Course Provider
What will you learn in this course?
At the end of this course, you should be able to:
- Understand the measures of central tendency and dispersion.
- Be able to explore your data through histograms, box plots, and bar plots.
- Explore pairs of variables using scatterplots and scatterplot matrices.
- Understand the intuition behind and be able to carry out PCA.
- Realize how all these techniques can be sewed together into an analytics pipeline in important business scenariosb.abc
Exploratory Data Analysis
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Skill Type
Emerging Tech
- Domain
Artificial intelligence
- Course Category
Deepskilling Course
- Certificate Earned Joint Co-Branded Participation Certificate
- Nasscom Assessment Available
- Course Covered under GoI Incentive
No
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- Course Price
Free
- Course Duration
5 Hours
- Course Price
Why should you take this course?
At the end of this course, you should be able to:
- Understand the measures of central tendency and dispersion.
- Be able to explore your data through histograms, box plots, and bar plots.
- Explore pairs of variables using scatterplots and scatterplot matrices.
- Understand the intuition behind and be able to carry out PCA.
- Realize how all these techniques can be sewed together into an analytics pipeline in important business scenariosb.abc
Who should take this course?
- BE/ BTech students-any stream
- Non-engineering students-STEM background
- Working Professionals
Curriculum
- This course covers the most widely used techniques for initial data exploration and inference:
- Obtaining the basic summary statistics for each variable in the data
- Visualizing the distributions of each variable
- Visualizing and inferring on the correlation trend between pairs of variables
- Reducing the number of multiple correlated variables to a few independent variables using principal component analysis (PCA)
- Testing hypothesis for one sample mean
- Real-world case study
Tools you will learn in the course
- Central Tendency
- Mean
- Median
- Mode
- Variance
- Standard Deviation
- Correlation, Boxplot
- Scatterplot
- Dimensionality Reduction