Course description
Data Mining is the process of sorting through large data sets to identify patterns and establish relationships to solve problems through data analysis. Data mining tools allow enterprises to predict future trends.
Data Mining has great importance in today’s highly competitive business environment. Data mining is largely used in several applications such as understanding consumer research marketing, product analysis, demand and supply analysis, e-commerce, investment trend in stocks & real estates, telecommunications, and so on. Data Mining is based on mathematical algorithms and analytical skills to drive the desired results from the huge database collection.
Data Mining and Management Strategies training course will help you uncover and explore hidden patterns in the data, providing insight to predict, experiment, and continuously refine strategic decisions with big business impact. You will explore marketing business processes that increasingly rely on analytics, including customer acquisition, marketing segmentation, and understanding customer lifetime value. Use analytical tools to develop models to support these business processes.
Upcoming start dates
Upcoming start dates
Suitability - Who should attend?
Data Mining and Management Strategies training course is designed for:
- professionals who want to deepen their understanding of how big data can be mined and managed to uncover information. With its exploration into relational databases and predictive modeling techniques, the course helps professionals understand how this process works effectively with various types of data.
Outcome / Qualification etc.
At the end of this Data Mining and Management Strategies training course , you will be able to:
- The definitions of data mining and data science.
- The role of statistics in data mining.
- Machine learning concepts.
- To differentiate between supervised and unsupervised learning.
- The data mining process.
- How to conduct exploratory data analysis.
- To identify data mining models and algorithms.
- How to match the problem with the model.
- Model validation techniques.
- How to deploy data mining models.
Training Course Content
Day 1
Enterprise Database and Data Models
- Key differences between data and information.
- An understanding of enterprise database environments.
- Define specific challenges with data cleansing.
- The elements that make up a data model.
Day 2
Extracting Data from a Database
- The role of queries in extracting data from a database.
- How to implement advanced queries in Microsoft® Access (or other database environment) using a visual querying language.
- How to write queries using Structured Query Language (SQL).
- Recognize the manner in which SQL supports, extracts, transforms and loads to prepare data for analytics model development.
Day 3
Large Scale Implementation of Hadoop®MR
- An understanding of and differences between brute force and parallel approaches.
- Core concepts, advantages and supporting programs of ApacheTMHadoop®.
- Identify the components of MapReduce.
Day 4
Getting Data: Social Networks and Geolocalization
- Structure of a web page and how to obtain HTML files.
- The advantages of web crawlers and how to get data page by page.
- How to conduct text analysis: identifying human text, common issues, and resource libraries.
- The ethical implications of using publicly available data.
Day 5
Unstructured Data, Graphs and Networks
- How to apply the right data structure for a problem.
- The differences between graph, node and edge properties.
- Define what degree means and analyze and interpret the degree distribution.
- Concept of clustering coefficient and what it can mean for your data.
Day 6
Clustering: Understanding the Relationship of Things
- The Idea Behind Clustering.
- Types of Clusters.
- Distances Between Points.
- K-Means Clustering.
- Not Every Cluster Is a Good Cluster.
- How Good Are My Clusters?
- Hierarchical Clustering.
- Min, Max, and Mean.
Day 7
Classifications: Putting Things Where They Belong
- The Idea Behind Classification.
- Reading and Interpreting a Classification Tree.
- Making a Decision Tree.
Day 8
Alternative Impurity Measures
- Expansion to 2D.
- How Good Is My Classifier?
- But I Only Have Training Data.
- A Brief Look at Association Rule Mining.
Day 9
Classifications: Advanced Methods
- Rule-Based Classifier.
- Extracting Rules.
- Nearest Neighbors.
- Classifiers – Defined Boundaries.
Day 10
Artificial Neural Networks
- Limits, Boundary Conditions and Choosing the Right Classifier.
- Clustering vs. Classification.
- Outlier and Anomaly Detection.
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London Premier Centre is a UK leading training provider based in London and specialises in international short courses. Our inspiring, comprehensive portfolio of more than 400 professional development courses and seminars covers a wide range of professions from Administration, Leadership,...