Your Cart Is Empty
Home > Professional Development Skills > Business Analysis > Data Analysis Boot Camp
This three-day course leverages straightforward business examples to explain practical techniques for understanding and reviewing data quality.
This course, organized into key topic areas, leverages straightforward business examples to explain practical techniques for understanding and reviewing data quality and how to translate data into analysis of business problems to begin making informed, intelligent decisions. Get an overview of data quality and data management, followed by foundational analysis and statistical techniques. Throughout the course, you will learn to communicate about data and findings to stakeholders who need to quickly make the decisions that drive your organization forward. This data analysis training class is a lively blend of expert instruction combined with hands-on exercises so you can practice new skills. Leave prepared to start performing practical analysis techniques the moment you return to work.
Category
ID
Duration
Level
Price
Business Analysis
13412
3 Day(s)
Foundation
$1,595.00
Objectives
Lesson objectives help students become comfortable with the course, and also provide a means to evaluate learning. Upon successful completion of this course, students will be able to:· Identify opportunities, manage change and develop deep visibility into your organization· Understand the terminology and jargon of analytics, business intelligence and statistics· Learn a wealth of practical applications for applying data analysis capability· Visualize both data and the results of your analysis for straightforward graphical presentation to stakeholders· Learn to estimate more accurately than ever, while accounting for variance, error, and Confidence Intervals· Practice creating a valuable array of plots and charts to reveal hidden trends and patterns in your data· Differentiate between "signal" and "noise" in your data· Understand and leverage different distribution models, and how each applies in the real world· Form and test hypotheses – use multiple methods to define and interpret useful predictions· Learn about statistical inference and drawing conclusions about the population
Part 1: The Value and Challenges of Data-Driven Disruption1. Objectives and expectations2. Hurdles to becoming a data-driven organization3. Data empowerment4. Instilling data practices in the organization5. The CRISP-DM model of data projectsPart 2: Tying Data to Business Value1. What constitutes data-driven value2. Requirements gathering: How to approach it3. Kanban for data analysis4. Know your customers5. Stakeholder cheat sheets· EXERCISE: Data-driven project checklist· LAB: Data analysis techniques: AggregationsPart 3: Understanding Your Data1. Data defined2. Data versus information3. Types of data1. Unstructured vs. Structured2. Time scope of data3. Sources of data4. Data in the real world5. The 3 V’s of data6. Data Quality1. Cleansing2. Duplicates3. SSOT4. Field standardization5. Identify sparsely populated fields6. How to fix common issues· LAB: Prioritizing data qualityPart 4: Analyzing Data1. Analysis foundations1. Comparing programs and tools2. Words in English vs. data3. Concepts specific to data analysis4. Domains of data analysis5. Descriptive statistics6. Inferential statistics7. Analytical mindset8. Describing and solving problems2. Averages in data1. Mean2. Median3. Mode4. Range3. Central tendency1. Variance2. Standard deviation3. Sigma values4. Percentiles4. Demystifying statistical models5. Data analysis techniques· LAB: Central tendency· LAB: Variability· LAB: Distributions· LAB: Sampling· LAB: Feature engineering· LAB: Univariate linear regression· LAB: Prediction· LAB: Multivariate linear regression· LAB: Monte Carlo simulationPart 5: Thinking Critically About Your Analysis1. Descriptive analysis2. Diagnostic analysis3. Predictive analysis4. Prescriptive analysis
Part 6: Data Analysis in the Real World1. Deployment of analyses2. Best practices for BI3. Technology ecosystems1. Relational databases2. NoSQL databases3. Big data tools4. Statistical tools5. Machine learning6. Visualization and reporting tools4. Making data useablePart 7: Data Visualization & Reporting1. Best practices for data visualizations1. Visualization essentials2. Users and stakeholders3. Stakeholder cheat sheet2. Common presentation mistakes3. Goals of visualization1. Communication and narrative2. Decision enablement3. Critical characteristics4. Communicating data-driven knowledge1. Formats and presentation tools2. Design considerationsPart 8: Hands-On Introduction to R and R Studio1. What is R?· LAB: Intro to R Studio· LAB: Univariate linear regression in R· LAB: Multivariate linear regression in R
Questions?
Associate Certified Analytics Professional (aCAP)Certified Analytics ProfessionalMicrosoft Certified Data Analyst Associate
If you have basic familiarity with Excel, this three-day course can teach you practical applied analysis techniques to leverage data for relatively common decision-making methods.
Productivity Point Learning Solutions evolved out of a desire to increase our outreach both nationally and internationally.
Productivity Point Headquarters 1580 Sawgrass Corporate Parkway Suite 205 Sunrise, Florida 33323 United States
Contact T 1-844-238-8607 P 1-954-425-6141 F 1-954-928-9057 E info@productivitypointls.com