Data Analytics - Full Course

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Course curriculum

1. 1.1 Understanding and Using Data
2. 1.2 Descriptive, Predictive and Prescriptive Analytics
3. 1.3 Data Innovators
4. 1.4 Applications
5. 1.5 Summary
6. Quiz
1. 2.1 Measuring Central Tendency
2. 2.2 Measuring Variation
3. 2.3 Standard Deviation
4. 2.4 Types of Variables
5. 2.5 Summary
6. Quiz
1. 3.1 Making Sense of Numerical Data
2. 3.2 A Picture is Worth a Thousand Words
3. 3.3 Line Charts, Bar Charts and Pie Charts
4. 3.4 Geospatial Charts, Bubble Charts and Dashboards
5. 3.5 Boston Heart Diagnostics
6. 3.6 Summary
7. Quiz
1. 4.1 Downloading Tableau
2. 4.2 Starting with a Spreadsheet
3. 4.3 Bar Charts
4. 4.4 Chart Titles, Labels and Order
5. 4.5 Geospatial Maps
6. 4.6 Dashboards
7. 4.7 Adding Additional Dashboard Charts
8. 4.8 Summary
9. Quiz
1. 5.1 Classes of Distributions
2. 5.2 Special Properties of the Normal Distribution
3. 5.3 Z Distribution and Z Value
4. 5.4 Summary
5. Quiz
1. 6.1 Sampling
2. 6.2 Sampling Techniques
3. 6.3 Process Control
4. 6.4 Covid-19
5. 6.5 Summary
6. Quiz
1. 7.1 The Basics of Hypothesis Testing
2. 7.2 One Sample t Test
3. 7.3 Two Sample t Test
4. 7.4 ANOVA
5. 7.5 Pfizer
6. 7.6 Summary
7. Quiz
8. Calculator
1. 8.1 Assumptions
2. 8.2 One Sample Sign Test
3. 8.3 Mann Whitney Test
4. 8.4 Kruskal-Wallis Test
5. 8.5 Chi Square Test
6. 8.6 Friedman Test
7. 8.7 Summary
8. Quiz
9. Calculators
1. 9.1 Testing Alternative Strategies
2. 9.2 Lyyti.com
3. 9.3 Exact Target
4. 9.4 SixPackAbsExercises.com
5. 9.5 Summary
6. Quiz
1. 10.1 From Sample to Population
2. 10.2 Constructing the Confidence Interval for Means
3. 10.3 Sample Sizes
4. 10.4 Constructing the Confidence Interval for Proportions
5. 10.5 Summary
6. Quiz
1. 11.1 Understanding the Relationship Between Variables
2. 11.2 The Regression Line
3. 11.3 Evaluating the Significance of the Regression Line and the Strength of the Relationship
4. 11.4 Summary
5. Quiz
1. 12.1 Multiple Regression
2. 12.2 Significance
3. 12.3 Residuals
4. 12.4 Multicollinearity
5. 12.5 Prediction Interval
6. 12.6 Summary
7. Quiz
1. 13.1 Datasets, Structure, Supervised, and Unsupervised Models
2. 13.2 Scriptbook
3. 13.3 Summary
4. Quiz
1. 14.1 Predefined Target Categories
2. 14.2 Decision Trees
3. 14.3 Defining Similarity
4. 14.4 Nearest Neighbor Algorithm
5. 14.5 Target
6. 14.6 Netflix
7. 14.7 Summary
8. Quiz
1. 15.1 The Data Does the Talking
2. 15.2 Clustering Algorithm
3. 15.3 Macy's
4. 15.4 Battlefield 2: Bad Company 2
5. 15.5 Dallas Museum of Art
6. 15.6 Summary
7. Quiz
1. 16.1 Finding Relationships Between Entities
2. 16.2 Co-Occurrence Algorithm
3. 16.3 Summary
4. Quiz
1. 17.1 Characteristics of Big Data
2. 17.2 Caesar's Entertainment
3. 17.3 Centers for Disease Control
4. 17.4 Opower
5. 17.5 Summary
6. Quiz
1. 18.1 The Software Technology of Data Analytics
2. 18.2 Understanding the Logical Relationships Among Data
3. 18.3 Why Combining Data into One Table is Not an Alternative
4. 18.4 How is the Problem Solved?
5. 18.5 Combining Tables to Create Reports
6. 18.6 Summary
7. Quiz
1. 19.1 Accessing Data from an SQL Database
2. 19.2 Filtering Data
3. 19.3 Conditional Operators
4. 19.4 Brigham and Women's Hospital
5. 19.5 Summary
6. Quiz
1. 20.1 Downloading r
2. 20.2 Expressions, Variables and Functions
3. 20.3 Vectors
4. 20.4 Descriptive Anaytics
5. 20.5 Graphics
6. 20.6 Regression Correlation
7. 20.7 Summary
8. Quiz
1. Summary and Conclusions

About this course

131 lessons

Data Analytics - Full Course

Course curriculum

1 Data

2 Descriptive Analytics

3 Data Visualization

4 Data Visualization with Tableau

5 Probability Distributions

6 Sampling and Process Control

7 Hypothesis Testing

8 Nonparametric Hypothesis Tests

9 A/B Testing

10 Confidence Interval Estimation

11 Simple Linear Regression

12 Multiple Linear Regression

13 Data Mining

14 Classification

15 Clustering

16 Co-Occurrence

17 Big Data

18 Database Technology

19 SQL

20 Statistical Analysis Using r

21 Summary and Conclusions

About this course