Big Data Analytics requires involvement of Data Scientists
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Big Data Overview | State of the practice in analytics | The role of the Data Scientist | Big Data Analytics in Industry Verticals |
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Introduction to Big Data Analytics |
Key roles for a successful analytic project | Main phases of the lifecycle | Developing core deliverables for stakeholders | ||
End-to-end data analytics lifecycle |
Introduction to R | Analyzing and exploring data with R | Statistics for model building and evaluation | |||
Using R to execute basic analytic methods |
Naïve Bayesian Classifier | K-Means Clustering | Association Rules | Decision Trees | Linear and Logistic Regression | Time Series Analysis | Text Analytics | ||||||
Advanced analytics and statistical modeling for Big Data – Theory and Methods |
Using MapReduce/Hadoop for analyzing unstructured data | Hadoop ecosystem of tools | In-database Analytics | MADlib and Advanced SQL Techniques | ||||
Advanced analytics and statistical modeling for Big Data – Technology and Tools |
How to operationalize an analytics project | Creating the Final Deliverables | Data Visualization Techniques | Hands-on Application of Analytics Lifecycle to a Big Data Analytics Problem |
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Communication of Results and Big Data Analytics Life Cycle Lab |
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