Enterprise Database Systems
Data Mining and Decision Making
Data Mining and Decision Making: Data Mining for Answering Business Questions
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Data Mining and Decision Making: Data Preparation & Predictive Analytics
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Data Mining and Decision Making: Modern Data Science Lifecycle
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Data Mining and Decision Making: Predictive Analytics for Business Strategies
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Final Exam: Raw Data To Insights
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Data Mining and Decision Making: Data Mining for Answering Business Questions
Lesson Objectives
Data Mining and Decision Making: Data Mining for Answering Business Questions
- discover the key concepts covered in this course
- define and compare data science, data analytics and machine learning and recognize their use cases for business management
- compare the roles of machine learning engineers and data scientists
- list and define major types of machine learning used in business management
- describe the workings of a machine learning algorithm
- outline the association rules used in data mining and specify their roles
- describe how to perform anomaly detection during data mining
- describe how to perform customer segmentation during data mining
- describe how to perform data analysis for business by showing examples of Walmart and Market Basket
- describe how to use data mining for clinical decision support through a case study
- specify the importance of utilizing Predictive Analytics for Business
- summarize the key concepts covered in this course
Overview/Description
The data mining process provides the opportunity for businesses to collect additional information and insights that are unavailable through other everyday operations of the company. Use this course to learn more about how utilizing data mining effectively may provide a competitive advantage and additional knowledge about the market and competitors. Start by examining the essential concepts in data exploration using summary statistics and visuals and discover different data mining techniques. This course will also help you develop an understanding of the complete data mining process - data gathering, cleaning, exploration, and mining. After completing this course, you'll be able to use data mining to answer in-depth questions about any business.
The data mining process provides the opportunity for businesses to collect additional information and insights that are unavailable through other everyday operations of the company. Use this course to learn more about how utilizing data mining effectively may provide a competitive advantage and additional knowledge about the market and competitors. Start by examining the essential concepts in data exploration using summary statistics and visuals and discover different data mining techniques. This course will also help you develop an understanding of the complete data mining process - data gathering, cleaning, exploration, and mining. After completing this course, you'll be able to use data mining to answer in-depth questions about any business.
Target
Prerequisites: none
Data Mining and Decision Making: Data Preparation & Predictive Analytics
Lesson Objectives
Data Mining and Decision Making: Data Preparation & Predictive Analytics
- discover the key concepts covered in this course
- recognize the primary industrial and commercial data sources around us and use this knowledge to select a suitable data source for your business processes
- define key characteristics and requirements for a reliable data collection pipeline
- describe the purpose of the data validation process and name the major steps involved in it
- outline several ways to clean a dataset and describe why data cleaning is necessary
- specify how summary statistics can be used to explore and prepare a dataset and define what's meant by measures of frequency and central tendency
- specify how summary statistics can be used to explore and prepare a dataset and describe measures of dispersion and statistics
- identify how data visualization done correctly can become a key business driver
- name advanced visualization techniques and describe their use cases
- outline how feature generation can be used to facilitate business decision-making
- outline how feature reduction can be useful when producing business analytics
- summarize the key concepts covered in this course
Overview/Description
Data preparation transforms raw data into datasets with stable structures suitable for predictive analytics. This course shows you how to produce clean datasets with valid data to ensure accurate insights for sound business decision-making. Examine the role data sources, systems, and storage play in descriptive analytics. Explore best practices used for data preparation, including data collection, validation, and cleaning. Additionally, investigate some more advanced data exploration and visualization techniques, including the use of different chart types, summary statistics, and feature engineering. Upon completing this course, you'll know how to gather, store, and analyze data to make reliable predictions and smart business decisions.
Data preparation transforms raw data into datasets with stable structures suitable for predictive analytics. This course shows you how to produce clean datasets with valid data to ensure accurate insights for sound business decision-making. Examine the role data sources, systems, and storage play in descriptive analytics. Explore best practices used for data preparation, including data collection, validation, and cleaning. Additionally, investigate some more advanced data exploration and visualization techniques, including the use of different chart types, summary statistics, and feature engineering. Upon completing this course, you'll know how to gather, store, and analyze data to make reliable predictions and smart business decisions.
Target
Prerequisites: none
Data Mining and Decision Making: Modern Data Science Lifecycle
Lesson Objectives
Data Mining and Decision Making: Modern Data Science Lifecycle
- discover the key concepts covered in this course
- compare the roles of data science and data analysis in a business context
- name the steps and processes essential to any data science project
- specify how to establish the business perspective of a data science project and how business goals relate to data analysis and predictive modeling
- list the processes essential to preparing data and specify the goal of each process
- describe how descriptive analysis can be used to drive business decision-making
- describe how predictive analytics can be used to drive business decision-making
- name multiple ways in which predictive modeling should be interpreted through a business context
- define the role of model validation when using machine learning for predictive modelling
- specify the requirements for model implementation when using machine learning for predictive modeling
- outline how data-driven decision-making is used in a business context using the example of a case study
- summarize the key concepts covered in this course
Overview/Description
Data mining and data science are rapidly transforming decision-making business practices. For these activities to be worthwhile, raw data needs to be transformed into insights relevant to your business's goals. In this course, you'll walk through each stage of the data mining pipeline covering all requirements for reaching a conclusive and relevant business decision. You'll examine data preparation, descriptive and predictive analytics, and predictive modeling. You'll also investigate the role of model validation and implementation in machine learning. On completion, you'll have a solid grasp of how data-driven decision-making has helped other businesses succeed and how it can help yours, too, if you employ the right methods.
Data mining and data science are rapidly transforming decision-making business practices. For these activities to be worthwhile, raw data needs to be transformed into insights relevant to your business's goals. In this course, you'll walk through each stage of the data mining pipeline covering all requirements for reaching a conclusive and relevant business decision. You'll examine data preparation, descriptive and predictive analytics, and predictive modeling. You'll also investigate the role of model validation and implementation in machine learning. On completion, you'll have a solid grasp of how data-driven decision-making has helped other businesses succeed and how it can help yours, too, if you employ the right methods.
Target
Prerequisites: none
Data Mining and Decision Making: Predictive Analytics for Business Strategies
Lesson Objectives
Data Mining and Decision Making: Predictive Analytics for Business Strategies
- discover the key concepts covered in this course
- specify the role of deep learning and artificial neural networks when dealing with data
- describe how a neural network works
- recognize unique features of regression problems and how these can be applied to predictive analytics
- describe unique features of classification problems and how these can be applied to predictive analytics
- describe how time series analysis is used for predictive analytics
- specify the role of actionable recommender systems in predictive analytics
- outline the key advantages of using recurrent and convolutional neural network pipelines
- identify key advantages of using NLP techniques in predictive analytics pipelines
- recognize the key advantages of using computer vision in predictive analytics pipelines
- describe the future prospects of predictive analytics alongside its most promising fields of study
- summarize the key concepts covered in this course
Overview/Description
Predictive analytics enable modern businesses to obtain important insights about the market and gaining a competitive advantage. Use this course to explore how predictive analytics could provide insights about the future, empowering businesses to make informed decisions. Start by delving deeper into the concepts of deep learning, machine learning, and data. You'll then examine neural networks theory of operation and discover natural language processing (NLP) and computer vision applications. You'll wrap up the course by developing a deeper understanding of the future of analytics. Once you've completed this course, you'll be able to identify how regression, classification, time-series analysis, and recommender engines can be used to drive business decisions. You'll also have a solid grasp of using machine learning and deep learning methodologies for predictive analytics.
Predictive analytics enable modern businesses to obtain important insights about the market and gaining a competitive advantage. Use this course to explore how predictive analytics could provide insights about the future, empowering businesses to make informed decisions. Start by delving deeper into the concepts of deep learning, machine learning, and data. You'll then examine neural networks theory of operation and discover natural language processing (NLP) and computer vision applications. You'll wrap up the course by developing a deeper understanding of the future of analytics. Once you've completed this course, you'll be able to identify how regression, classification, time-series analysis, and recommender engines can be used to drive business decisions. You'll also have a solid grasp of using machine learning and deep learning methodologies for predictive analytics.
Target
Prerequisites: none
Final Exam: Raw Data To Insights
Lesson Objectives
Final Exam: Raw Data To Insights
- compare the roles of Machine Learning Engineers and Data Scientists
- define and compare Data Science, Data Analytics and Machine Learning and their use cases for Business Management
- define key characteristics and requirements for the Data Collection pipeline
- describe broadly how a Machine Learning algorithm works
- describe broadly how a neural network works
- describe how Descriptive Analysis can be used to drive business decision making
- describe how good visualization of data can become a key business driver
- describe the prospects of Predictive Analytics alongside its most promising fields of study
- describe the purpose of the Data Validation process and name major steps in Data Validation
- describe the role of Data Science and Data Analysis in Modern Business
- identify key advantages of using Computer Vision in Predictive Analytics pipelines
- identify key advantages of using NLP techniques in Predictive Analytics pipelines
- list the processes essential to preparing data and specify their role
- name advanced visualization techniques and describe their use cases
- name and define major types of Machine Learning used in Business Management
- name the association rules used in Data Mining and specify their roles
- name the steps and processes essential to any Data Science project
- specify several ways to clean the dataset and describe why data cleaning is necessary
- specify the business perspective in relation to data analysis and predictive modeling
- specify the role of Deep Learning and Artificial Neural Networks when dealing with data
Overview/Description
Final Exam: Raw Data To Insights will test your knowledge and application of the topics presented throughout the Raw Data To Insights track of the Skillsoft Aspire Data for Leaders and Decision Makers Journey.
Target
Prerequisites: none
