Data architecture is composed of models, policies, rules or standards that govern which data is collected, and how it is stored, arranged, integrated, and put to use in data systems and in organizations. We usually export our data to cloud for purposes like safety, multiple access and real time simultaneous analysis. By the end of this session, student will be able to:
1. Design Data Architecture
2. Understand various Data Sources
3. Export Data to Amazon S3
Introduction to Big Data tools like Hadoop, Spark, Impala etc., Data ETL process, Identify gaps in the
There are thousands of Big Data tools out there. All of them promising to save you time, money and help you uncover never-before-seen business insights. By the end of this session, student will be able to:
1. Know the basics of Big Data Tools
2. Understand gaps in data.
data and follow-up for decision making.
Big data analytics is the process of examining large datasets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. By the end of this session, student will be able to:
1. Execute Descriptive analytics on Big Data tools.
2. Detect outlier and eliminate them.
3. Prepare data for analysis.
Machine learning is the subfield of computer science that "gives computers the ability to learn without being explicitly programmed".Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis. By the end of this session, you will be able to:
1. Do Hypothesis Testing
2. Determine multiple analytical methodologies.
3. Train model no 2/3 sample data.
4. Predict Sample.
5. Explore chosen algorithms for accuracy
Data visualization is the presentation of data in a pictorial or graphical format. It enables decision makers to see analytics presented visually, so they can grasp difficult concepts or identify new patterns. With interactive visualization, you can take the concept a step further by using technology to drill down into charts and graphs for more detail, interactively changing what data you see and how it’s processed
By the end of this session, you will be able to:
1. Prepare Data for visualization.
2. Draw insights out of visualization tools
T1:Student’s Handbook for Associate Analytics-2.
T2:Introduction to Data Mining, Tan, Steinbach and Kumar, Addison Wesley, 2006
T3:Data Mining Analysis and Concepts, M. Zaki and W. Meira (the authors have kindly made an online version available): http://www.dataminingbook.info/uploads/book.pdf
T4:Mining of Massive Datasets Jure Leskovec Stanford Univ. Anand RajaramanMilliway Labs Jeffrey D. Ullman Stanford Univ.