"DATA ARE BECOMING THE NEW RAW MATERIAL OF BUSINESS”
Craig Mundie
COURSES AND CERTIFICATES
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Topics Covered:
- Data Collection and Processing
- Setting Up Data Collection and Configuration
- Advanced Analysis Tools and Techniques
- Advanced Marketing Tools
Skills Measured:
- Create and save data connections
- Modify data connections
- Manage data properties
- Create basic charts
- Organize data and apply filters
- Apply analytics to a worksheet
- Format view for presentation
- Create and modify a dashboard
- Dimensions and measures
- Discrete and continuous fields
- Aggregation
Topics Covered:
- AI processing and knowledge creation on Azure
- The use of AI systems in cloud computing
- The basics of the Microsoft Azure IaaS public cloud
- AI use cases in finance, law enforcement, and education
- Building, training, and deploying AML models
- How Azure Search (AS) works
- Designing your AI system
- Training your knowledge base
- The cost of a Microsoft AI system
- Operating an AI system on Azure
Topics Covered:
- What is business analytics?
- How business analytics compares to business intelligence and data science
- Business analytics process
- Predictive analytics tools and techniques
- Best practices for prescriptive analytics
- Scope and process of experimental analytics
Topics Covered:
- Business analytics and its stages
- Descriptive analytics
- Exploratory analytics
- Explanatory analytics
- Best practices and use cases
Topics Covered:
- Agile principles and the agile mindset
- Defining valuable deliverable
- Engaging your stakeholders
- Collaborating across stakeholders
- Planning agile projects
- Troubleshooting projects
- Continuous improvement
Topics Covered:
- Connect to your data and edit a data source.
- Edit metadata and group fields.
- Sort and filter data.
- Use the Desktop workspace to create visualizations.
- Build essential chart types.
- Create basic calculations, including arithmetic calculations, custom aggregations and ratios; and use quick table calculations.
- Build interactive dashboards and stories to reveal data insights.
Topics Covered:
- Matplotlib
- Dictionaries & Pandas
- Logic, Control Flow and Filtering
- Loops
- Case Study: Hacker Statistics
Topics Covered:
- Power BI Desktop vs. Pro vs. Tableau
- Importing data from files or from online
- Using Query Editor
- Joining tables and creating tables
- Formulating via DAX logic
- Using X-factor functions
- Using quick measures and dynamic measures
- Using conditional statements
- Working with disconnected tables
- Designing charts and visualizations
Topics Covered:
- Explain advantages of Power BI for a business organization.
- Differentiate the process of uploading data to a file stored locally from uploading data to a file stored in the cloud.
- Recognize the analytical value provided by different visualization types in Power BI.
- Identify the limitations in sharing a Power BI dashboard with someone outside your domain.
- Describe ease-of-use features available with Power BI for Mobile.
- Apply knowledge of Power BI Desktop to a scenario.
Topics Covered:
- Importing from flat files such as .txt and .csv.
- Importing from pickled files, Excel spreadsheets, SAS and Stata files, HDF5 files, and MATLAB files.
- Importing from relational databases such as SQLite, MySQL, and PostgreSQL.
- Using sqlalchemy, NumPy, and pandas to import files and customize your imports.
Topics Covered:
- Running a Jupyter notebook
- Using shortcuts
- Line and cell commands
- Visualizing data with plotting
- Publishing Jupyter notebooks in GitHub
- Presenting my notebooks with the slideshow feature
Topics Covered:
- Data visualization principles
- How to communicate data-driven findings
- How to use ggplot2 to create custom plots
- The weaknesses of several widely-used plots and why you should avoid them
Topics Covered:
- Understanding of data sources a company can use and how to store that data.
- Ways to analyze and visualize data through dashboards and A/B tests.
- Machine learning, including clustering, time series prediction, natural language processing (NLP), deep learning, and explainable AI!
- Variety of real-world applications of data science through practical exercises.
Topics Covered:
- Basic R syntax
- Foundational R programming concepts such as data types, vectors arithmetic, and indexing
- How to perform operations in R including sorting, data wrangling using dplyr, and making plots
Topics Covered:
- Determine how to utilize display graphs and charts including bar, Pareto charts, and scatterplots.
- Recognize how to compare variance and multiple means.
- Identify how to compare medians.
- Explore how to run a multiple regression test and examine the resulting data.
- Break down how to compare inferences on continuous data.
- Differentiate how to compare proportions.
Topics Covered:
- Collecting the voice of the customer
- Process maps
- Sampling in data collection
- Measurement system analysis
- Measuring performance using descriptive statistics
- Process performance measures
- Hypothesis testing
- Testing for means, variances, proportions, and independence
- Correlation and regression
- Using failure modes and effects analysis
- Statistical process control
Topics Covered:
- Describe what a P chart is used for.
- Identify how a C chart can be used to summarize count data.
- Define both X-bar and R charts.
- State the focus of statistical process control.
- Demonstrate how to calculate the standard deviation of data in a sample set.
- Point out what process capability analysis incorporates.
- Explain the process for calculating the process capability index.
- Identify the lowest point that the control limit line can go.
Topics Covered:
- Name the predicate of the following statement: SELECT EyeColor, Age FROM Student WHERE FirstName = 'Tim' ORDER BY LastName ASC;
- Explain what to use to enforce the order in which an expression must be evaluated if the WHERE clause contains multiple expressions to evaluate.
Identify the best option to join two tables in a database to be able to display data from both.
- List a data type that is not numeric.
- Determine the result of running the following statement on a table containing columns col_1 and col_2:
- Determine the best approach of deleting Jon Ramirez (ID 3452) from a Student table.
Topics Covered:
- Differentiate between perceptrons and sigmold neurons.
- Describe the three types of layers of a neural network.
- Identify the purpose of weights.
- Recognize the steps for initializing a neural network.
- Explain how back propagation improves accuracy.
- Evaluate the effectiveness of supervised and unsupervised learning methods in a given situation.
Topics Covered:
- Managing data sources and visualizations
- Creating custom calculations and fields
- Analyzing data using statistical tools
- Sorting and filtering Tableau data
- Defining groups and sets
- Creating and pivoting crosstabs
- Formatting Tableau visualizations
- Annotating and formatting charts
- Mapping geographic data
- Creating dashboards and actions
Topics Covered:
- Creating a machine learning workspace
- Creating and training an experiment
- Creating a predictive experiment
- Deploying an experiment as a web service
- Enabling logging
- Viewing logs for diagnostic purposes
- Scale and geographic deployment of your service
- Using machine learning with API management
Topics Covered:
- What is Azure ML Studio?
- Creating experiments
- Cleaning data
- Scoring the model
- Evaluating the model
- Deploying and testing the experiment
- Calling the model using custom code
Topics Covered:
- Artificial intelligence and Azure Machine Learning
- Supervised vs. unsupervised learning
- Reinforcement learning
- Data quality
- Making predictions
- Machine learning algorithms