###### CCNA 200-301 Pearson uCertify Network Simulator

ISBN: 978-1-61691-837-8Cisco 200-301-SIMULATOR.AB1

Kickstart your career with this beginner-friendly Data Analytics course by learning how to use data for making informed decisions.

(FDN-DA.AE1) / ISBN : 978-1-64459-277-9This course includes

Lessons

TestPrep

Hands-On Labs

AI Tutor (Add-on)

27
Review

Foundation of Data Analytics is a beginner-friendly certification course that focuses on data science tools for effective analysis and visualization. This introduction to data analytics course teaches you the techniques for transforming raw data into actionable insights that can be used for making informed decisions. Learn how to perform data manipulation and the role of this information in improving business statistics. Understand the dynamics of data optimization, and forecasting techniques using regression analysis, and data visualizations.

- Expertise is using Excel for handling and manipulating data
- Identify issues and perform data clean up
- Expertise in hypothesis testing, regression analysis, and correlation analysis
- Understanding the business landscape and use of data
- Knowledge of business analysis
- Maintain high-quality data for effective data management
- Creating informative charts for visual representation
- Communicate data-driven insights
- Exposure to R for Data science

Get the support you need. Enroll in our Instructor-Led Course.

7+ Interactive Lessons | 9+ Exercises | 64+ Quizzes | 112+ Flashcards | 112+ Glossary of terms

51+ Pre Assessment Questions | 53+ Post Assessment Questions |

35+ LiveLab | 29+ Video tutorials | 38+ Minutes

1

- Opening Case
- Introduction
- Managers and Decision Making
- The Business Analytics Process
- Business Analytics Tools
- Business Analytics Models: Descriptive Analytics, Predictive Analytics, and Prescriptive Analytics
- Summary
- Discussion Questions
- Closing Case 1
- Closing Case 2

2

- Some Sample Data
- Moving Quickly with the Control Button
- Copying Formulas and Data Quickly
- Formatting Cells
- Paste Special Values
- Inserting Charts
- Locating the Find and Replace Menus
- Formulas for Locating and Pulling Values
- Using VLOOKUP to Merge Data
- Filtering and Sorting
- Using PivotTables
- Using Array Formulas
- Solving Stuff with Solver
- OpenSolver: I Wish We Didn't Need This, but We Do

3

- Opening Case
- Introduction
- Managing Data
- The Database Approach
- Big Data
- Data Warehouses and Data Marts
- Knowledge Management
- Summary
- Discussion Questions
- Problem-Solving Activities
- Closing Case 1
- Closing Case 2

4

- Introduction to Probability
- Structure of Probability
- Marginal, Union, Joint, and Conditional Probabilities
- Addition Laws
- Multiplication Laws
- Conditional Probability
- Revision of Probabilities: Bayes' Rule
- Introduction to Hypothesis Testing
- Testing Hypotheses About a Population Mean Using the z Statistic (σ Known)
- Testing Hypotheses About a Population Mean Using the t Statistic (σ Unknown)
- Testing Hypotheses About a Proportion
- Testing Hypotheses About a Variance
- Solving for Type II Errors
- Summary
- Formulas
- Supplementary Problems
- Analyzing the Databases
- Case - Colgate-Palmolive Makes a “Total” Effort

5

- Why Should Data Scientists Know Optimization?
- Starting with a Simple Trade-Off
- Fresh from the Grove to Your Glass…with a Pit Stop through a Blending Model
- Modeling Risk
- Wait, What? You're Pregnant?
- Don't Kid Yourself
- Predicting Pregnant Customers at RetailMart Using Linear Regression
- Predicting Pregnant Customers at RetailMart Using Logistic Regression
- For More Information
- Correlation
- Introduction to Simple Regression Analysis
- Determining the Equation of the Regression Line
- Residual Analysis
- Standard Error of the Estimate
- Coefficient of Determination
- Hypothesis Tests for the Slope of the Regression Model and Testing the Overall Model
- Estimation
- Using Regression to Develop a Forecasting Trend Line
- Interpreting the Output
- Summary
- Formulas
- Supplementary Problems
- Analyzing the Databases
- Case - Caterpillar, Inc.

6

- Getting Up and Running with R
- Doing Some Actual Data Science

7

- Why Do We Visualize Data?
- How Do We Visualize Data?
- Color
- Common Chart Types
- When Our Visual Processing System Betrays Us
- Every Decision Is a Compromise
- Summary

1

- Summarizing the Aspects of Business Analytics and its Applications

2

- Freezing the Top Row
- Using the AVERAGE Function
- Using Relative, Absolute, and Mixed References
- Formatting Numbers
- Applying Conditional Formatting
- Using the Paste Special Feature
- Analyzing Data Using a Line Chart
- Creating a PivotTable Automatically
- Calculating the Minimum and Maximum Sales Value
- Using the SUM Function
- Using the MATCH Function
- Using the VLOOKUP Function
- Sorting Data

3

- Understanding Big Data
- Understanding the Relational Database Model

4

- Understanding Business Statistics
- Calculating the Statistics

5

- Using the SUMIF Function
- Using the IF Function

6

- Using the factor() Function
- Using the str() Function
- Using the sqrt() Function
- Using the matrix() Function
- Using the length() Function
- Using the rbind() and cbind() Functions
- Using the aggregate() Function
- Using the order() Function
- Using the predict() Function
- Using the print() Function
- Using the summary() Function
- Using the which() Function

7

- Visualizing Data
- Understanding Data Visualization
- Creating and Analyzing Chart Types

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