Statistics for Business Analysis and Data Science

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    Course

  • Methodology

    Online

  • Start date

    Different dates available

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 youThis 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 
Comprehensive 
Practical 
To the point 
Packed with plenty of exercises and resources  
Data-driven 
Introduces you to the statistical scientific lingo 
Teaches you about data visualisation 
Shows you the main pillars of quant researchWhy 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

Facilities

Location

Start date

Online

Start date

Different dates availableEnrolment now open

About this course

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

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This centre's achievements

2021

All courses are up to date

The average rating is higher than 3.7

More than 50 reviews in the last 12 months

This centre has featured on Emagister for 4 years

Subjects

  • Visualisation
  • University
  • Statistics
  • Business Analysis

Course programme

Descriptive Statistics 9 lectures 49:44 Lesson 1: Understanding Population and Sample Data Transcript for this lesson1 Understanding the Difference between A Population and A Sample Slide 1 Before processing any data making decisions 1B we should introduce some key definitions. The first step of every statistical analysis you perform 1C is determine whether the data you are dealing 1d with is a population or a sample 1e a population is the collection of all items of interest to our study and is usually denoted with an 1f upper case N the numbers we've obtained when using a population are called 1g parameters. 1h A sample is a subset of the population and is denoted with 1i a lowercase n and the numbers we've obtained when working with the sample are called 1j statistics. Now you know why the field we are studying is called statistics.2 Let's say we want to perform a survey of the job prospects of the students studying in the New York University 2B what is the population 2C you can simply walk into New York University and find every student right. 2D Well surely that would not be the population of NYU students. 2E The population of interest includes not only the students on campus but also the ones at 2F Home 2G on exchange 2h abroad 2i distant education students 2j part time students 2k even the ones who enroll but are still at high school. 3 Populations are hard to define and hard to observe in real life 3b a sample however is much easier to gather. 3c It is less time consuming 3d and less costly time and resources are the main reasons we prefer drawing samples compared to analyzing an entire population. 4 So let's draw a sample then 4b 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. 4c We can then interview 50 of them. Cool. This is a sample drawn from the population of NYU students. Good job 4d populations are hard to observe and contact. 4e That's why statistical tests are designed to work with incomplete data. 4f And You will almost always be working with sample data and make data driven decisions and inferences based on it. 5 Right since the statistical tests are usually based on sample data samples are key to accurate insights. They have two defining characteristics. 5b Randomness 5c and representativeness. A sample must be both random and representative for an insight to be precise 5d a random sample is collected when each member of the sample is chosen from the population strictly by chance. 5e A representative sample is a subset of the population that accurately reflects the members of the entire population. 6 Let's go back to the sample we just discussed the 50 students from the NYU canteen we walked into the university canteen 6b and violated both conditions. 6c 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. 6d Thus we conclude the sample was not random but was it representative. 6e 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 OK. 7 You must be wondering 7b how to draw a sample that is both random and representative. 7cWell the safest way would be to get access to the student database and contact individuals in a random manner. 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 Visualisation Techniques Skewness Standard Deviation Calculating and Understanding Covariance Correlation Descriptive Statistics. 9 lectures 49:44 Lesson 1: Understanding Population and Sample Data Transcript for this lesson1 Understanding the Difference between A Population and A Sample Slide 1 Before processing any data making decisions 1B we should introduce some key definitions. The first step of every statistical analysis you perform 1C is determine whether the data you are dealing 1d with is a population or a sample 1e a population is the collection of all items of interest to our study and is usually denoted with an 1f upper case N the numbers we've obtained when using a population are called 1g parameters. 1h A sample is a subset of the population and is denoted with 1i a lowercase n and the numbers we've obtained when working with the sample are called 1j statistics. Now you know why the field we are studying is called statistics.2 Let's say we want to perform a survey of the job prospects of the students studying in the New York University 2B what is the population 2C you can simply walk into New York University and find every student right. 2D Well surely that would not be the population of NYU students. 2E The population of interest includes not only the students on campus but also the ones at 2F Home 2G on exchange 2h abroad 2i distant education students 2j part time students 2k even the ones who enroll but are still at high school. 3 Populations are hard to define and hard to observe in real life 3b a sample however is much easier to gather. 3c It is less time consuming 3d and less costly time and resources are the main reasons we prefer drawing samples compared to analyzing an entire population. 4 So let's draw a sample then 4b 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. 4c We can then interview 50 of them. Cool. This is a sample drawn from the population of NYU students. Good job 4d populations are hard to observe and contact. 4e That's why statistical tests are designed to work with incomplete data. 4f And You will almost always be working with sample data and make data driven decisions and inferences based on it. 5 Right since the statistical tests are usually based on sample data samples are key to accurate insights. They have two defining characteristics. 5b Randomness 5c and representativeness. A sample must be both random and representative for an insight to be precise 5d a random sample is collected when each member of the sample is chosen from the population strictly by chance. 5e A representative sample is a subset of the population that accurately reflects the members of the entire population. 6 Let's go back to the sample we just discussed the 50 students from the NYU canteen we walked into the university canteen 6b and violated both conditions. 6c 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. 6d Thus we conclude the sample was not random but was it representative. 6e 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 OK. 7 You must be wondering 7b how to draw a sample that is both random and representative. 7cWell the safest way would be to get access to the student database and contact individuals in a random manner. 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 Visualisation Techniques Skewness Standard Deviation Calculating and Understanding Covariance Correlation Lesson 1: Understanding Population and Sample Data Transcript for this lesson1 Understanding the Difference between A Population and A Sample Slide 1 Before processing any data making decisions 1B we should introduce some key definitions. The first step of every statistical analysis you perform 1C is determine whether the data you are dealing 1d with is a population or a sample 1e a population is the collection of all items of interest to our study and is usually denoted with an 1f upper case N the numbers we've obtained when using a population are called 1g parameters. 1h A sample is a subset of the population and is denoted with 1i a lowercase n and the numbers we've obtained when working with the sample are called 1j statistics. Now you know why the field we are studying is called statistics.2 Let's say we want to perform a survey of the job prospects of the students studying in the New York University 2B what is the population 2C you can simply walk into New York University and find every student right...

Additional information

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

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Statistics for Business Analysis and Data Science

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