TECH221: Data-Driven Decision-Making

(DV-TECH221.AE1)
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1

Today's Data Analyst

  • Welcome to the World of Analytics
  • Careers in Analytics
  • The Analytics Process
  • Analytics Techniques
  • Data Governance
  • Analytics Tools
  • Summary
2

Understanding Data

  • Exploring Data Types
  • Common Data Structures
  • Common File Formats
  • Summary
  • Exam Essentials
3

Databases and Data Acquisition

  • Exploring Databases
  • Database Use Cases
  • Data Acquisition Concepts
  • Working with Data
  • Summary
  • Exam Essentials
4

Data Quality

  • Data Quality Challenges
  • Data Manipulation Techniques
  • Managing Data Quality
  • Summary
  • Exam Essentials
5

Data Analysis and Statistics

  • Fundamentals of Statistics
  • Descriptive Statistics
  • Inferential Statistics
  • Analysis Techniques
  • Summary
  • Exam Essentials
6

Data Analytics Tools

  • Spreadsheets
  • Programming Languages
  • Statistics Packages
  • Machine Learning
  • Analytics Suites
  • Summary
  • Exam Essentials
7

Data Visualization with Reports and Dashboards

  • Understanding Business Requirements
  • Understanding Report Design Elements
  • Understanding Dashboard Development Methods
  • Exploring Visualization Types
  • Comparing Report  Types
  • Summary
  • Exam Essentials
8

Data Governance

  • Data Governance Concepts
  • Understanding Master Data Management
  • Summary
  • Exam Essentials
9

The Art and Science of Business Statistics

  • Representing the Key Properties of Data
  • Probability: The Foundation of All Statistical Analysis
  • Using Sampling Techniques and Sampling Distributions
  • Statistical Inference: Drawing Conclusions from Data
10

Pictures Tell the Story: Graphical Representations of Data

  • Analyzing the Distribution of Data by Class or Category
  • Histograms: Getting a Picture of Frequency Distributions
  • Checking Out Other Useful Graphs
11

Finding a Happy Medium: Identifying the Center of a Data Set

  • Looking at Methods for Finding the Mean
  • Getting to the Middle of Things: The Median of a Data Set
  • Comparing the Mean and Median
  • Discovering the Mode: The Most Frequently Repeated Element
12

Searching High and Low: Measuring Variation in a Data Set

  • Determining Variance and Standard Deviation
  • Finding the Relative Position of Data
  • Measuring Relative Variation
13

Measuring How Data Sets Are Related to Each Other

  • Understanding Covariance and Correlation
  • Interpreting the Correlation Coefficient
14

Probability Theory: Measuring the Likelihood of Events

  • Working with Sets
  • Betting on Uncertain Outcomes
  • Looking at Types of Probabilities
  • Following the Rules: Computing Probabilities
15

Probability Distributions and Random Variables

  • Defining the Role of the Random Variable
  • Assigning Probabilities to a Random Variable
  • Characterizing a Probability Distribution with Moments
16

The Binomial, Geometric, and Poisson Distributions

  • Looking at Two Possibilities with the Binomial Distribution
  • Determining the Probability of the Outcome That Occurs First: Geometric Distribution
  • Keeping the Time: The Poisson Distribution
17

The Uniform and Normal Distributions: So Many Possibilities!

  • Comparing Discrete and Continuous Distributions
  • Working with the Uniform Distribution
  • Understanding the Normal Distribution
18

Sampling Techniques and Distributions

  • Sampling Techniques: Choosing Data from a Population
  • Sampling Distributions
  • The Central Limit Theorem
19

Confidence Intervals and the Student’s t-Distribution

  • Almost Normal: The Student’s t-Distribution
20

Testing Hypotheses about the Population Mean

  • Applying the Key Steps in Hypothesis Testing for a Single Population Mean
21

Testing Hypotheses about Multiple Population Means

  • Getting to Know the F-Distribution
  • Using ANOVA to Test Hypotheses
22

Testing Hypotheses about the Population Mean

  • Staying Positive with the Chi-Square Distribution
  • Testing Hypotheses about the Population Variance
  • Practicing the Goodness of Fit Tests
  • Testing Hypotheses about the Equality of Two Population Variances
23

Simple Regression Analysis

  • The Fundamental Assumption: Variables Have a Linear Relationship
  • Defining the Population Regression Equation
  • Estimating the Population Regression Equation
  • Testing the Estimated Regression Equation
  • Using Statistical Software
  • Assumptions of Simple Linear Regression
24

Multiple Regression Analysis: Two or More Independent Variables

  • The Fundamental Assumption: Variables Have a Linear Relationship
  • Estimating a Multiple Regression Equation
  • Checking for Multicollinearity
25

Forecasting Techniques: Looking into the Future

  • Defining a Time Series
  • Modeling a Time Series with Regression Analysis
  • Forecasting a Time Series
  • Changing with the Seasons: Seasonal Variation
  • Implementing Smoothing Techniques
  • Comparing the Forecasts of Different Models
26

Ten Common Errors That Arise in Statistical Analysis

  • Designing Misleading Graphs
  • Drawing the Wrong Conclusion from a Confidence Interval
  • Misinterpreting the Results of a Hypothesis Test
  • Placing Too Much Confidence in the Coefficient of Determination (R2)
  • Assuming Normality
  • Thinking Correlation Implies Causality
  • Drawing Conclusions from a Regression Equation when the Data do not Follow the Assumptions
  • Including Correlated Variables in a Multiple Regression Equation
  • Placing Too Much Confidence in Forecasts
  • Using the Wrong Distribution
27

Ten Key Categories of Formulas for Business Statistics

  • Summary Measures of a Population or a Sample
  • Probability
  • Discrete Probability Distributions
  • Continuous Probability Distributions
  • Sampling Distributions
  • Confidence Intervals for the Population Mean
  • Testing Hypotheses about Population Means
  • Testing Hypotheses about Population Variances
  • Using Regression Analysis
  • Forecasting Techniques

1

Today's Data Analyst

  • Understanding Data Analytics Techniques
2

Understanding Data

  • Understanding Data Types
  • Identifying Categories of Data
  • Understanding Common Data Structures
3

Databases and Data Acquisition

  • Creating a Data Model through ERD
  • Normalizing Data from 2NF to 3NF
  • Normalizing Data from 1NF to 2NF
  • Normalizing Unnormalized Data to 1NF
  • Sorting Data
  • Removing Unnecessary Data
  • Updating Existing Data
  • Retrieving Specific Data
4

Data Quality

  • Eliminating Redundant Data
  • Concatenating Data
  • Understanding Data Quality
5

Data Analysis and Statistics

  • Performing Data Analysis
  • Understanding Descriptive Statistics
6

Data Analytics Tools

  • Saving Data in Excel
  • Representing Data
  • Analyzing Data Using Python
  • Identifying SQL Commands in Data Analytics
7

Data Visualization with Reports and Dashboards

  • Visualizing Data Using a Line Chart
  • Visualizing Data Using a Histogram
  • Visualizing Data Using a Bar Chart
8

Data Governance

  • Understanding Laws in Data Governance
9

The Art and Science of Business Statistics

  • Visualizing the Temperature Fluctuations
  • Visualizing Exam Grades Distribution
10

Pictures Tell the Story: Graphical Representations of Data

  • Exploiting a Website Using SQL Injection
  • Calculating the Relative Frequency
  • Figuring the Class Width
  • Calculating the Cumulative Frequency
  • Illustrating a Cumulative Frequency
  • Illustrating a Relative Frequency
  • Illustrating a Frequency Distribution
  • Representing Fluctuations of Gold Price
  • Draw a scatter chart using the calculated XY coo...he given equation y=x-1, where -2 < x < 2.
11

Finding a Happy Medium: Identifying the Center of a Data Set

  • Calculating the Arithmetic Mean
  • Calculating the Geometric Mean
  • Calculating the Weighted Geometric Mean
  • Calculating the Weighted Arithmetic Mean
  • Calculating the Median
  • Representing Positively Skewed Data Set
  • Representing Negatively Skewed Data Set
  • Representing Symmetrical Data Set
12

Searching High and Low: Measuring Variation in a Data Set

  • Finding Sample Variance and Sample Standard Deviation
  • Calculating Population Variance and Standard Deviation
  • Calculating Percentiles
  • Finding Quartiles
  • Finding Coefficient of Variation
13

Measuring How Data Sets Are Related to Each Other

  • Finding Sample Covariance
  • Calculating the Sample Covariance
  • Finding Population Covariance and Correlation Coefficient
14

Probability Theory: Measuring the Likelihood of Events

  • Performing Set Operations
  • Looking at Types of Probabilities
  • Finding Unconditional Probabilities
  • Finding Joint Probabilities
  • Finding the Conditional Probability
  • Calculating the Multiplication Rule
  • Calculating the Complement Rule
15

Probability Distributions and Random Variables

  • Calculating the Probability Distribution
  • Calculating the Expected Value
16

The Binomial, Geometric, and Poisson Distributions

  • Calculating the Binomial Probability
  • Representing the Binomial Distribution
  • Calculating Geometric Probabilities
  • Computing Poisson Probabilities
17

The Uniform and Normal Distributions: So Many Possibilities!

  • Representing the Discrete Distribution
  • Uniform Distribution: Computing Variance and Standard Deviation
  • Calculating the Expected Value
  • Computing Uniform Probabilities with Formulas
18

Sampling Techniques and Distributions

  • Portraying Sampling Distributions Graphically
  • Calculating the Moments a Sampling Distribution
  • Converting Random Variable into a Standard Normal Random Variable
19

Confidence Intervals and the Student’s t-Distribution

  • Calculating the Variance of a t-distribution
20

Testing Hypotheses about the Population Mean

  • Determining the Two-Tailed Hypothesis Test
  • Determining the Test Statistic
21

Testing Hypotheses about Multiple Population Means

  • Calculating the Error Sum of Squares (SSE)
22

Testing Hypotheses about the Population Mean

  • Testing Hypotheses about the Population Variance
23

Simple Regression Analysis

  • Calculating Total Sum of Squares (TSS)
  • Calculating Coefficients and Predicting Sales Revenue in Simple Linear Regression
24

Multiple Regression Analysis: Two or More Independent Variables

25

Forecasting Techniques: Looking into the Future

  • Analyzing User Growth Trends