Data science is rapidly capturing the interest of students, emerging as a dynamic and sought-after career path. It is a field that uses and combines other fields to draw insights from data that otherwise might seem complex.
Being a data scientist will give you the opportunity to work in various fields. Let us look at some of the key data analysis methods you will need to not only learn but understand and master to be a successful data scientist.
Hypothesis vs. Null Hypothesis
One of the most important fields when it comes to data science is unsurprisingly statistics and if you want to master data science, you will have to master statistics and know how to apply them accordingly to your data.
One of the most fundamental parts of statistics is hypothesis. In order to be a successful data scientist you will need to understand and master the hypothesis as it is important for making inferences from data.
So what is a hypothesis and a null hypothesis and how are they different?
A hypothesis is where an assumption is made about the potential connections between two or more variables. A good example of a hypothesis is ‘having a tutor improves student’s scores’. In order for you to prove this statement, you will need to test and investigate. According to the researcher of this hypothesis, they believe that having a tutor can improve a student’s scores.
Conversely, a null hypothesis asserts that there is no effect or distinction in the relationship between two or more variables. An example of a null hypothesis is ‘having a tutor does not improve a student’s scores’. In this, you can see that the researchers believe that there is no difference in test scores when a student has a tutor. The assumption would be it is affected by a different factor altogether.
Now that you know what are these two, how do you differentiate them? One of the most obvious and important differences between these two is the testing goals. Scientists aim to provide evidence for a hypothesis while for a null hypothesis, they aim to potentially reject the statement. The very nature of these two are also widely different.
Exploring Multivariate Methods
Another statistical method that all data scientists should know is the multivariate method. This method is usually used to analyse data that has multiple variables simultaneously. These remain as one of the most important methods in data science. The main reason we say this is because the multivariate method of analysis is able to uncover patterns that are not obvious when looking at them in isolation.
Let us look at some of the more common multivariate methods that are used in data science.
Principal Component Analysis
The principle component analysis is widely used because of its ability to simplify complex data. One of the ways this is achieved is by reducing its dimensions while simultaneously identifying patterns that are important.
Multiple Regression
The multiple regression statistical method is very versatile. This is because it is able to help its users understand the relationship between one dependent variable as well as multiple variables. On top of this, this method is able to also predict outcomes which is extremely useful in the field of data science.
Cluster Analysis
Like its name, cluster analysis is a grouping method that organises data that are similar into clusters. On top of this, it is able to analyse data and make it easier for its users to understand which helps tremendously with decision-making.
Multidimensional Scaling
This method is highly useful when it comes to dealing with complex data. This method is used to visualise data in low dimensions while preserving distances. It is commonly used in a lot of fields and this includes data science.
What is Dependence Multivariate Methods?
Another statistical technique that you should learn about is the dependence multivariate method. This is a statistical method that is highly useful when it comes to analysing the relationships between many variables simultaneously.
You might be surprised to know that this method is not only popular in the field of data science but also in fields such as health and even finance.
One of the most common methods that is used in the dependence multivariate method is correlation analysis. This analysis is used to measure the strength between two or more variables.
Similar to what was discussed earlier, multivariate regression is also important and principal component analysis is also used.
What is Interdependence Multivariate Methods?
The interdependence multivariate method is important when it comes to understanding the relationships and interactions. Common techniques that fall under this method are factor analysis and canonical correlation analysis.
When it comes to testing real-world scenarios, these are very important as they help identify patterns that would normally be overlooked. Their real-world application makes them extremely popular in many fields such as marketing and finance
Best Way to Structure Your Analytic Report
Now that we have explained some important statistical methods and techniques that will play a big part in your role as a data scientist, we now look at the final and most important part of why you do tests on your data, to come up with a good structured analytical report.
Although you might conduct your testing properly, if you do not structure your report well, you will not be able to convey your results and insights efficiently and effectively.
We believe there are always nine parts to writing an effective analysis report. Let us look at them closely.
Title
More often than not, people tend to downplay the importance of having a title page. One of the basic things when it comes to a title page is having the title of your report as well as the date and most importantly your name!
Having the proper title page is important as it will be the first impression you will give to the people who read your report.
Summary
It is important to have an executive summary of your report. This is where you will summarise your findings and provide recommendations. It is important to have this part properly worded and concise so as to not appear draggy. You should be straight to the point and highlight any insights as this is mostly what will be used especially for decision making.
Introduction
The introduction section of your analytic report is where you will address the problem or question as well as the objectives of your study. This is where you will give your readers the context, purpose as well as scope of your study.
Data Description
In this section of your report, you will list down the data that you have used for your study as well as all the sources and steps you have taken. This is important to give your readers clarity as well as help them understand your result. This part is extremely important as it gives other researchers the ability to replicate your study.
Methodology
In this part, you will explain the methods that you have used as well as the models you have employed. In order to get your message across easily, we suggest employing visual aids such as graphs, pie charts and diagrams. This will help tremendously when it comes to communicating your analysis in a way that readers can understand.
Results
Next, we come to the results part. In this part. You can show your result together with the key statistical findings. It is important to ensure that the data you present here is easy to understand and interpret.
Discussion
Next, we have the discussion part. This is where you will interpret the findings of your study. You will need to explain properly what are your findings and how they relate to the main objective of your entire report. Make sure your report has a critical perspective as this will help with the credibility of your analysis.
Conclusion
Finally, there is the conclusion part of your report. This is where you will summarise your findings and give key takeaway points to your readers. This will also be the final persuasion to your readers while providing a sense of closure to your report
Reference and Appendix
This is arguably one of the most important parts of your report. Without this part, your entire report loses its credibility as well as its reliability. Always cite your sources properly and list them down under references. Here is where you can provide supplementary data that helps support your report as well.
Data science is a field that is known to be complicated. It can be tough and challenging but it is not impossible to master your studies. One of the best ways you can do this is by hiring a statistics tutor from Superprof. Our tutors are not only qualified and verified but they are also highly skilled in their respective fields. You can rest assured that when it comes to our tutors you are getting the best quality for only a fraction of what you would be paying normally for private tutors. On top of this, most of our tutors also provide their first lesson for free so you can always try out their class to see if they are a good fit for you. Our platform is not only user-friendly but we are sure if you give our tutors a chance, you will be on your way to becoming a highly knowledgeable and successful data scientist.
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