Data Just Right LiveLessons (Video Training)

Table of contents

1. Introduction to Data Just Right LiveLessons 00:09:15
2. Learning objectives 00:00:33
3. 1.1 Why is big data such a hot concept now? 00:02:52
4. 1.2 Four strategies for tackling big data problems 00:04:13
5. 1.3 Anatomy of a data pipeline 00:03:58
6. 1.4 What the ideal database would look like 00:02:23
7. Learning objectives 00:00:35
8. 2.1 Challenges of hosting and sharing large amounts of data 00:02:48
9. 2.2 Choosing the right data format 00:07:33
10. 2.3 Best practices for physically storing and sharing large amounts of data 00:04:43
11. 2.4 Understanding data serialization formats 00:03:56
12. Learning objectives 00:00:50
13. 3.1 History and use of relational databases 00:03:59
14. 3.2 Databases and the Internet: Understanding the CAP theorem 00:05:07
15. 3.3 Non-relational databases: Document and key-value stores 00:04:20
16. 3.4 Introduction to Redis 00:06:31
17. 3.5 Sharding Redis across a cluster of machines 00:07:16
18. 3.6 Future trends in database technology 00:04:03
19. Learning objectives 00:00:35
20. 4.1 History and meaning of business intelligence 00:06:27
21. 4.2 Data warehousing and Hadoop 00:03:00
22. 4.3 Data silos can be good 00:03:59
23. 4.4 Convergence and the future of the business intelligence concept 00:02:25
24. Learning objectives 00:00:42
25. 5.1 Introduction to Apache Hive 00:02:53
26. 5.2 Loading data into Hive 00:07:07
27. 5.3 Querying data with Hive 00:06:03
28. 5.4 Introduction to AMPLab's Shark 00:02:18
29. 5.5 Data warehousing in the cloud 00:02:01
30. Learning objectives 00:00:36
31. 6.1 Introduction to analytical databases 00:02:47
32. 6.2 Google's Dremel and BigQuery 00:02:15
33. 6.3 Running a BigQuery query and retrieving the result 00:06:35
34. 6.4 Visualizing BigQuery query results 00:06:12
35. 6.5 The future of analytical query engines 00:02:27
36. Learning objectives 00:00:47
37. 7.1 History and goals of data visualization 00:02:59
38. 7.2 Strategies for dealing with visualization of very large datasets 00:02:19
39. 7.3 Building interactive visualizations with R and ggplot() 00:08:09
40. 7.4 Building 2D plots with Python and matplotlib 00:05:49
41. 7.5 Building interactive visualizations for the Web with D3.js 00:07:26
42. Learning objectives 00:00:40
43. 8.1 Writing a simple data pipeline script 00:04:53
44. 8.2 Introduction to the Hadoop MapReduce framework 00:03:36
45. 8.3 Writing a Hadoop streaming MapReduce job in Python 00:06:05
46. 8.4 Writing a multistep MapReduce job using the mrjob Python library 00:06:30
47. 8.5 Running mrjob scripts on Amazon Elastic MapReduce 00:04:26
48. Learning objectives 00:00:41
49. 9.1 Challenges of building complex data workflows 00:02:00
50. 9.2 Writing a MapReduce workflow script with Apache Pig 00:05:52
51. 9.3 Creating a MapReduce workflow application with Cascading 00:04:23
52. 9.4 When to use Pig versus Cascading 00:02:19
53. Learning objectives 00:00:40
54. 10.1 Use cases and limitations of machine learning 00:02:49
55. 10.2 Bayesian classification, clustering, and recommendation engines 00:05:17
56. 10.3 Using Apache Mahout for bayesian classification 00:07:35
57. 10.4 Introduction to MLbase 00:02:35
58. Learning objectives 00:00:53
59. 11.1 Understanding memory usage with R 00:05:49
60. 11.2 Working with large matrices using bigmemory and biganalytics 00:05:17
61. 11.3 Manipulating large data frames with ff 00:04:47
62. 11.4 Running a linear regression over large datasets using biglm 00:05:23
63. 11.5 Interfacing with Hadoop using R and RHadoop 00:03:25
64. Learning objectives 00:00:42
65. 12.1 Choosing a programming language for analytics 00:02:35
66. 12.2 Working with NumPy and SciPy 00:06:01
67. 12.3 Using the Pandas library for analysing time series data 00:09:08
68. 12.4 Using the iPython notebook 00:06:08
69. Learning objectives 00:00:48
70. 13.1 Understanding Your Data Problem 00:03:40
71. 13.2 A playbook for the build versus buy problem 00:03:14
72. 13.3 Investing in a data center: Public versus private 00:03:30
73. 13.4 Understanding the costs of open-source software 00:04:05
74. 13.5 Using analytics as a service technologies 00:03:55
75. Learning objectives 00:00:40
76. 14.1 Trends driving innovation in data analytics technology 00:03:04
77. 14.2 Hadoop: The disruptor and the disrupted 00:03:11
78. 14.3 Analytics move toward the cloud 00:03:15
79. 14.4 The evolving definition of “data scientist” 00:04:18
80. 14.5 Converging technologies 00:03:01
81. Summary of Data Just Right LiveLessons 00:00:55