Establish a comprehensive foundation in data analytics by exploring both descriptive and inferential statistics, and learning how data drives forecasting and strategic organizational decisions across industries. Dive deep into statistical modeling and analysis, examining essential algorithms, foundational theorems, and their practical real-world applications.
Build Strong Analytical Foundations with Statistics
This beginner-focused course introduces you to the fundamental stages of the data analytics workflow, covering critical topics such as descriptive statistics, inferential statistics, and essential data distribution concepts that form the backbone of analytical work.
Understand How Organizations Use Data for Strategic Planning
Investigate how leading organizations utilize predictive and prescriptive analytics to inform and guide strategic decision-making at all levels. Explore the analytical tools prevalent in contemporary business environments and recognize the expanding importance of Big Data in shaping organizational operations and competitive advantage.
Explore Statistical Analysis Methods and Modeling Techniques
Examine core statistical approaches, including fundamental algorithms, theorems, and analytical models that professionals employ for data analysis, pattern recognition, and accurate forecasting in business contexts.
Important Course Prerequisites
- Students should be comfortable using Excel at a fundamental level. Experience comparable to our Excel Level II: Intermediate class is highly recommended, although not absolutely required for enrollment.
Data Analytics Foundations Course Online Curriculum and Content
Core Learning Outcomes
- Master core statistical principles, including measures of central tendency, data dispersion, and the properties of the normal distribution
- Examine descriptive and inferential statistical methods, including probability distributions such as binomial and Poisson distributions
- Learn comprehensive data analysis and forecasting approaches using correlation techniques, linear regression, and multiple regression analysis
- Implement predictive analytics applications, including trendlines, moving averages, and scenario modeling methods
- Construct informative data visualizations using charts, histograms, icon sets, color scales, sparklines, and Pivot Tables
- Apply prescriptive analytics approaches, such as Solver and linear programming, to optimize organizational decision-making
Detailed Course Curriculum Outline
- Basic Data Analysis Foundations
- Measures of Central Tendency and Average Calculations
- Measures of Position and Distribution
- Measures of Dispersion and Variability
- Understanding the Normal Curve and Distribution
- Descriptive Statistics Methods
- Predictive Analytics I Techniques
- Forecasting Methods
- Series Forecast Application
- Data Visualization I Tools
- Chart Construction and Types
- Icon Sets and Visual Representations
- Histograms for Distribution Analysis
- Moving Average Calculations
- Predictive Analytics Advanced
- Correlation and Relationship Analysis
- Regression Analysis Overview
- Regression Analysis Application
- Linear Regression Techniques
- Multiple Regression Approaches
- Probability Theory and Applications
- Probability Fundamentals I
- Probability Theory II
- Binomial Probability Distribution
- Poisson Probability Distribution
- Prescriptive Analytics I Strategies
- What If Analysis Tools
- Data Table Application (3 variables)
- Scenario Manager Implementation
- Scenario Manager and Pivot Analysis
- Data Visualization II Advanced
- Sparklines and Mini Charts
- Color Scales for Pattern Recognition
- Drawing Shapes and Annotations
- Pivot Tables for Summarization
- Pivot Charts for Visual Analysis
- Prescriptive Analytics II Optimization
- Solver Overview and Setup
- Linear Programming Applications
- Building the Solver Model
- Non-Linear Programming Methods
- Evolutionary Solver Applications
Learn more about the Data Analytics Foundations Course Online (Self-Paced) at Noble Desktop.
