Executive Overview Training Courses
Decision Makers, managers and executives must understand technical people to make decisions.
Courses in this category focus on understanding methodologies, technologies and apply sciences for the sole purpose of decision making.
Executive Overview Course Outlines
|statdm||Statistical Thinking for Decision Makers||7 hours||This course has been created for decision makers whose primary goal is not to do the calculation and the analysis, but to understand them and be able to choose what kind of statistical methods are relevant in strategic planning of the organization. For example, a prospect participant needs to make decision how many samples needs to be collected before they can make the decision whether the product is going to be launched or not. If you need longer course which covers the very basics of statistical thinking have a look at 5 day "Statistics for Managers" training. What statistics can offer to Decision Makers Descriptive Statistics Basic statistics - which of the statistics (e.g. median, average, percentiles etc...) are more relevant to different distributions Graphs - significance of getting it right (e.g. how the way the graph is created reflects the decision) Variable types - what variables are easier to deal with Ceteris paribus, things are always in motion Third variable problem - how to find the real influencer Inferential Statistics Probability value - what is the meaning of P-value Repeated experiment - how to interpret repeated experiment results Data collection - you can minimize bias, but not get rid of it Understanding confidence level Statistical Thinking Decision making with limited information how to check how much information is enough prioritizing goals based on probability and potential return (benefit/cost ratio ration, decision trees) How errors add up Butterfly effect Black swans What is Schrödinger's cat and what is Newton's Apple in business Cassandra Problem - how to measure a forecast if the course of action has changed Google Flu trends - how it went wrong How decisions make forecast outdated Forecasting - methods and practicality ARIMA Why naive forecasts are usually more responsive How far a forecast should look into the past? Why more data can mean worse forecast? Statistical Methods useful for Decision Makers Describing Bivariate Data Univariate data and bivariate data Probability why things differ each time we measure them? Normal Distributions and normally distributed errors Estimation Independent sources of information and degrees of freedom Logic of Hypothesis Testing What can be proven, and why it is always the opposite what we want (Falsification) Interpreting the results of Hypothesis Testing Testing Means Power How to determine a good (and cheap) sample size False positive and false negative and why it is always a trade-off|
|d2dbdpa||From Data to Decision with Big Data and Predictive Analytics||21 hours||Audience If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you. It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing. It is not aimed at people configuring the solution, those people will benefit from the big picture though. Delivery Mode During the course delegates will be presented with working examples of mostly open source technologies. Short lectures will be followed by presentation and simple exercises by the participants Content and Software used All software used is updated each time the course is run so we check the newest versions possible. It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning. Quick Overview Data Sources Minding Data Recommender systems Target Marketing Datatypes Structured vs unstructured Static vs streamed Attitudinal, behavioural and demographic data Data-driven vs user-driven analytics data validity Volume, velocity and variety of data Models Building models Statistical Models Machine learning Data Classification Clustering kGroups, k-means, nearest neighbours Ant colonies, birds flocking Predictive Models Decision trees Support vector machine Naive Bayes classification Neural networks Markov Model Regression Ensemble methods ROI Benefit/Cost ratio Cost of software Cost of development Potential benefits Building Models Data Preparation (MapReduce) Data cleansing Choosing methods Developing model Testing Model Model evaluation Model deployment and integration Overview of Open Source and commercial software Selection of R-project package Python libraries Hadoop and Mahout Selected Apache projects related to Big Data and Analytics Selected commercial solution Integration with existing software and data sources|
|cloudsaasiaas||Cloud, SaaS, IaaS - Practical Overview of Available Options||35 hours||This course is created for people who face choices which solution to choose for a specific problem. IT Managers, Solution Architects, Test Managers, System Administrators and Developers can benefit from this course by understanding the benefits and costs of available Cloud/SaaS/Iaas solutions. Overview of Cloud Virtalization (e.g. VirtualBox, WMware, KVM...) Hardware support for virtalization (sharing networki interfaces, etc...) Share nothing storage (S3, Ceph, Glacier) Mixed model (Bare Metal + Cloud) Public Cloud Providers Amazon Azure Google Aliyun UnitedStack Private Cloud Solutions OpenStack Amazon EC2 Ohters Software as a Service Benefits over deployable software Constomer isoaltion Legal aspects influencing solution Redunancy Availability Managing upgrades, versionsing, etc... Deployment options (BeanStalk, etc...) Redundant Databases NoSQL (e.g. MongoDB) SQL/NewSQL (e.g. Galera Cluster) Automate redundancy management with RDS Pros vs Cons Dealing with transactioons and consistency Hadoop Redundant WebServers Loadbalacing DNS load balacing (roundrobin, geo-proximity, etc..., e.g. Route53) Session handling Virtual Image Management (Appliances) Image formats Transfering images between zones Image interoperability between clouds|
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