Course Summary
Learn how to manage, describe, and analyze data in this comprehensive course. Gain skills in data organization, visualization, and statistical analysis with real-world examples and exercises.Key Learning Points
- Learn how to organize and clean data for analysis
- Visualize data using charts, graphs, and other tools
- Apply statistical analysis techniques to draw meaningful conclusions
Job Positions & Salaries of people who have taken this course might have
- Data Analyst
- USA: $62,453
- India: ₹4,70,000
- Spain: €28,000
- Data Scientist
- USA: $113,309
- India: ₹9,77,000
- Spain: €40,000
- Business Intelligence Analyst
- USA: $68,592
- India: ₹6,00,000
- Spain: €33,000
Related Topics for further study
Learning Outcomes
- Ability to organize and manage data effectively
- Skills in data visualization and statistical analysis
- Experience with real-world examples and exercises
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics
- Familiarity with Excel or similar spreadsheet software
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Interactive
Similar Courses
- Data Analysis and Interpretation
- Python Data Analysis
- Data Science Methodology
Related Education Paths
Related Books
Description
In this course, you will learn the basics of understanding the data you have and why correctly classifying data is the first step to making correct decisions. You will describe data both graphically and numerically using descriptive statistics and R software. You will learn four probability distributions commonly used in the analysis of data. You will analyze data sets using the appropriate probability distribution. Finally, you will learn the basics of sampling error, sampling distributions, and errors in decision-making.
Knowledge
- Calculate descriptive statistics and create graphical representations using R software
- Solve problems and make decisions using probability distributions
- Explore the basics of sampling and sampling distributions with respect to statistical inference
- Classify types of data with scales of measurement
Outline
- Data and Measurement
- Welcome to Managing, Describing and Analyzing Data
- Types of Data and Measurement Scales
- Measurement Scales: Nominal and Ordinal
- Measurement Scales: Interval, Ratio and Absolute
- Measurement as a Process, The Big 5 Aspects of Data
- Sampling Concepts
- Working in RStudio
- Attention Learners: R Code / File Resources
- Week 1 Practice Assessment
- Assessment: Data and Measurement
- Describing Data Graphically and Numerically
- Create a Run Chart
- Frequency Distributions
- Frequency Polygons and Histograms
- Histogram Patterns and Density Plots
- Box and Whisker Plots
- Measures of Central Tendency Mean
- Measures of Central Tendency: Median, Mode
- Measures of Position
- Measures of Dispersion
- Measures of Shape
- Measures of Relationship
- Week 2 Practice Assessment
- Assessment: Describing Data Graphically
- Assessment: Describing Data Numerically
- Probability and Probability Distributions
- Introduction to Probability Part 1
- Introduction to Probability Part 2
- Probability Distributions Part 1
- Probability Distributions Part 2
- The Binomial Distribution
- The Poisson Distribution
- The Normal Distribution
- The Exponential Distribution
- Week 3 Practice Assessment
- Probability and Probability Distributions
- Sampling Distributions, Error and Estimation
- Sampling Error
- Random Sampling Distributions
- The Central Theorem
- Probability with RSDs
- Estimates and Estimators
- Confidence Intervals
- Confidence Intervals for the Mean and Variance
- Confidence Intervals for Proportions and Poisson Counts
- Week 4 Practice Assessment
- Sampling Distributions, Error and Estimation
- Two Sample Hypothesis Testing
- Hypothesis Testing
- Significance Level and Risk
- One vs Two Tail
- Type 1 and 2 Error
- Beta and Power
- Calculating Power
- Calculating Sample Size
- Independent vs Dependent Samples
- Two Independent Sample Tests for Means
- Two Dependent Sample Tests for Means
- Two Sample Tests for Variances
- Two Sample Tests for Proportions
- Two Sample Independent Tests for Poisson Counts
- Week 5 Practice Assessment
- Two Sample Hypothesis Testing
Summary of User Reviews
Learn how to manage, describe, and analyze data with this course from Coursera. Users have rated this course highly for its comprehensive coverage of the subject matter and practical approach. While some users found the course challenging, many appreciated the interactive exercises and real-world examples provided.Key Aspect Users Liked About This Course
comprehensive coverage of subject matterPros from User Reviews
- Practical approach to learning
- Interactive exercises
- Real-world examples provided
Cons from User Reviews
- Challenging course material
- Some users found the pace too fast
- Not suitable for beginners
Wendy Martin
4.4 Raiting
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