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70 most recommended articles in Big Data space

This is a collection of 70 most recommended articles in Big Data landscape. The list is based on recommendations by leading websites, bloggers, big data community leaders/members and users of various social media channels. The articles are arranged in no particular order.
1. Why becoming a Data Scientist is NOT actually easier than you think
2. Big Data Beyond MapReduce: Google’s Big Data Papers
3. Difference between Data Scientist and Data Analyst
4. Machine Learning in 10 pictures
5. Big Data: A Practical Definition
6. Parallel Programming in the Age of Big Data
7. 9 Open Source Big Data Technologies to Watch
8. The Pathologies of Big Data
9. 10 surprising Machine Learning applications
10. Tapping into the power of Big Data
11. Hadoop Basics – Creating a MapReduce Program
12. 8 Real-World Big Data Deployments
13. The $30/hr Data Scientist
14. Reinventing Society In The Wake Of Big Data
15. 10 Trends Driving Big Data in Financial Services
16. Get Ready For Sensor-Driven Business Models
17. Can big data be racist?
18. Fill in the Blanks: Using Math to Turn Lo-Res Datasets Into Hi-Res Samples
19. Data scientists need their own GitHub. Here are four of the best options
20. How to Be Ready for Big Data
21. 7 Big Data Solutions Try To Reshape Healthcare
22. Getting started in Data Science: My thoughts [Trey Causey]
23. Big Data Causes Concern and Big Confusion
24. 5 Big Wishes For Big Data Deployments
25. When Astronomy Met Computer Science
26. “How do I become a Data Scientist?”
27. Equifax Eyes Are Watching YouBig Data Means Big Brother
28. How Big Data Brings BI, Predictive Analytics Together
29. Elusive Data Scientists Driving High Salaries
30. Big Data’s Surprising Uses: From Lady Gaga To CIA
31. How I made $500k with Machine Learning and High Frequency Trading
32. Microsoft’s Big Data Strategy: An Insider’s View
33. Deep Learning – How & Why Deep Learning Methods Work
34. A non-comprehensive list of awesome things other people did this year
35. Big Data Career Switch: 4 Key Points
36. Big Data Analytics Today Lets Businesses Play Moneyball
37. Why The R Programming Language Is Good For Business
38. What I learnt from 2 years of ‘Data Sciencing’
39. IBM And Big Data Disruption: Insider’s View
40. The Big Data Challenge: How to Develop a Winning Strategy
41. Data, data everywhere
42. You might be a Data Scientist if
43. Hadoop Creator Outlines the Future of Big Data Platform
44. NoSQL Vs. Hadoop: Big Data Spotlight At E2
45. Big Data Is Less About Size, And More About Freedom
46. Is Julia the Future for Big Data Analytics?
47. What is the Difference Between Artificial Intelligence, Machine Learning, Statistics, and Data Mining
48. Clouds, big data, and smart assets: Ten tech-enabled business trends to watch
49. How to Use Big Data to Stop Customer Churn
50. Five Things CIOs Should Know About Big Data
51. META: What Data Scientists are reading. And why.
52. This Data Scientist spent a year deep inside The New York Times. Here’s what he discovered
53. Big (Bad) Data
54. New to Data Science
55. The Big Data Landscape
56. Big Data Debate: Will Hadoop Become Dominant Platform?
57. How Python became the language of choice for Data Science
58. 10 Mistakes Enterprises Make in Big Data Projects
59. The real promise of big data: It’s changing the whole way humans will solve problems
60. Scientists set new speed record for big data
61. BlueKai Acquisition Validates that Customer Data is King
62. Why “Big Data” Is a Big Deal
63. How to find the bars that women love
64. Big Data: Are you ready for blast-off?
65. What Big-Data VCs are Sick of — and What They Really Want
66. 4 Ways to Actually Use Big Data
67. Big Data, Big Business, Big Brother?
68. Big Data’s Fading Bloom
69. Data Isn’t Just for the Big Guys Anymore
70. PayPal chief scientist on cracking the code for big data analytics

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