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Data Analysis Boot Camp
Throughout this course, you will learn to communicate about data and findings to stake holders who need to quickly make the decisions that drive your organization forward.
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.
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
1. Data FundamentalsCourse Overview and Level Set· Objectives of the class· Expectations for the classUnderstanding "real-world" data· Unstructured vs. structured· Relationships· Outliers· Data growthTypes of Data· Flavors of data· Sources of data· Internal vs. external data· Time scope of data (lagging, current, leading)LAB: Getting started with our classroom data Data-related Risk· Common identified risks· Effect of process on results· Effect of usage on results· Opportunity costs, Tool investment· Mitigating common risksData Quality· Cleansing· Duplicates· SSOT· Field standardization· Identifying sparsely populated fields· How to fix some common issuesLAB: Data QualityRelationships· Finding common attributes· 1:N, N:N, 1:1LAB: Relationships in a dataset 2. Analysis FoundationsStatistical Practices: Overview· Comparing programs and tools· Words in English vs. data· Concepts specific to data analysisDomains of data analysis· Descriptive statistics· Inferential statistics· Analytical mindset· Describing and solving problems3. Analyzing DataAverages in data· Mean· Median· Mode· RangeCentral Tendency· Variance· Standard deviation· Sigma values· Percentiles· Using these concepts to estimate thingsLAB: Hands-On – Central TendencyLAB: Hands-On – Linear RegressionOverview of commonly useful distributions· Probability distribution· Cumulative distribution· Bimodal distributions· Skewness of data· Pareto distributionCorrelationLAB: DistributionsAnalytical Graphics for Data· Categorical – bar charts· Continuous – histograms· Time series – line charts· Bivariate data – scatter plots· Distribution – box plot4. Analytics & ModelingROI & Financial DecisionsCommon uses of financial data· Earned Value· Actual Cost, BAC and EAC· Expected Monetary Value· Cost Performance/Schedule Performance IndexCommon uses for random numbers· Sampling· Simulation· Monte Carlo analysis· Pseudo-random sequences
Demo / Lab – Random numbers in ExcelAn introduction to Predictive Analytics· A discussion about patterns· Regression and time series for prediction· Machine learning basics· Tools for predictive analyticsDemo / Lab – Getting started with RUnderstanding Clustering· Segmentation· Common algorithms· K-MEANS· PAMFundamentals of Data Modeling· Architecture and analysis· Stages of a data model· Data warehousing· Top-down vs. Bottom-upUnderstanding Data Warehousing· Context tables· Facts· Dimensions· Star vs. Snowflake Schema5. Visualizing & Presenting DataGoals of Visualization· Communication and Narrative· Decision enablement· Critical characteristicsVisualization Essentials· Users and stakeholders· Stakeholder cheat sheet· Common misstepsCommunicating Data-Driven Knowledge· Alerting and trending· To self-serve or not· Formats & presentation tools· Design considerations
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
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