data world, data mining has reached a whole new level. One of the hottest fields in big data last year is machine learning, which is poised for a breakout year in 2015. Big data pros who can harness machine learning technology to build and train predictive analytic apps such as classification, recommendation, and personalization systems are in super high demand, and can command top dollar in the job market. coursera
5. Statistical and Quantitative coursera Analysis(统计和定量分析) This is what big data is all about. coursera If you have a background in quantitative reasoning and a degree in a field like mathematics or statistics, you re already halfway there. Add in expertise with a statistical tool like R, SAS, Matlab, SPSS, or Stata, and you ve got this category locked down. In the past, most quants went to work on Wall Street, but thanks to the big data boom, companies in all sorts of industries coursera across the country are in need of geeks with quantitative backgrounds.
The data-centric coursera language is more than 40 years old, but the old grandpa still has a lot of life yet in today s big data age. While it won t be used with all big data challenges (see: NoSQL above), the simplify of Structured Query Language makes it a no-brainer for many of them. And thanks to initiatives like Cloudera s Impala, SQL is seeing new life as the lingua coursera franca for the next-generation of Hadoop-scale data warehouses.
Big coursera data can be tough to comprehend, but in some circumstances there s no replacement for actually getting your eyeballs onto data. You can do multivariate or logistic regression analysis on your data until the cows come home, but sometimes exploring just a sample of your data in a tool like Tableau or Qlikview can tell you the shape of your data, and even reveal hidden details that change how you proceed. And if you want to be a data artist when you grow up, being well-versed in one or more visualization coursera tools is practically a requirement.
大数据可能不是那么容易理解, 但在某些情况下, 通过鲜活的数据吸引眼球仍然是不可替代的方法 你可以一直采用多元或逻辑回归分析方法解析数据, 但是, 有时候使用类似 Tableau 或 Qlikview 这样的可视化工具探索数据样本能够直观的告诉你所拥有的数据的形态, 甚至是发现那些能够改变你处理数据方法的一些隐蔽细节 coursera 当然 如果你长大后想成为数据艺术家, 那么, 精通一个甚至是更多的可视化工具就是必不可少的了
Having experience programming coursera applications in general-purpose languages like Java, C, Python, or Scala could give you the edge over other candidates whose skill sets are confined to analytics. According to Wanted Analytics, there was a 337 percent increase in the number of job postings for computer programmers that required background in data analytics. Those who are comfortable at the intersection of traditional app dev and emerging analytics will be able to write their own tickets and move freely between end-user companies and big data startups.
No matter how many advanced analytic tools and