MDC5153 - Revision history

Juan.gutierrez3: /* Mathematical Foundations of Data Analytics MDC4153/MDC5153 */

2023-06-30T17:17:03Z

Mathematical Foundations of Data Analytics MDC4153/MDC5153

Juan.gutierrez3: Added content in this page instead of directing.

2023-06-12T16:53:13Z

Added content in this page instead of directing.

Juan.gutierrez3: Redirected page to MAT5153

2023-04-06T22:15:33Z

Redirected page to MAT5153

Juan.gutierrez3: /* Data Analytics MDC4153/MDC5153 */

2023-04-06T21:26:59Z

Data Analytics MDC4153/MDC5153

Juan.gutierrez3: /* Data Analytics MDC4153/MDC5153 */

2023-04-03T15:35:52Z

Data Analytics MDC4153/MDC5153

Juan.gutierrez3: /* Data Analytics MDC4153/MDC5153 */

2023-03-31T02:14:51Z

Data Analytics MDC4153/MDC5153

Juan.gutierrez3: /* Data Analytics MDC4153/MDC5153 */ Corrected typo

2023-03-31T02:14:20Z

Data Analytics MDC4153/MDC5153: Corrected typo

Juan.gutierrez3: /* Data Analytics */ Added catalog entry

2023-03-31T02:13:41Z

Data Analytics: Added catalog entry

Juan.gutierrez3: Created page with "=Data Analytics= Pre-requisite: MAT2243 Applied Linear Algebra or equivalent set of courses. This immersive Data Analytics course equips students with the essential skil..."

2023-03-30T07:09:34Z

Created page with "=Data Analytics= Pre-requisite: MAT2243 Applied Linear Algebra or equivalent set of courses. This immersive Data Analytics course equips students with the essential skil..."

New page

=Data Analytics=

Pre-requisite: [[MAT2243]] Applied Linear Algebra or equivalent set of courses.

This immersive Data Analytics course equips students with the essential skills and knowledge required to analyze, visualize, and interpret complex datasets. Students will learn the importance of ethics in data analysis and how to set up a suitable environment for efficient processing. Throughout the course, participants will explore basic operations in Python, delve into advanced visualization techniques, and investigate linear and non-linear discriminants, such as artificial neural networks.

Furthermore, the course will provide an understanding of relational databases and their integration with programming environments, as well as guidance on creating effective data analysis plans. Emphasis will be placed on solution architecture, reproducibility, configuration management, and generating standardized reports. By the end of the course, students will have a strong foundation in data analytics, allowing them to transform raw data into valuable insights for decision-making.

== Course Content==
{| class="wikitable"
! Week !! Source !! Topic !! Prerequisites !! SLOs
|-
| 1 || || Description of the course project. || ||
|-
| 2 || || Ethics in data analysis. || ||
|-
| 3 || || Environment setup. || ||
|-
| 4 || || Basic operations in python. || ||
|-
| 5 || || Visualization (basic and advanced). || ||
|-
| 6 || || Linear discriminants & regressions. || ||
|-
| 7 || || Relational databases. || ||
|-
| 8 || || Relational databases from python. || ||
|-
| 9 || || Non-linear discriminants (i.e. artificial neural networks). || ||
|-
| 10 || || Data analysis plans. || ||
|-
| 11 || || Solution architecture & reproducibility. || ||
|-
| 12 || || Management of the configuration. || ||
|-
| 13 || || Standardized reports. || ||
|-
| 14 || || Project presentations
|}

@@ Line 4: / Line 4: @@
 '''Catalog entry'''
-''Prerequisite'': [[MAT2243]] Applied Linear Algebra or ([[MAT2233]] Linear Algebra and [[MAT2214]]/[[MAT2213]] Calculus III).
+''Prerequisite'': [[MAT2253]] Applied Linear Algebra or ([[MAT2233]] Linear Algebra and [[MAT2214]]/[[MAT2213]] Calculus III).
 ''Content'': Data Analytics refers to classic data analysis plus all other areas that support it. This immersive Data Analytics course equips students with the essential mathematical skills and knowledge required to analyze, visualize, and interpret complex datasets. Students will be exposed to the entire life cycle of data analysis. Throughout the course, participants will explore basic operations in scripting languages, delve into advanced visualization techniques, and investigate linear discriminants, generalized regressions, time series analysis, and non-linear discriminants, and clustering. Students will program essential algorithms, instead of using toolboxes, to explore the discrete Fourier transform, generalized regressions, clustering algorithms, and artificial neural networks.

← Older revision		Revision as of 16:53, 12 June 2023
Line 1:		Line 1:
−	~~#REDIRECT~~[[~~MAT5153~~]]	+
		+	== Mathematical Foundations of Data Analytics MDC4153/MDC5153 ==
		+
		+	'''Catalog entry'''
		+
		+	''Prerequisite'': [[MAT2243]] Applied Linear Algebra or ([[MAT2233]] Linear Algebra and [[MAT2214]]/[[MAT2213]] Calculus III).
		+
		+	''Content'': Data Analytics refers to classic data analysis plus all other areas that support it. This immersive Data Analytics course equips students with the essential mathematical skills and knowledge required to analyze, visualize, and interpret complex datasets. Students will be exposed to the entire life cycle of data analysis. Throughout the course, participants will explore basic operations in scripting languages, delve into advanced visualization techniques, and investigate linear discriminants, generalized regressions, time series analysis, and non-linear discriminants, and clustering. Students will program essential algorithms, instead of using toolboxes, to explore the discrete Fourier transform, generalized regressions, clustering algorithms, and artificial neural networks.
		+
		+	This course uses generative AI tools to aid in data analysis. Furthermore, the course will provide an understanding of relational databases and their integration with programming environments, as well as guidance on creating effective data analysis plans. Emphasis will be placed on solution architecture, reproducibility, configuration management, and generating standardized reports.
		+
		+	By the end of the course, students will have a strong foundation in data analytics, allowing them to transform raw data into valuable insights for decision-making.
		+
		+	== Course Content==
		+	{\| class="wikitable"
		+	! Week !! Source !! Topic !! Prerequisites !! SLOs
		+	\|-
		+	\| 1 \|\| \|\| Description of the course project. \|\| None. \|\| Understanding of government databases. Conduct basic data exploration. Identity questions answerable with data available for a specific problem.
		+	\|-
		+	\| 2 \|\| \|\| Scripts vs. compiled code. \|\| Previous exposure to computer programming in any language. \|\| Basic numeric operations in scripting vs. compiled code. Clarity about the differences between interpreted and compiled code, and how it impacts data analysis. Setting up environments.
		+	\|-
		+	\| 3 \|\| \|\| Ethics in data analysis. \|\| None. \|\| Identification of biases introduced during data collection, storage, analysis, and access.
		+	\|-
		+	\| 4 - 5 \|\| \|\| Linear discriminants \|\| Linear Algebra and Calculus I \|\| Ability to minimize an equation involving matrices and vectors. Mastery of Principal Component Analysis (PCA), Fisher's linear discriminant, and multiple discriminant analysis. Mastery in multi-linear operations in scripting and compiled languages. Understanding of the balance between computational performance and development time.
		+	\|-
		+	\| 6 \|\| \|\| Visualization (basic and advanced). \|\| Scripts vs. compiled code (week 2) \|\| Understanding of different families of visualization techniques. Ability to create Circos plots.
		+	\|-
		+	\| 7 \|\| \|\| Generalized regressions \|\| Linear discriminants (week 4-5) \|\| Understanding of mathematical approaches to produce an infinite family of regressions for the purpose of data smoothing.
		+	\|-
		+	\| 8 \|\| \|\| Relational databases \|\| Scripts vs. compiled code (week 2) \|\| Ability to create, access, and use relational databases from within programming environments. Understanding of when to use relational databases
		+	\|-
		+	\| 9 \|\| \|\| Clustering \|\| Generalized regressions (week 7) \|\| Ability to create basic clusters using multiple definitions of distance.
		+	\|-
		+	\| 10 \|\| \|\| Solution architecture & reproducibility. \|\| Scripts vs. compiled code (week 2) \|\| Capacity to design a complex data analysis solution that guarantees reproducibility, interoperability, and maintainability,
		+	\|-
		+	\| 11 \|\| \|\| Non-linear discriminants (i.e. artificial neural networks). \|\| Clustering (week 9) \|\| Capability to program a fully-connected feed-forward artificial neural network from scratch. Understanding of the effect of multiple activation functions,
		+	\|-
		+	\| 12 \|\| \|\| Management of the configuration. \|\| Scripts vs. compiled code (week 2) \|\| Ability to program in collaborative multi-layered environments. Capacity to resolve conflicts in code, create code branches, and propagate effectively code changes across multiple environments such as development, test production, etc.
		+	\|-
		+	\| 13 \|\| \|\| Data analysis plans & standardized reports. \|\| Scripts vs. compiled code (week 2) \|\| Dexterity to break a data analysis problems into multiple interconnected components, and then produce automated reports targeting specific audiences.
		+	\|-
		+	\| 14 \|\| \|\| Project presentations \|\| Entire course \|\| Exposure to presentation of results in front an audience of experts.
		+	\|}

← Older revision		Revision as of 22:15, 6 April 2023
Line 1:		Line 1:
−	~~==Data Analytics MDC4153/MAT5153==~~	+	#REDIRECT[[MAT5153]]
−
−	~~'''Catalog entry'''~~
−
−	~~''Prerequisite'':~~ [[~~MAT2243]] Applied Linear Algebra or ([[MAT2233]] Linear Algebra and [[MAT2214]]/[[MAT2213~~]] ~~Calculus III).~~
−
−	''Content'': This immersive Data Analytics course equips students with the essential skills and knowledge required to analyze, visualize, and interpret complex datasets. Students will learn the importance of ethics in data analysis and how to set up a suitable environment for efficient processing. Throughout the course, participants will explore basic operations in Python, delve into advanced visualization techniques, and investigate linear and non-linear discriminants, such as artificial neural networks.
−
−	Furthermore, the course will provide an understanding of relational databases and their integration with programming environments, as well as guidance on creating effective data analysis plans. Emphasis will be placed on solution architecture, reproducibility, configuration management, and generating standardized reports. By the end of the course, students will have a strong foundation in data analytics, allowing them to transform raw data into valuable insights for decision-making.
−
−	~~== Course Content==~~
−	~~{\| class="wikitable"~~
−	~~! Week !! Source !! Topic !! Prerequisites !! SLOs~~
−	\|-
−	~~\| 1 \|\| \|\| Description of the course project. \|\| \|\|~~
−	\|-
−	~~\| 2 \|\| \|\| Ethics in data analysis. \|\| \|\|~~
−	\|-
−	~~\| 3 \|\| \|\| Environment setup. \|\| \|\|~~
−	\|-
−	~~\| 4 \|\| \|\| Basic operations in python. \|\| \|\|~~
−	\|-
−	~~\| 5 \|\| \|\| Visualization (basic and advanced). \|\| \|\|~~
−	\|-
−	~~\| 6 \|\| \|\| Linear discriminants & regressions. \|\| \|\|~~
−	\|-
−	~~\| 7 \|\| \|\| Relational databases. \|\| \|\|~~
−	\|-
−	~~\| 8 \|\| \|\| Relational databases from python. \|\| \|\|~~
−	\|-
−	~~\| 9 \|\| \|\| Non-linear discriminants (i.e. artificial neural networks). \|\| \|\|~~
−	\|-
−	~~\| 10 \|\| \|\| Data analysis plans. \|\| \|\|~~
−	\|-
−	~~\| 11 \|\| \|\| Solution architecture & reproducibility. \|\| \|\|~~
−	\|-
−	~~\| 12 \|\| \|\| Management of the configuration. \|\| \|\|~~
−	\|-
−	~~\| 13 \|\| \|\| Standardized reports. \|\| \|\|~~
−	\|-
−	~~\| 14 \|\| \|\| Project presentations~~
−	\|}

← Older revision		Revision as of 21:26, 6 April 2023
Line 1:		Line 1:
−	==Data Analytics MDC4153/~~MDC5153~~==	+	==Data Analytics MDC4153/MAT5153==

	'''Catalog entry'''		'''Catalog entry'''

@@ Line 3: / Line 3: @@
 '''Catalog entry'''
-''Prerequisite'': [[MAT2243]] Applied Linear Algebra OR [[MAT2233]] Linear Algebra + [[MAT2214]]/[[MAT2213]] Calculus III.
+''Prerequisite'': [[MAT2243]] Applied Linear Algebra or ([[MAT2233]] Linear Algebra and [[MAT2214]]/[[MAT2213]] Calculus III).
 ''Content'': This immersive Data Analytics course equips students with the essential skills and knowledge required to analyze, visualize, and interpret complex datasets. Students will learn the importance of ethics in data analysis and how to set up a suitable environment for efficient processing. Throughout the course, participants will explore basic operations in Python, delve into advanced visualization techniques, and investigate linear and non-linear discriminants, such as artificial neural networks.

← Older revision		Revision as of 02:14, 31 March 2023
Line 1:		Line 1:
−	=Data Analytics MDC4153/MDC5153=	+	==Data Analytics MDC4153/MDC5153==

	'''Catalog entry'''		'''Catalog entry'''

@@ Line 1: / Line 1: @@
-=Data Analytics=
+=Data Analytics MDC4153/MDC5153=
-Pre-requisite: [[MAT2243]] Applied Linear Algebra or equivalent set of courses.
+'''Catalog entry'''
-This immersive Data Analytics course equips students with the essential skills and knowledge required to analyze, visualize, and interpret complex datasets. Students will learn the importance of ethics in data analysis and how to set up a suitable environment for efficient processing. Throughout the course, participants will explore basic operations in Python, delve into advanced visualization techniques, and investigate linear and non-linear discriminants, such as artificial neural networks.
+''Prerequisite'': [[MAT2243]] Applied Linear Algebra OR [[MAT2244]] Linear Algebra + [[MAT2214]]/[[MAT2213]] Calculus III.
 Furthermore, the course will provide an understanding of relational databases and their integration with programming environments, as well as guidance on creating effective data analysis plans. Emphasis will be placed on solution architecture, reproducibility, configuration management, and generating standardized reports. By the end of the course, students will have a strong foundation in data analytics, allowing them to transform raw data into valuable insights for decision-making.