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Statistics & Probability for Business Analytics

Learn descriptive statistics, inferential statistics, probability, correlation analysis, and computational statistics
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Welcome to Statistics & Probability for Business Analytics course. This is a comprehensive business math tutorial designed for data analysts and business analysts. This course will cover basic to intermediate statistics and probability concepts, computational techniques using Python and their practical applications in the field of business analytics. This course is a perfect combination between statistics and Python, making it an ideal opportunity to practice your business analytics skills while improving your statistical knowledge. In the introduction session, you will learn the basic fundamentals of business analytics, statistics and probability applications in this field, and also business analytics workflow. Then, in the next section, we will start the first lesson where you will learn about descriptive statistics. This section will cover mean, median, mode, standard deviation, range, variance, and quartile. These concepts will provide you with the foundational tools to summarize data, identify patterns, and gain meaningful business insights. Afterward, in the second lesson, you will learn about inferential statistics. This section will cover hypothesis testing, confidence interval, regression analysis, and analysis of variance. These methods will help you to make data driven predictions and draw conclusions from sample data. Then, in the third lesson, we will cover the fundamentals of probability. This section will introduce key concepts such as basic probability calculation, joint probability, conditional probability, Bayes’ theorem, and expected value. These probability concepts will assist you to assess the likelihood of various business scenarios and outcomes. Meanwhile, in the fourth lesson, we will learn about probability distribution. This section will cover discrete distributions such as Binomial and Poisson, as well as continuous distributions including Normal, Uniform, and Exponential. Additionally, we will explore their practical applications in business analytics. By learning these concepts, you will be able to model uncertainty, analyze patterns, and apply probability distributions to solve real-world business problems effectively. Then, in the fifth lesson, we will learn about correlation analysis, specifically, we will measure the strength and direction of relationships between variables. At the end of the course, we will apply all statistics and probability concepts that we have learnt to real-world business case studies, where we will use Python to perform data analysis, build statistical models, and calculate probability to predict customer churn.

First of all, before getting into the course, we need to ask ourselves these questions, why should we learn about statistics and probability? Why are they crucial for business analytics? Well, here is my answer, statistics and probability enable businesses to analyze data effectively, identify patterns, and understand trends with greater accuracy. They help in optimizing processes, forecasting future outcomes, and evaluating risks, ensuring that every decision is backed by evidence. By applying these concepts, businesses can enhance efficiency, improve strategies, and make better data driven decisions.

Below are things that you can expect to learn from this course:

  • 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

Statistics & Probability Applications in Business Analytics
Calculating Mean, Median, Mode, Sum, Max, and Min
Calculating Standard Deviation, Variance, and Range
Splitting Data Into Quartiles & Visualising Data with Histogram
Predicting House Prices with Linear Regression
Calculating Joint Probability & Conditional Probability
Calculating Binomial Distribution & Poisson Distribution
Calculating Normal Distribution, Uniform Distribution, Exponential Distribution
Performing Correlation Analysis & Calculating Correlation Coefficient
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
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!
77411
Course details
Video 4 hours
Certificate of Completion

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