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Welcome to Big Data Challenges and Analysis-Driven Data. I’m Mark Craver, and I’m a principal technical training consultant in the Education Division at SAS. This is my 29th year with SAS, and most of that time has been spent in the Data Management space.

I started my career in the Quality Assurance Department, before spending several years on the road as a consultant before settling into my teaching career in 2001. Since that time, I have focused almost exclusively on SAS’ robust Data Management suite of products.

I love the technology, and I love the direction SAS is taking in the world of big data management, especially big data, Hadoop, and big data analytics! I also love to interact with customers to hear of the creative and wonderful things they are doing with our technology. That might, quite possibly, be the most rewarding part of my job!

This course is designed to expose you to the concepts of the big data explosion, the types of analysis organizations are trying to do with big data, the emergence of the field of data science, why SAS cares about the field of data science, and how big data and Hadoop fit into the equation. Fasten your seatbelts because here we go!

Course Curriculum

1.1 From Scarcity to Abundance
1.1 From Scarcity to Abundance Details 00:00:00
1.2 Disk Size Preferences
1.2 Disk Size Preferences Details 00:00:00
1.3 Google Trends
1.3 Google Trends Details 00:00:00
1.4 What is Data Science
1.4 What is Data Science Details 00:00:00
1.5 Disciplines of Data Science
1.5 Disciplines of Data Science Details 00:00:00
1.6 The Analytical Landscape
1.6 The Analytical Landscape Details 00:00:00
1.7 Stakeholders and Methods
1.7 Stakeholders and Methods Details 00:00:00
1.8 Data Science Life Cycle
1.8 Data Science Life Cycle Details 00:00:00
1.9 Projection Iteration
1.9 Projection Iteration Details 00:00:00
2.1 What is a Data Scientist
2.1 What is a Data Scientist Details 00:00:00
2.2 Parts of a Data Scientist
2.2 Parts of a Data Scientist Details 00:00:00
2.3 Data Scientist Roles
2.3 Data Scientist Roles Details 00:00:00
2.4 Differences in Job Roles
2.4 Differences in Job Roles Details 00:00:00
2.5 Job Responsibilities
2.5 Job Responsibilities Details 00:00:00
2.6 Role in the Data Life Cycle
2.6 Role in the Data Life Cycle Details 00:00:00
2.7 Data Scientist Tools
2.7 Data Scientist Tools Details 00:00:00
2.8 Communication is Key
2.8 Communication is Key Details 00:00:00
2.9 Communicating Results
2.9 Communicating Results Details 00:00:00
2.10 Stages of Communication
2.10 Stages of Communication Details 00:00:00
2.11 Communication Workfolw
2.11 Communication Workfolw Details 00:00:00
2.12 Team Collaboration
2.12 Team Collaboration Details 00:00:00
3.1 Compute Enviroment
3.1 Compute Enviroment Details 00:00:00
3.2 Types of Data Analyses
3.2 Types of Data Analyses Details 00:00:00
3.3 Data Requirements
3.3 Data Requirements Details 00:00:00
4.1 Data Deluge
4.1 Data Deluge Details 00:00:00
4.2 Realities of the Data Deluge
4.2 Realities of the Data Deluge Details 00:00:00
4.3 The Increasing Data Deluge
4.3 The Increasing Data Deluge Details 00:00:00
4.4 Analysts' Thoughts
4.4 Analysts’ Thoughts Details 00:00:00
4.5 What is Big Data
4.5 What is Big Data Details 00:00:00
4.6 Another View of Big Data
4.6 Another View of Big Data Details 00:00:00
4.7 The Internet of Things
4.7 The Internet of Things Details 00:00:00
4.8 Cost of Large Scale Storage
4.8 Cost of Large Scale Storage Details 00:00:00
4.9 Values of large Scale Analysis
4.9 Values of large Scale Analysis Details 00:00:00
4.10 Big Data Factors
4.10 Big Data Factors Details 00:00:00
4.11 Big Data Explosion
4.11 Big Data Explosion Details 00:00:00
5.1 Factors Driving Demand
5.1 Factors Driving Demand Details 00:00:00
5.2 Challenges Posed by Big Data
5.2 Challenges Posed by Big Data Details 00:00:00
5.3 Data Scientist's Challenge
5.3 Data Scientist’s Challenge Details 00:00:00
5.4 Connecting the Data Sources
5.4 Connecting the Data Sources Details 00:00:00
5.5 Data management Strategy
5.5 Data management Strategy Details 00:00:00
5.6 Business Values Examples
5.6 Business Values Examples Details 00:00:00
5.7 The Evolution of Big Data
5.7 The Evolution of Big Data Details 00:00:00
5.8 Why Does Hadoop Matter
5.8 Why Does Hadoop Matter Details 00:00:00
5.9 Why Apache Hadoop
5.9 Why Apache Hadoop Details 00:00:00
5.10 Primary Uses fro Hadoop
5.10 Primary Uses fro Hadoop Details 00:00:00
5.11 SAS-Hadoop Portfolio
5.11 SAS-Hadoop Portfolio Details 00:00:00
5.12 What is Hadoop
5.12 What is Hadoop Details 00:00:00
5.13 Distrubuted Data
5.13 Distrubuted Data Details 00:00:00
5.14 Distributed Processes
5.14 Distributed Processes Details 00:00:00
5.15 Hadoop Distributions
5.15 Hadoop Distributions Details 00:00:00
5.16 Hadoop Ecosystem
5.16 Hadoop Ecosystem Details 00:00:00
5.17 SAS and Big Data
5.17 SAS and Big Data Details 00:00:00
5.18 Why SAS
5.18 Why SAS Details 00:00:00
5.19 Why SAS and Hadoop
5.19 Why SAS and Hadoop Details 00:00:00
5.20 Why the marriage
5.20 Why the marriage Details 00:00:00
5.21 Hadoop Business Reasons
5.21 Hadoop Business Reasons Details 00:00:00
5.22 Conclusion
5.22 Conclusion Details 00:00:00
Additional Resources
Additional Resources Details 00:00:00

Course Reviews

4

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  1. Very nice introduction

    4

    Very nice introduction to basic concepts of data science especially for a candidate from non-programming background

  2. Good intro to why we need Data Science & what are the best tools available

    4

    Good intro to why we need Data Science & what are the best tools available

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