Think about this for a second. If you were to become a data scientist, you would probably be solving problems that could have an impact on so many people's lives. For instance, statisticians who are based particularly in the field of biostatics have been using their knowledge of statistics and testing methods to produce drugs that very well could be saving lives. Other statisticians are busy collecting data to decide what the odds are that a 4oth-year-old man will get a heart attack. Using data such as the man's dietary preferences, genetics, medical history and demographics, they will predict whether the man will get a heart attack at 40th or not. Some statisticians that you know may also be developing software that will enable them to detect fraudulent emails. Has reading all about the work that statisticians do by employing various statistical modelling techniques make you all the more interested in the field of statistics and data science?
If you are still unsure what statistics or data science is all about, perhaps you should be discovering applied statistics meaning.
What are Statistical Modelling Techniques?
If you are already studying statistics or data science, you would know all about this section of statistical learning. If you are still contemplating whether to enter the field of statistics, then here's a little taste of what you can expect. Statistical modelling techniques were developed to emphasize the benefit of models which often show both precisions as well as uncertainty with regards to data that has been collected. The field of statistics equips statisticians with the knowledge of how to build a model that can be used with a variety of statistical techniques in order to produce much more effective solutions to problems. Some of the statistical techniques used for model building solutions include:
Linear Least Squares Regression Model
The linear least-squares regression model is one of the most popular statistical modelling techniques employed by statisticians for the reason that it has become known to be an effective and complete method. Also, it is often used because the best possible results can be achieved with a small amount of data. Many statisticians particularly favour the linear least squares regression model as the theory of the model is rather easily understood. There are some restrictions and parameters that need to be considered when using the linear least squares regression model but then again there are limitations associated with all model types.
Non-Linear Least Squares Regression Model
In terms of parameters and restrictions, the non-linear least squares regression model is a far more desirable choice because there are barely any parameters and limitations. It is also based upon a fairly well-developed theory which makes computing a breeze.
Now that you've been acquainted with some of the statistical models, you need to understand where exactly in statistics these models play a role and make an appearance. Statistics as a field can be classified into two separate branches: descriptive and inferential statistics. The linear least squares regression model and the non-linear least square regression model are part of the branches of statistics known as inferential statistics.
Inferential statistics only began to be implemented during the 19th century but has achieved immense popularity since computer software has been seen to play a role in inferential statistics too. We ought to remember that inferential statistics is the branch of statistics that attempts to develop models that will be able to make assumptions and predictions outside of the data set that has been given. Also as part of the inferential statistics field of study, you will stumble across the term, "regression analysis methodology." So, what is regression analysis methodology? What does regression analysis methodology mean for statisticians and data scientists?
Talking About Regression Analysis Methodology
Perhaps there is a bit of sweat trickling down your forehead. You didn't expect to be confronted with so much jargon related to the field of statistics and data science when you started reading this blog post. While you may not have stepped foot into your first data science or statistics lecture as yet, the truth is you have been using regression methodology as a method to analyze data all your life. You just didn't up until this point know you were using regression analysis methodology.
Every month when you sit down to try to budget for the next month, considering all the variables that might cause you to spend more or save more, you are using a form of regression analysis methodology. If its winter, you will probably use a heater often and hence you will spend so much more on lights. An added electricity expense is now an important variable that you will have to consider when you try forecasting what your expenses for the next month will look like.
A simple task like this where you sort through variables at play and try to decipher which variables will actually have an effect and which variables won't, you are involved in what statisticians would call regression analysis utilizing regression analysis methodology theory. When you try to predict something (dependent variable), you see which factors may impact on that which you are trying to predict (independent variable).
Sounds quite easy, right?
Having stated it in that way, regression analysis seems like something that even the ordinary man on the street can do. Well, there is a mathematical component to regression analysis that makes it a speciality best performed by statisticians. Regression analysis is actually a quantitative research method which involves modelling and analyzing variables. While you are still getting to grips with regression analysis methodology, we would like to let you know how valuable forecasting is in statistics.
Forecasting in Statistics
You may be wondering why people look up to statisticians. Well, statisticians are always trying to generate numbers that relate to future events and even predict future trends. You ought to admit that forecasting or predicting future occurrences that may or may not happen is a big weight to carry on your shoulders.
Statisticians make use of 6 forecasting methods. These include:
- linear regression
- multiple linear regression
- productivity ratios
- time-series analysis
- stochastic analysis
As you can see, statistics is as broad a field of study as any other field of study and there is much that you can learn when you take the plunge and choose to study statistics in order to become a data scientist.
Data Science Analytics
Far too often we hear about the branch of data science called data science analytics but to know what a data science analyst does is never really as easy to explain. A data science analyst is a person who engages himself or herself in descriptive statistics, sees constant illustrations of data as well as communicate the data to others. In order to be a data scientist, you must have knowledge of data science analysis.
How to Become a Data Science Analyst?
If you are looking to be a data science analyst, there are some skills that are a requirement. Firstly you must have knowledge of mathematical statistics. Secondly, you must have knowledge of data wrangling which entails transforming one piece of raw data into another format so it becomes more valuable to be used for problem-solving. You must also have some knowledge of programming software including knowledge of software like R and Python.
If the only Python that you have ever heard of is a venomous snake, then its time for you to further your research on programming environments and software. Where can you actually start? Well, the internet is one of your best resources when it comes to learning anything. You can begin by watching videos about how to get started when using programming software such as Python. The more videos you watch about getting started on software like Python, the easier all software becomes. If you still find no joy in getting started entirely on your own, there is a chance that you could always start with an online short course in data science. Many universities including the University of Cape Town offer short data science with Python courses.
The internet consists of so many tools and options that you can choose to advance your knowledge of data science analytics, forecasting and regression analysis methodology. All you have to do is search for the answers that you require.
If you feel that the internet was not working for you, you can always opt for a statistics tutor. If you are keen on finding a tutor to start your journey towards a possible career as a statistician or data analyst, you can begin by searching through Superprof's community of tutors. You may find a qualified data analyst willing to lend a helping hand or you could even start off by ensuring you build your mathematical foundation first with assistance from a mathematician or maths professor. With over 225 175 top tier mathematics tutors, you are surely bound to find one who will willingly offer you the kickstart that you need towards a well-calculated future.
Can you forecast a bright future as a statistician? Perhaps, you want to advance as a data analyst. Now that you have explored some of the terminology related to statistics, and decided that statistics is surely the field for you, you are well on your way to success as a statistician.