Data Analyst
Competency-based
ONET: 15-2051.00
1
Years
28
Skills
72h
Related instructions
Classroom instruction topics
- Exploratory data analysis
- SQL
- Environment Setup / Data Scientist Workflow
- Statistics
- Object Oriented Programming
- Ins and Outs of Coding Pythonically - Functions & Scope, Linking it all together
- Matplotlib & Tableau
- Intro to modeling -Graph theory and modeling, Social Network Analysis
- Data Visualization in Python
- Ins and Outs of Coding Pythonically - Introduction to Python data structures, Python libraries, Lists, Strings, Tuples, Dictionaries, and Sets
- Intro to modeling - Decision trees, random forests
- Probability
- Introduction to Statistical Modeling
On-the-job training
- Statistics and Programming Foundation
- Understand sampling, probability theory, and probability distributions
- Demonstrate knowledge of descriptive statistical concepts
- Demonstrate knowledge of inferential statistics
- Demonstrate knowledge of python programming skills
- Implement descriptive and inferential statistics using python
- Demonstrate ability to visualize data and extract insights
- Demonstrate through a project the ability to analyze a dataset and communicate insights
- Data Science Foundation
- Demonstrate understanding of what is Data Analyst and what Data Analyst do
- Demonstrate ability to characterize a business problem
- Demonstrate ability to formulate a business problem as a hypothesis question
- Demonstrate use of methodologies in the execution of the analytics cycle
- Demonstrate through a project the ability to plan for the execution of a project
- Data Preparation
- Demonstrate ability to identify and collect data – multiple formats
- Demonstrate ability to manipulate, transform, and clean data
- Demonstrate expertise with techniques to deal with missing values, outliers, unbalanced data, as well as data normalization
- Demonstrate through a project the ability to construct usable data sets
- Model Building
- Demonstrate understanding of Linear Algebra principles for Machine Learning
- Demonstrate understanding of different modeling techniques
- Demonstrate understanding of model validation and selection techniques
- Communicate results translating insight into business value
- Demonstrate through a project the ability to test different models on a dataset, validate and select the best model, and communicate results
- Model Deployment
- Deploy and monitor a validated model in an operational environment
- Demonstrate through a project the ability to deploy and use a deployed model
- Big Data Foundation
- Understand the concept of Big Data, and how Big Data is used at organizations
- Understand with the Big Data ecosystem and its major components
- Demonstrate through a project expertise
- Leadership and Professional Development Skills
- Participate as a data analyst on own organizations projects or external projects.
- Contribute to the profession by teaching or mentoring others
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