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This Statistics for Data Science and Business Analysis course will teach everything you want to know about statistics. Whether you want to start a depth learning of descriptive statistics or inferential statistics, this course will teach you. In this Statistics for Data Science and Business Analysis course, you will also come to know more about hypothesis testing and regression analysis. Along with that, this course will help you pick up the fundamental statistics skills that enable you to make analysis based on the real-life situations productively. Are you ready to learn all about statistics? Don’t miss out on trying this course on Udemy.
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This Statistics for Data Science and Business Analysis course is taught by 365 Careers, an all-in-one statistics course for people learning the statistics they need in the office. In this course, you will learn statistics for data science and business analysis, understand all fundamentals of statistics, master advanced statistics skills, and be able to work as a high-paid data analyst, data scientist, marketing analyst or business intelligence analyst. No matter who you want to be, knowing everything about statistics will be very helpful to advance your current careers and become better yourself.
Course target:
No matter who you are, this Statistics for Data Science and Business Analysis course is a good start if you want to know more about statistics skills. Anyone who want to learn statistics for data science or business analysis will love this course. So, if you’re a business analyst, business executive, or individual, this course won’t let you feel disappointed. You will be capable of gradually building up your statistic skills and techniques.
Course requirement:
To start this Statistics for Data Science and Business Analysis course does not require any prior knowledge of statistics. This means whether you know the basics of statistics or not, you can learn the course. To get the most of this course, learners should have a strong will to ace each lecture.
What you will gain from the course:
Course content:
This Statistics for Data Science and Business Analysis course comes with 92 lectures in total, there are 5 hours on-demand video guide, 28 articles, and 98 downloadable resources. Next, let’s detail what each lecture will teach you.
This Statistics for Data Science and Business Analysis course will teach you the difference between population data and sample data. Many professionals fail to determine the difference between a population and sample data. You should be aware of this difference if you perform statistical analysis. Population data and sample data are two different things. Learning the difference will allow you to conclude the target population by assessing sample data obtained from that population. Let’s find out the difference between a population data and sample data:
What is population data?
A population data is obtained from all the members of the target group. This Statistics for Data Science and Business Analysis course will detail what it is. As you can imagine, the population is a much larger group of people used to generalize the outcomes of the study. The sample is just a subset of that entire population. The scope of study decides the exact population. Suppose you want to know whether there is a relationship between job satisfaction and emotional intelligence in Doctors. In this research, the population will include all the doctors working in your country.
You can narrow down the scope of the study by targeting a more specific population. For example, you can include doctors of a particular state in your country. You can further narrow down the population by including doctors from a specific field.
What is the sample data?
Sample data is obtained from individuals participating in the research, you can deeply learning it through the Statistics for Data Science and Business Analysis course. These individuals are chosen from the target group for interviewing. Your survey relies on these people who participate in your research. Suppose there are ten thousand doctors in the target state but you interview only 2000 for your survey, the obtained data from those 2000 doctors is called sample data.
In simple words, a sample is always a subset of the entire population. Most of the surveyors and researchers rely on sample data to conclude their study. Although the sample data does not show the mood of the entire population, it can still reveal the interests of the majority of people in the population.
This Statistics for Data Science and Business Analysis course will teach you the levels of measurement. The levels of measurement describe a relationship between attributes’ values for a variable. Anyone, who wants to learn statistics, must learn the levels of measurement. A variable is described as a quantity which you can measure. Its values can change with time through the population.
Suppose you want to calculate the average salary of citizens in your country, you can ask each citizen and then compute or take sample data to assess the average salary and use this survey to determine the conclusion for the entire population.
The type of statistical analysis you use to obtain the conclusion about the entire population relies on the level of measurement. It is the mathematical nature of the variables. The levels of measurement decide how variables are measured.
Four levels of measurement:
It is ordered into non-numeric categories which are not possible to compare or rank quantitatively. For example, you can organize shoes depending on their type and colours. These two categories have no ordering like “equal to, greater than, less than”. Therefore, you will have to use nominal level variables to organize it.
The ordinal level measurement involves the classification of variables into categories but you can arrange these categories in order. For example, upper class, middle class, and lower class are used to categorize a person depending on his wealth.
All the variables will be classified into organized categories, but these categories will have an equivalent distance between them. So, interval level measurement is used to make a direct comparison between categories.
All the variables will have attributes of nominal, ordinal, and interval variables but there will be a meaningful zero point. That zero-point describes that there are nothing and no arbitrary conclusions. Therefore, you can subtract, add, divide, and multiply the variables.
According to central limit theorem (CLT), the distribution of sample approximates the bell curve or normal distribution as the size of the sample increases, regardless of the shape of the population distribution. This theorem applies when the sample size is more than 30. As you collect more samples, the graph of the sample means is going to look similar to normal distribution.
This theorem also states that the sample means the average will be equal to the population mean. You can sum up the means of all the samples, calculate average, and that average will also be the average of the population mean. To start a deep learning of central limit theorem and normal distribution, this Statistics for Data Science and Business Analysis course will be a superb choice.
What is a normal distribution?
It is the most vital probability distribution in the statistics hence it can be used for several natural phenomena. It is also known as Gaussian Distribution and bell curve. It is a probability function which stats how a variable’s values are being distributed. The normal distribution is a symmetric distribution in which observation clusters will be near the central peak.
A central limit theorem on normal distribution:
Many people got surprised by an amazing feature of the CLT. It states that normal distribution will arise despite the initial distribution. Even though the population has an uneven distribution (it may occur when you are evaluating things like people’s weight, income, etc.), the sampling distribution for your sample with a huge size is going to be normal. That statement astonishes many people.
The central limit theorem is quite useful because the researchers do not know which sampling distribution mean is equivalent to the population mean. The sample mean can cluster together by randomly picking samples from a huge population. It helps the researcher in making flawless estimates on the population mean. The sampling error will reduce as you increase the sample size and that’s what this theorem reveals.
In this Statistics for Data Science and Business Analysis course, you will learn everything you may want to know about regression analysis. Regression analysis is an essential statistical method for business applications. This technique allows you to assess the strength of a relationship between 2 or more variables. You can assess your company’s sales and profits within the past few years and use regression analysis to evaluate the actual link between these variables. It will help you in learning whether or not the relationship is valid.
Besides sales, you can also use regression analysis to assess other factors for your company profits. It is also possible to learn that sales do not explain your profits at all!
Professionals such as analysts, researchers, traders, and portfolio managers can use this technique to assess historical relationships between a variety of financial assets. This method can also help assess risks in a portfolio and develop trading strategies.
Five applications of regression analysis:
There are five basic applications of regression analysis, which are as follows:
Predictive analytics: It is used to forecast future risks and opportunities. Many corporations use this application of regression analysis for their benefits.
This application is used mainly to optimize business procedures. It helps you in eliminating guesswork and relying totally on the number to develop optimum efficiency.
Regression analysis will help you in making well-informed decisions. Even top managers won’t rely on their gut intuition or guesswork to assess whether a decision is beneficial for the firm or not.
People can sometimes make mistakes in judging things. Regression analysis allows you to find those mistakes and correct them as soon as possible.
You can collect a lot of data regarding your target customers, but it will be useless without regression analysis. This technique helps you in looking at the latest information and developing fresh insights.
Regression analysis is an important technique for running a business successfully by taking beneficial decisions. If you are looking to know more about it, this Statistics for Data Science and Business Analysis course will help you.
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This Statistics for Data Science and Business Analysis course is perfect for anyone who are looking to know more about statistics for data science and business analysis. Mastering the high-demand statistics will improve yourself and advance your business skills level. For those people who want to advance their existing analytics skills, this Statistics for Data Science and Business Analysis course will help you learn something new. Are you willing to start a learning here? Don’t forget to redeem a valid Udemy coupon to save BIG.
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