- Introduction to Data Science
- Introduction to Statistic for Data Science
- Common statistic used by Data Science
- Introduction to Data Science Tools (Python)
- Install and use package
- Basic Python syntax
- Connect and explore your data
- Numpy for data manipulation
- Graphical and numerical techniques to begin uncovering the structure of data
- Import, build, and manipulate Data Frame
- Tidy, rearrange, and structure data
- Introduction to Data Science Methodology
- Apply Data Science with Classification (Decision Tree) Model Apply Data Science with Estimation (Regression) Model
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- Introduction to Data Warehousing
- Implementation, Operation and Expansion of Data Warehouse
- Introduction to Data Integration tools
- Working with files
- Joining data sources
- Filtering data
- Using context variables
- Error handling
- Generic schemas
- Working with databases
- Creating master Jobs
- Running a stand-alone Job
- Documenting a Job
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- Introduction Data Visualization Concepts
- Introduction to Data Visualization Tools (Tableau)
- Connecting to Data
- Different Chart Types and When to Use Each One
- Adding Filter, Sort, and Group
- Work with Time series Data
- Using Multiple Measure in a View
- Showing the Relationship Between Numerical Values
- Mapping Data Geographically
- Viewing Specific Values
- Basic Calculations
- Highlight Data with Reference Line
- Making Your Views Available
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- Understanding Database Concepts
- Understanding Data Manipulation Language (DML)
- Understanding Data Definition Language (DDL)
- Defining Data Types
- Creating and Using Tables
- Creating Views
- Creating Stored Procedures
- Using Queries to Select Data
- Using Queries to Insert Data
- Updating Data and Databases
- Deleting Data
- Normalizing a Database
- Understanding Primary, Foreign, and Composite Keys
- Understanding Clustered and Non-Clustered Indexes
- Securing Databases
- Backing Up and Restoring Databases