Data Analytics Foundations
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- Live or Self-paced
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Book live on Noble DesktopSummary
This beginner-friendly course introduces the core concepts of data analytics, from descriptive and inferential statistics to data distribution and modeling techniques. You’ll explore how organizations use predictive and prescriptive analytics to support decision-making, gain insight into the tools and methods applied across industries, and understand the growing role of big data in today’s business landscape.
Prerequisites
Students should feel comfortable using Excel at a basic level. Experience equal to our Excel Level II: Intermediate class is strongly recommended, but not required.
Curriculum
What you'll learn
- Understand core statistical concepts such as measures of central tendency, data dispersion, and the normal distribution
- Explore descriptive and inferential statistics, including probability distributions such as binomial and Poisson
- Learn to analyze and forecast data using correlation, linear regression, and multiple regression models
- Apply predictive analytics using tools such as trendlines, moving averages, and scenario modeling
- Create clear data visualizations with charts, histograms, icon sets, color scales, sparklines, and pivot tables
- Discover prescriptive analytics methods like Solver and linear programming to support optimized decision-making
Course syllabus
Basic Data Analysis
- Measures of Central Tendency
- Measures of Position
- Measures of Dispersion
- The Normal Curve
- Descriptive Statistics
Predictive Analytics I
- Forecasting
- Series Forecast
Data Visualization I
- Charts
- Icon Sets
- Histograms
- Moving Average
Predictive Analytics
- Correlation
- Regression - overview
- Regression - analysis
- Linear regression
- Multiple regression
Probability
- Probability I
- Probability II
- Binomial Probability
- Poisson Probability
Prescriptive Analytics I
- What If Analysis
- Data Table (3 variables)
- Scenario Manager
- Scenario Manager - Pivot
Data Visualization II
- Sparklines
- Color Scales
- Drawing Shapes
- Pivot Tables
- Pivot Charts
Prescriptive Analytics II
- Solver - overview
- Linear Programming
- The Solver model
- Non-Linear Programming
- Evolutionary Solver
What's included
- Free course retake within one year to refresh the material and gain practice.
- Class recordings
Stated by the provider. Confirm what your tuition covers before enrolling.
Live classes
Choose dates and book your instructor-led class on nobledesktop.com.
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In-person or live online
Starts at 18:00 · America/New_York (EDT); final session ends at 21:00 EDT
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In-person or live online
Starts at 10:00 · America/New_York (EDT); final session ends at 17:00 EDT
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In-person or live online
Starts at 10:00 · America/New_York (EDT); final session ends at 17:00 EDT
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In-person or live online
Starts at 10:00 · America/New_York (EST); final session ends at 17:00 EST
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–
In-person or live online
Starts at 10:00 · America/New_York (EST); final session ends at 17:00 EST
-
–
In-person or live online
Starts at 18:00 · America/New_York (EST); final session ends at 21:00 EST
-
–
In-person or live online
Starts at 10:00 · America/New_York (EST); final session ends at 17:00 EST
-
–
In-person or live online
Starts at 10:00 · America/New_York (EST); final session ends at 17:00 EST
-
–
In-person or live online
Starts at 10:00 · America/New_York (EDT); final session ends at 17:00 EDT
-
–
In-person or live online
Starts at 18:00 · America/New_York (EDT); final session ends at 21:00 EDT
-
–
In-person or live online
Starts at 10:00 · America/New_York (EDT); final session ends at 17:00 EDT
-
–
In-person or live online
Starts at 10:00 · America/New_York (EDT); final session ends at 17:00 EDT
-
–
In-person or live online
Starts at 10:00 · America/New_York (EDT); final session ends at 17:00 EDT
-
–
In-person or live online
Starts at 10:00 · America/New_York (EDT); final session ends at 17:00 EDT
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–
In-person or live online
Starts at 10:00 · America/New_York (EST); final session ends at 17:00 EST
Confirm current dates and availability on Noble Desktop before booking.
Self-paced course
Learn through recorded lessons on your own schedule.
This course is available for 30 days. You can choose when to start your access period. Once you activate, you will have 30 days to complete it (access the course materials, quizzes, projects and videos). You may request one extension of seven (7) days. Other extension requests will be evaluated on a case-by-case basis. Videos are not downloadable.
- Tuition
- $595
- Course length
- 12 hours
- Schedule
- On your schedule
Build a Strong Foundation in Data Analytics This beginner-level course introduces the key stages of the data analytics process, covering essential topics such as descriptive and inferential statistics, as well as data distribution concepts.
Discover How Data Drives Decisions Explore how organizations leverage predictive and prescriptive analytics to guide strategic decisions. Learn about the tools commonly used in industry and the growing role of Big Data in business operations.
Dive Into Statistical Analysis and Modeling Examine core statistical methods, including foundational algorithms, theorems, and models used for data analysis and forecasting.
Self-paced prerequisites
Students should feel comfortable using Excel at a basic level. Experience equal to our Excel Level II: Intermediate class is strongly recommended, but not required.
Self-paced curriculum
What you'll learn self-paced
- Understand core statistical concepts including measures of central tendency, data dispersion, and the normal curve
- Explore descriptive and inferential statistics, including probability distributions like binomial and Poisson
- Learn to analyze and forecast data using correlation, linear regression, and multiple regression techniques
- Apply predictive analytics with tools like trendlines, moving averages, and scenario modeling
- Create clear data visualizations using charts, histograms, icon sets, color scales, sparklines, and pivot tables
- Discover prescriptive analytics techniques such as Solver and linear programming to optimize decision-making
Self-paced syllabus
Basic Data Analysis
- Measures of Central Tendency
- Measures of Position
- Measures of Dispersion
- The Normal Curve
- Descriptive Statistics
Predictive Analytics I
- Forecasting
- Series Forecast
Data Visualization I
- Charts
- Icon Sets
- Histograms
- Moving Average
Predictive Analytics
- Correlation
- Regression - overview
- Regression - analysis
- Linear regression
- Multiple regression
Probability
- Probability I
- Probability II
- Binomial Probability
- Poisson Probability
Prescriptive Analytics I
- What If Analysis
- Data Table (3 variables)
- Scenario Manager
- Scenario Manager - Pivot
Data Visualization II
- Sparklines
- Color Scales
- Drawing Shapes
- Pivot Tables
- Pivot Charts
Prescriptive Analytics II
- Solver - overview
- Linear Programming
- The Solver model
- Non-Linear Programming
- Evolutionary Solver
What's included with self-paced
- Self-paced video lessons
Self-paced lessons
Watch free previews and explore the lessons included with enrollment at Noble Desktop.
Data Analytics Foundations Course Online (Self-Paced)
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Central Tendency
Lesson 1: Basic Data Analysis
10:00
Explain measures of central tendency, including mean, median, mode, and mid-range, using examples and Excel functions to analyze data distribution.
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Enrollment required
Mixed Reference
Lesson 1: Basic Data Analysis
5:50
Use mixed cell referencing to efficiently apply formulas across multiple cells by locking either columns or rows, enabling quick calculations in spreadsheets.
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Enrollment required
Position
Lesson 1: Basic Data Analysis
15:05
Compare data using percentiles to rank positions and quartiles to divide data into four equal parts, visualizing results with box-and-whisker charts for risk assessment.
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Enrollment required
Dispersion
Lesson 1: Basic Data Analysis
15:26
Explain measures of dispersion by using standard deviation and variance to describe how far data points fall from the mean.
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Enrollment required
Normal Curve
Lesson 1: Basic Data Analysis
17:38
Create a bell curve using the normal distribution function by calculating the mean and standard deviation, and use z-scores to determine standard deviations from the mean.
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Enrollment required
Descriptive Statistics
Lesson 1: Basic Data Analysis
13:46
Summarize descriptive statistics concepts such as skewness, kurtosis, and standard error, and demonstrate how to use Excel's analysis tool pack to generate relevant statistical calculations.
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T Test
Lesson 2: Predictive Analytics I
10:00
Run a paired T-Test using the Data Analysis Toolpak in Excel to determine the significance of performance changes by comparing t-stat and p-values before and after an intervention.
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Enrollment required
Correlation
Lesson 2: Predictive Analytics I
16:28
Examine linear relationships between variables by calculating correlation coefficients, recognizing that correlation does not imply causation.
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Enrollment required
Series Forecast
Lesson 2: Predictive Analytics I
6:09
Forecast future values using Excel's fill series tool for linear or exponential growth patterns.
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Enrollment required
Forecasting
Lesson 2: Predictive Analytics I
17:23
Explore forecasting in Excel using methods like least sum of squares, exponential smoothing, and confidence intervals to predict future data based on historical trends.
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Enrollment required
Regression
Lesson 2: Predictive Analytics I
8:41
Explain how to use linear regression and Excel tools to model and predict relationships between dependent and independent variables.
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Enrollment required
Regression Analysis
Lesson 2: Predictive Analytics I
9:24
Calculate regression analysis using linear regression, predict future outcomes, and evaluate correlation strength with R-squared.
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Enrollment required
Simple Linear Regression
Lesson 2: Predictive Analytics I
9:17
Estimate the relationship between variables using Excel's analysis tool pack to determine the least sum of squares regression equation, assess its predictive accuracy, and calculate the independent variable's contribution.
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Multiple Regression
Lesson 2: Predictive Analytics I
6:36
Perform a multiple regression analysis using the data analysis tool pack to identify the impact of employees, products, and advertising expenses on company revenue.
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Histogram
Lesson 3: Data Visualization I
10:00
Create histograms by using the analysis tool pack to group data into intervals and visualize frequency distributions for categories and seniority levels.
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Enrollment required
Moving Averages
Lesson 3: Data Visualization I
7:40
Calculate different types of moving averages to smooth data and assess recent trends, including simple, weighted, and exponential methods.
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Charts
Lesson 3: Data Visualization I
7:53
Transform data into visual insights using stacked column, area, tree map, funnel, and pie charts for clear representation of values and relationships.
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Probability 1
Lesson 4: Probability
10:00
Summarize the key points about probability, including concepts like zero to one range, law of large numbers, calculating probabilities using Excel's RAND function, and applications in games like card drawing and craps.
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Enrollment required
Probability 2
Lesson 4: Probability
13:34
Calculate probabilities and analyze independent versus dependent events, expected values, fair prices, and conditional probability.
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Binominal
Lesson 4: Probability
14:18
Calculate the probability of a specific number of successes in a fixed number of independent binary (success/failure) trials using binomial distribution.
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Enrollment required
Poisson Distribution
Lesson 4: Probability
11:11
Calculate the probability of a specific number of events occurring within a fixed interval using the Poisson distribution.
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Enrollment required
Monte Carlo
Lesson 4: Probability
12:25
Use Monte Carlo simulation to assess the likelihood of achieving a profit over $10,000 by modeling revenue and costs with variable distributions and calculating outcomes over 300 scenarios.
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Enrollment required
Resampling
Lesson 4: Probability
12:15
Resample data by using techniques like RANDBETWEEN and VLOOKUP to fill in missing information and make statistical inferences, allowing for comparison and analysis despite incomplete data.
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Statistics
Lesson 4: Probability
9:30
Calculate weighted averages and total salaries with conditions using the SUMPRODUCT function alongside SUMIF as needed.
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Scenario Manager
Lesson 5: Prescriptive Analytics I
10:00
Use Excel's Scenario Manager to create, edit, and summarize different financial scenarios, such as base, best-case, and worst-case scenarios, for revenue growth, profit margin, and price per earnings, and generate a summary report to compare their impacts on projected revenues and values.
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Scenario Manager 2
Lesson 5: Prescriptive Analytics I
10:20
Use the Scenario Manager in Excel to create and analyze multiple hiring scenarios, summarizing the financial impact of hiring up to five new employees on company benefits.
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Solver 1
Lesson 5: Prescriptive Analytics I
11:46
Install Solver through Excel's add-ins, set objective and constraints, and use it to determine optimal production quantities for maximum profit.
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Solver 2
Lesson 5: Prescriptive Analytics I
4:29
Use Solver to abandon Project B to maximize net present value while keeping capital usage within available limits.
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Solver 3
Lesson 5: Prescriptive Analytics I
9:07
Attempt to use Excel Solver to optimize the route between cities for minimal travel distance, potentially reducing total miles from 18,275 to a lower value using GRG nonlinear or evolutionary techniques.
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Pivot Tables
Lesson 6: Data Visualization II
10:00
Create and analyze pivot tables to summarize data, perform calculations, and visualize information using pivot charts and slicers for efficient data manipulation and interpretation.
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Enrollment required
Sparklines
Lesson 6: Data Visualization II
7:55
Visualize data trends using sparklines by inserting miniature, single-cell charts like lines, columns, or win/loss indicators to represent changes and highlight high or low points.
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Enrollment required
Color Scales
Lesson 6: Data Visualization II
8:28
Apply color scales and icon sets to format cells based on their values, sort data in ascending or descending order, and use the percentile function to determine threshold values for conditional formatting.
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