- Type Validation
- Level Foundational
Data Science and Its Applications
The Data Science and Its Applications industry-aligned credential is awarded to earners who successfully complete Inferential Statistics – Hypothesis Testing; Inferential Statistics – Regression, ANOVA, and Forecasting; Power Bl and Real-time Dashboards; Intro to R; Applications of R; Machine Learning and Predictive Modeling in R; Intro to Python; Data Analysis in Python; Machine Learning in Python
- Type Validation
- Level Foundational
Skills
- Analysis Of Variance (ANOVA)
- ANOVA
- Artificial Intelligence (AI)
- Basic Diagnostics
- Basic R Functions
- Big Data
- Binomial and Discrete Distributions
- Call Modules in Python
- CART
- Central Limit Theorem
- Chi-square
- Clustering Methods
- Cluster Number
- Confidence Intervals
- Convolutional Neural Network Models
- Dashboard
- Dashboards
- Data Analysis
- Data Analytics in R
- Data Management
- Data Science
- Datasets
- Data Visualization
- Decision Trees
- Decision Trees and CART Modeling
- Deep Learning
- Descriptive Statistics and Data Visualization
- Dplyr
- Else
- Flow Control
- Forecasting
- Functions
- If
- Imputation Methods
- Interactive Data Visualization
- Interpretation Of Results
- Intro to Statistics
- Keras
- Key Performance Indicators (KPIs)
- kNN Classificiation
- kNN Model
- Libraries
- Linear and Logistic Regression
- Linear Regression
- Linear Regression Models
- Logistic Regression
- Looping
- Loops
- Machine Learning
- Machine Learning (ML)
- Market Basket Analysis
- Matplotlib Library
- MLxtend
- Model Diagnostics
- Model Selection
- Modules
- Naïve Bayes Classification
- Natural Language Processing (NLP)
- Nearest Neighbor Algorithms
- Neural Network Models
- Neural Networks
- Normal Distribution
- Output Predictions
- Pandas Module
- PCA
- PIP
- Power BI
- Predictive Modeling
- Principal Component Analysis (PCA)
- Principles of Hypothesis Testing
- Python
- Python (Programming Language)
- Python Syntax
- Random Forest
- Random Number Generators
- R concepts
- Real-Time Reporting
- Regression
- Regression Analysis
- Regression Testing
- Researchpy Module
- R Packages
- R (Programming Language)
- R Syntax
- Sampling Methods
- Scikit-learn (Sklearn)
- Scipy Module
- Seaborn Libraries
- Seaborn Module
- Statistical Hypothesis Testing
- Statistical Inference
- Statistics and Model Fit
- Statistics Module
- Summary Calculations
- Support-vector Machines
- SVM Models
- TensorFlow
- Testing Differences in Means
- Tests of Categorical Variables
- Tidyverse
- Time-series
- Training Sets
- T-tests
- Validation Sets
- Variable Classes
- Variable Types
- Variance Testing (QA/QC)
Earning Criteria
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Earners have successfully completed all assessments and projects inherent in the constellation (Inferential Statistics – Hypothesis Testing; Inferential Statistics – Regression, ANOVA, and Forecasting; Power Bl and Real-time Dashboards; Intro to R; Applications of R; Machine Learning and Predictive Modeling in R; Intro to Python; Data Analysis in Python; Machine Learning in Python). All assessments required a minimum score of 80%