
Explore how statistics powers data science to solve real-world problems across industries, using forecasting, segmentation, and personalized recommendations to boost engagement, retention, and supply chain decisions.
Explore the basics of probability, including zero to one scales, coin toss examples, and conditional probability, then distinguish between discrete and continuous random variables and understand probability distributions.
Implement sampling in SAS using simple random sampling, selecting 100 of 200 customers with a 0.5 probability, and explore 5% rate, seed, and stratification by gender.
This course is intended to give you an overview and detailed walkthrough of the various statistical concepts, data visualization techniques along with its implementation in SAS that are necessary for anyone interested in career of Marketing Analyst, a Business Intelligence Analyst, a Data Analyst, or a Data Scientist.
After this course, you will clearly know the statistical concepts discussed and be able to relate and think of real time use cases of those concepts across different industries.
Also, with implementation of these statistical concepts in SAS programming language, you will get an edge over the industry required skills to implement and visualize the data.
This course will help you to understand and learn:
Statistical analysis using SAS
Use of Statistics In Data Science
Use of Statistics in Decision Making
Descriptive Statistics - concepts and use cases
Central Tendency - Mean, Median and mode
Mean, Median and mode - implementation using SAS
Measures of Variance - Range, Quartiles, Percentiles, Variance and Standard Deviation
Range, Quartiles, Percentiles, Variance and Standard Deviation - Using SAS code
Exploratory Data Analysis
Data Visualization using SAS
Bar charts, Bar and Line charts, Bubble chart and scatter plot implementation in SAS
Inferential Statistics
Statistics use cases in different industries
Probability - Random Variable and Probability Distribution
Sampling - Sampling Techniques and Stratified Sampling
Sampling implementation using SAS
Hope you will get the intended learning out of this course!