Data Analysis Bootcamp
Gain the skills and knowledge of techniques and technologies to deal professionally with experts in all advanced data management fields.
About this event
Data is one of the biggest assets of any organization. It helps firms understand and enhance their processes, thereby saving time and money. The efficient use of data helps businesses to reduce wastage by analyzing different marketing channels’ performance and focusing on those offering the highest ROI. However, data is useless unless it is converted into valuable information. Data Science plays a big part in this conversion as it involves mining large datasets containing structured and unstructured data and identifying hidden patterns to extract actionable insights.
This training course is designed to provide participants with the relevant best practices, and the essential concepts of the Big Data ecosystem, as well as the opportunities for Artificial Intelligence. Additionally, this course will discuss the disciplines to which modern data relates. This will allow participants to gain the skills and knowledge in order to become specialists in techniques and technologies and deal professionally with experts in all advanced data management fields.
By the end of the course, participants will be able to:
- Gain a comprehensive understanding of the concept of data science and designing data for efficient analysis.
- Identify the difference between predictive models and pattern finding ones
- Compare solutions related to Data Analysis vs. Machine Learning
- Learn about the concept of “proprietary” and “open source” technologies
- Create a modern data flow outline from sources to reports.
This training course is designed to be highly interactive and participatory. To ensure maximum comprehension and retention, this training will utilize a variety of proven virtual learning methods such as break-out sessions for group discussions and brainstorming, virtual icebreakers, recorded videos, case studies, and readings.
Who Should Attend:
Experience level: Intermediate, 2 to 3 years as a Business Analyst, Data Analyst, IT Professional or an undergrad with technical background.
- 25 IIBA CDUs
- 25 PMI PDUs
Participants will be expected to attend a total of 5 sessions on:
- Monday, June 6
- Tuesday, June 7
- Wednesday, June 8
- Thursday, June 9
- Friday, June 10
The time is from12:00 pm - 5:00 pm (MST / Phoenix, AZ) on all days.
Introduction: Data Analysis and Visualization
- Types of data and data visualization
- Evaluating the representative quality of data
- Using descriptive statistics to summarize data
- Simple Linear Regression
- Simple Logistic Regression
- Managing and removing outliers
- Multiple linear regressions
- Multiple logistic regressions
- Discriminant analysis: Functions and probabilistic models
- Decision trees: CART – CHAID and Random Forests
- Support vector machines
- K-nearest neighbors
- Naïve Bayes
- Neural networks, deep learning and AI possibilities
- Principle Component Analysis
- Clustering: Hierarchical and K Means
- Simple correspondence analysis
- Multi-dimensional scaling
- Quadrant analysis
Business Intelligence Forecasting
- Business Intelligence
- Databases: collection and sources
- Storage: Data warehouses, data marts and data lakes
- Analytics: BI Tools, OLAP, Dashboards, etc.
- Exponential smoothing: Additive and multiplicative methods
- Time Series: Additive and multiplicative methods
- ARIMA models
- R vs. Python
- Statistical Tests
- Machine Learning algorithms
Big Data and Big Data Management
- IoT essentials - M2M and Embedded Systems
- Basic IoT protocols
- Big Data: “where” and “when”
- Big Data distributed files with HDFS
- MapReduce vs. Spark Data Sharing
- Big Data Ecosystem bird's eye view: Spark, Mongo DB, Cassandra, Flume, Cloudera, Oozie, Mahout
- IIBA Members - $650.00
- Non-IIBA Members - $750.00
Interested in registering 4 or more attendees? Contact the organizer at firstname.lastname@example.org to arrange.