Learn descriptive statistics, inferential statistics, probability, correlation analysis, and computational statistics
Sub Category
- Financial Modeling & Analysis
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Objectives
- Learn the basic fundamentals of business analytics, its workflow, statistics and probability applications in this field
- Learn about computational statistics using Python, Pandas, Numpy, Matplotlib, Scipy, Seaborn, and Scikit Learn
- Learn about descriptive statistics and inferential statistics
- Learn how to calculate mean, median, mode, sum, max, and min
- Learn how to calculate standard deviation, variance, and range
- Learn how to split data into quartiles and visualise data using histogram
- Learn how to conduct hypothesis testing & t-test
- Learn how to calculate confidence interval
- Learn how to predict house prices using linear regression
- Learn how to analyze price differences using ANOVA
- Learn how to calculate joint probability and conditional probability
- Learn how to calculate probability using Bayes Theorem
- Learn how to calculate expected value
- Learn about discrete distribution and continuous distribution
- Learn how to calculate binomial distribution and poisson distribution
- Learn how to calculate normal distribution, uniform distribution, and exponential distribution
- Learn how to perform correlation analysis and calculate correlation coefficient
- Learn how to predict customer churn using logistic regression
Pre Requisites
- No previous experience in business analytics is required
- Basic knowledge in statistics
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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Coupon Code(s)