Business Statistics 101
What you'll learn
- Students will learn descriptive statistics
- Students will learn how to interpret descriptive statistics
- Students will learn how to find out descriptive statistics using Microsoft Excel
- Students will learn how to analyse descriptive statistics to draw inferences about data
- Students will learn data visualisation
- Students will learn about different plots
- Students will learn how to visualise data on Excel
- Basic Mathematical skills
- Basic Excel Skills
- Eagerness to learn
- A Mac or Windows computer with a functional Excel program running on it
- No programming experience required
In this course I teach descriptive statistics and data visualisation using Excel.
I teach how descriptive statistics can be used to draw inferences about patterns in data. I teach how statistical tools such as mean, median, mode, variance, standard deviation, skewness, kurtosis, standard error, range, outliers, rank, percentile, maximum and minimum values can be used to understand data better.
The statistical tools are taught using Microsoft Excel, and the course is designed in a way that will allow students to understand the nature of data better.
Descriptive Statistics is the foundation for higher level statistical analysis and data analysis, and often people ignore it. But in this course I show how important and insightful descriptive statistics can be. Concepts such as skew, and kurtosis for example are pivotal in understanding the dispersion and distribution of data, and in this course I teach the intuition underlying these measures and their applications.
The following measurements will be taught in the course:
5. Standard Deviation.
6. Standard Error.
Also, in this course, using Excel, I teach how descriptive statistical measures can be obtained easily in the form of statistical reports.
In this course I use real world data to demonstrate concepts, and at the end of the course the students will be provided with a dataset to apply the techniques learnt in Excel.
Further, in this course, I teach about the most commonly used plots for data visualization. I teach how each plot is generated, what it represents and what can be inferred from them.
I, then, show how the plots can be generated with Excel and how data is visualised using Excel.
The following types of plots are taught:
1. Bar Plot
2. Pie Chart.
3. X-Y Scatter Plot
5. Line Plot
6. Box and Whisker Plot.
At the end of the course, you'll be well equipped to visualise data in Excel and interpret the visualised plots.
The course is meant for students who want to learn basic statistics, and understand how basic statistics can be used to draw inferences about the nature of data. The methods taught in the course will allow students to draw the following inferences:
1. Is the data widely dispersed.
2. Does the data have outliers.
3. Is the data skewed?
4. Is the distribution normal?
5. What is the structure of data?
6. What does the statistical measures tell about the dataset?
The course is structured as follows:
1. Activating data analysis toolkits in Excel.
2. Descriptive Statistics measures and concepts, their definition, intuition.
3. Descriptive Statistics in Excel.
4. Different types of plots.
5. Data Visualisation in Excel.
Who this course is for:
- Beginners in Statistics
- People who want to learn how descriptive statistics such as skew, kurtosis etc can be used to draw inferences about data
- People who want to develop business analytics skills
- People who want to learn business intelligence skills
- People who want to learn more about data and data analysis
- People who want to learn about different plots and graphs
- People who want to visualise data using Excel
Wrikki Lahiri is a doctoral student at the City, University of London, and has worked as a data scientist in industry before embarking on his PhD. He has a Master of Science degree in Industrial and Systems Engineering and a Bachelor's degree in Mechanical Engineering. Wrikki's research involves big data mining, entrepreneurial finance, and Behavioural Psychology and Economics. For his research projects Wrikki scrapes, and analyses big data on crowdfunding and social media using machine learning, statistical, and data mining methods. Other research projects Wrikki is working on investigates entrepreneurial habits and motivation and productivity in startup employees. As a doctoral student Wrikki has taught undergraduate statistics and Wrikki also has teaching experience through providing private tuition to undergraduates and high school students in statistics and maths. Wrikki teaches statistics, data mining methods, entrepreneurial psychology and entrepreneurial finance.