Weekly outline
- General
- 1. 14 September - 20 September
1. 14 September - 20 September
Introduction to Big Data
* Describe the Big Data landscape including
examples of real world big data problems
including the three key sources of Big Data:
people, organizations, and sensors.
* Explain the V’s of Big Data (volume, velocity,
variety, veracity, valence, and value) and why each
impacts data collection, monitoring, storage,
analysis and reporting.
* Get value out of Big Data by using a 5-step
process to structure your analysis. - 2. 21 September - 27 September
2. 21 September - 27 September
Introduction to Big Data
* Describe the Big Data landscape including
examples of real world big data problems
including the three key sources of Big Data:
people, organizations, and sensors.
* Explain the V’s of Big Data (volume, velocity,
variety, veracity, valence, and value) and why each
impacts data collection, monitoring, storage,
analysis and reporting.
* Get value out of Big Data by using a 5-step
process to structure your analysis. - 3. 28 September - 4 October
3. 28 September - 4 October
Introduce basic about Statistica Software
Introduce about Statistica v13 interface, data
import, descriptive statistics and correlation…
How to get open data, and import to Statistica - 4. 5 October - 11 October
4. 5 October - 11 October
Introduction real case application of big data
analytics in different fields
Big Data Analytics Application in agriculture,
manufacturing, marketing, online retailing, health
care and banking - 5. 12 October - 18 October
5. 12 October - 18 October
Introduction real case application of big data
analytics in different fields
Big Data Analytics Application in agriculture,
manufacturing, marketing, online retailing, health
care and banking - 6. 19 October - 25 October
6. 19 October - 25 October
Data Cleansing & Preparation; Data
Summarization & Visualization)
Find out outlier, missing data, combing or
separate data - 7. 26 October - 1 November
7. 26 October - 1 November
Association Rules (Baskets Analysis)
Detecting relationships or associations between specific values of categorical values in large data
sets. This is a common task in many data mining
projects applied to databases containing records of
customer transactions (e.g. Items purchased by
each customer). Allow analysts and researchers to
uncover hidden pattern in large data sets. - 8. 2 November - 8 November
8. 2 November - 8 November
Association Rules (Baskets Analysis)
Detecting relationships or associations between
specific values of categorical values in large data
sets. This is a common task in many data mining
projects applied to databases containing records of
customer transactions (e.g. Items purchased by
each customer). Allow analysts and researchers to
uncover hidden pattern in large data sets. - 9. 9 November - 15 November
- 10. 16 November - 22 November
10. 16 November - 22 November
Classification and Decision Tree
classification systems based on multiple covariates
or for developing prediction algorithms for a
target variable. This method classifies a
population into branch-like segments that
construct an inverted tree with a root node,
internal nodes, and leaf nodes. It commonly used
in operations research, specifically in decision
analysis, to help identify a strategy most likely to
reach a goal, but are also a popular tool in
machine learning - 11. 23 November - 29 November
11. 23 November - 29 November
Classification and Decision Tree
classification systems based on multiple covariates
or for developing prediction algorithms for a
target variable. This method classifies a
population into branch-like segments that
construct an inverted tree with a root node,
internal nodes, and leaf nodes. It commonly used
in operations research, specifically in decision
analysis, to help identify a strategy most likely to
reach a goal, but are also a popular tool in
machine learning. - 12. 30 November - 6 December
12. 30 November - 6 December
Cluster Analysis
Handling large data sets and enabling clustering of
continuous and/or categorical variables, and
providing the functionality for complete
unsupervised learning (clustering) for pattern
recognition, with all deployment options for
predictive clustering. - 13. 7 December - 13 December
13. 7 December - 13 December
Cluster Analysis Handling large data sets and enabling clustering of
continuous and/or categorical variables, and
providing the functionality for complete
unsupervised learning (clustering) for pattern
recognition, with all deployment options for
predictive clustering. - 14. 14 December - 20 December
14. 14 December - 20 December
Logistics Regression
To predict outcome of a categorical dependent
variable on the basic of predictor variables.
Logistic regression is used in various fields,
including machine learning, most medical fields,
and social sciences - 15. 21 December - 27 December
15. 21 December - 27 December
Logistics Regression
To predict outcome of a categorical dependent
variable on the basic of predictor variables.
Logistic regression is used in various fields,
including machine learning, most medical fields,
and social sciences. - 16. 28 December - 3 January
16. 28 December - 3 January
Discriminant Analysis
to develop discriminant functions that are nothing
but the linear combination of independent
variables that will discriminate between the
categories of the dependent variable in a perfect
manner. - 17. 4 January - 10 January
17. 4 January - 10 January
Discriminant Analysis
to develop discriminant functions that are nothing
but the linear combination of independent
variables that will discriminate between the
categories of the dependent variable in a perfect
manner. - 18. 11 January - 17 January
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