Statistics for Business Analysis and Data Science
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Is statistics a driving force in the industry you want to enter? Do you want to work as a Marketing Analyst, a Business Intelligence Analyst, a Data Analyst, or a Data Scientist?
Well then, you’ve come to the right place!
Statistics for Business Analysis and Data Science is here for you
This is where you start. And it is the perfect beginning!
In no time, you will acquire the fundamental skills that will enable you to understand complicated statistical analysis directly applicable to real-life situations. We have created a course that is:
- Easy to understand
- To the point
- Packed with plenty of exercises and resources
- Introduces you to the statistical scientific lingo
- Teaches you about data visualisation
- Shows you the main pillars of quant research
Why do you need these skills?
- Salary/Income – careers in the field of data science are some of the most popular in the corporate world today. And, given that most businesses are starting to realize the advantages of working with the data at their disposal, this trend will only continue to grow
- Promotions – If you understand Statistics well, you will be able to back up your business ideas with quantitative evidence, which is an easy path to career growth
- Secure Future – as we said, the demand for people who understand numbers and data, and can interpret it, is growing exponentially; you’ve probably heard of the number of jobs that will be automated soon, right? Well, data science careers are the ones doing the automating, not getting automated
- Growth - this isn’t a boring job. Every day, you will face different challenges that will test your existing skills and require you to learn something new
- Absolutely no experience is required. We will start from the basics and gradually build up your knowledge. Everything is in the course
- A willingness to learn and practice
- Understand the fundamentals of statistics
- Learn how to work with different types of data
- How to plot different types of data
- Calculate the measures of central tendency, asymmetry, and variability
- Calculate correlation and covariance
- Distinguish and work with different types of distributions
- Estimate confidence intervals
- Perform hypothesis testing
- Make data driven decisions
Transcript for this lesson
1 Understanding the Difference between A Population and A Sample
Before processing any data making decisions
we should introduce some key definitions.
The first step of every statistical analysis you perform
is determine whether the data you are dealing
with is a population or a sample
a population is the collection of all items of interest to our study
and is usually denoted with an
upper case N
the numbers we've obtained when using a population are called
A sample is a subset of the population and is denoted with
a lowercase n and the numbers we've obtained
when working with the sample are called
Now you know why the field we are studying is called statistics.
Let's say we want to perform a survey of the job prospects of the students studying in the New York University
what is the population
you can simply walk into New York University and find every student
Well surely that would not be the population of NYU students.
The population of interest includes not only the students on campus but also the ones at
distant education students
part time students
even the ones who enroll but are still at high
Populations are hard to define and hard to observe in real life
a sample however is much easier to gather.
It is less time consuming
and less costly
time and resources are the main reasons we prefer drawing
samples compared to analyzing an entire population.
So let's draw a sample then
as we first wanted to do we can just go to the NYU campus.
and enter the canteen because we know it will be full of people.
We can then interview 50 of them.
This is a sample drawn from the population of NYU students.
populations are hard to observe and contact.
That's why statistical tests are designed to work with incomplete data.
And You will almost always be working with sample data and make data driven decisions and inferences based on it.
Right since the statistical tests are usually based on sample data samples are key to accurate insights.
They have two defining characteristics.
A sample must be both random and representative for an insight to be precise
a random sample is collected when each member of the sample is chosen from the population strictly by chance.
A representative sample is a subset of the population that accurately reflects the members of the entire population.
Let's go back to the sample we just discussed the 50 students from the NYU canteen we walked into the university canteen
and violated both conditions.
People were not chosen by chance.
They were a group of NYU students who were there for lunch.
Most members did not even get the chance to be chosen as they were not in the canteen.
Thus we conclude the sample was not random
but was it representative.
Well it represented a group of people but definitely not all students in the university to be exact.
It represented the people who have lunch at the university canteen.
Had our survey been about job prospects of NYU students who eat in the university canteen we would have done well
You must be wondering
how to draw a sample that is both random and representative.
Well the safest way would be to get access to the student database and contact individuals in a random
However such surveys are almost impossible to conduct without assistance from the university.
All right throughout the course we will explore both sample and population statistics.
After completing this course samples and populations will be a piece of cake for you.
Thanks for watching.
Lesson 2: Various types of Data and Levels of Measurement
Lesson 3: Visualisation Techniques for Categorical and Numerical Variables
Lesson 4: Calculating the Measures of Central Tendency
Lesson 5:Calculating the Measures of Asymmetry
Lesson 6: How to Quantify Variable
Lesson 7: Standard Deviation and Coefficient of Variation
Lesson 8 :Measuring the Relationships between two variables
Lesson 9: Correlation Coefficient
Population vs Sample
Types of Data
Calculating and Understanding Covariance
Lesson 1: Distribution
Lesson 2: Normal Distribution
Lesson 3: Standard Normal Distribution
Lesson 4:: Central Limit Theorem
Lesson 5: Standard Error
Lesson 6: Estimators and Estimates
Lesson 7: Confidence Intervals
Lesson 8:Confidence Interval Clarification with Student's T Distribution
Lesson 9: Confidence Interval Population Variance Unknown
Estimators and Estimates
Student's T Distribution
Lesson 2: Establishing A Rejection Region
Lesson 1: Null and Alternative Hypothesis
Lesson 3: Type 1 and Type 11 Error
Lesson 4: Test for the Mean Population Variance Known
Lesson 5: What is P-Values and Why is it an Important tool in Statistics
Lesson 6: Test for the Mean Population Variance Unknown
Lesson 7: Test for the Mean Dependent Samples
Lesson 8:Test for the Mean Independent Samples Part 1
Lesson 9: Test for the Mean Independent Samples Part 2
Null vs Alternative Hypothesis