We know that from the very early beginnings of time people have been invested in collecting data, sifting through data and then using the data that they have collected to make predictions and forecast what might be a possibility in the future.
So while statistics have been employed since the very early beginnings of time, why is it that so many more people are becoming interested in the field of statistics and data science? Why is it that every second person has become obsessed almost with statistical modelling and regression analysis?
How Important is Statistics for Data Science?
Getting to Know All About the Background of Statistics
While data science and statistics have only recently begun to flourish, it ought to be said that recording and analysing trends have been techniques employed by people from long ago. Both statistics and probability are seen as being topics that are commonly covered in mathematics. Probability is believed to have originated from gambling. In fact, French mathematicians like Pierre de Fermat and Blaise Pascal in 1654 used a game of chance to further their understanding of probability. While the field of statistics is seen as having originated particularly from census counts in the 19th century, statistics became all the more valued particularly when population studies took the forefront. However, statistics actually became most popular in 1662 when William Petty and John Graunt developed the human statistical and census method. So while statistics may seem to be a recent invention, you must remember its been around for quite a while.
Well, if we have learnt anything from population studies that may have cause statistics to become all the more popular its that statistics is all about coming up with as well as studying methods specifically used for collecting, analyzing and interpreting data. To step it up a notch, statistics can be seen as synonymous to mathematics and computer programming tools which can be used for the process of making sense of a vast amount of data. In order to get a better understanding of what can be expected from statistics, we can say that statistics allows us to see that in any problem and within any situation, you can expect uncertainty. Uncertainty is a word found in almost any scientific problem and the idea of variation and uncertainty lies at the heart of statistics.
Studying Statistics at an Undergraduate Level
Studying statistics at a university undergraduate level will require that you have a strong mathematical foundation. Along with a strong mathematical foundation, there are also other subject disciplines that are interlinked with the field of statistics including economics, sociology, accounting and finance. So at an undergraduate level when studying statistics, you will learn everything that will serve as the basis of a data science degree. At an undergraduate level, you will learn:
- Probability and introductory statistical method
- Experimental design
- Computational inference
- Sampling and databases
- Environmental statistics and financial statistics
Once you have studied a bachelors degree in statistics for 3 or 4 years, you will be able to complete your honours degree and thereafter a Masters degree in data analysis. You must remember that you need to score over 75% in your undergraduate and honours degree before you can succeed in pursuing your master's degree in Data Science.
If you do feel that you need to read more about statistics and what statistics mean, look at this article: What is Applied Statistics?
All You Need to Know About Regression Analysis
So you are seriously contemplating completing a bachelors degree in statistics. Well done, you have chosen a career path which is currently trending. With more and more jobs becoming available on Indeed and Gumtree, let's dive a step further and view all the concepts that will be brought to the fore when you study statistics. During your first year of studying statistics, you are bound to be introduced to the term, "Regression analysis." These two words will slide off your lecturer's tongue all too often during your years of study.
Regression analysis simply put is one type of statistical technique employed to see the relationship between dependent and independent variables. Regression analysis is either used to model a set of data or even to analyze a data set. The reason why so many researchers employee regression analysis is because this statistical method is used often to predict or forecast a particular outcome. When you learn all about regression analysis at university, you come to understand that there are many regression analysis techniques such as:
- Linear regression analysis
- Logistic regression analysis
- Polynomial regression analysis
- Stepwise regression analysis
- Ridge regression analysis
- Lasso regression analysis
So you have managed to understand what regression analysis theory is. While this is just the tip of the iceberg as to what you can expect to learn about when you choose to study applied statistics at a university level, it is very important to know it. Once you have managed to master the statistical technique of regression analysis and you have successfully completed your degree, you most certainly can choose to advance your degree by choosing to do a masters degree in applied data science.
Preparing for a Degree in Applied Data Science
While you may have your head set on completing and pursuing your master's degree in Applied Data Science, this is where we tell you that there is a way to mentally prepare yourself for the demands of a masters degree in data science. Pursuing a Master's degree in Applied Data Science does require a lot of commitment and hence we always suggest taking baby steps first before you dive head first into the master's course.
Applied Data Science Course
The good news is that you can start to study for applied data science without paying a deposit. There is also a way of knowing what to expect before you actually start studying. Coursera offers a free Applied Data Science Specialization course particularly for people interested in advancing in the data science course. This particular course allows learners to focus on:
- Understanding the fundamentals of Python
- Gain practical skills in terms of using the Python software for data analysis
- Learning to communicate data insights and trends
- Producing and creating a project that showcases applied data science techniques and tools
It's always highly recommended to start advancing your knowledge of a certain field by trying a free course first. This way you can decide if you really enjoyed the course and get a feel of if you actually want to continue studying in the field. It also allows you to see what your weaknesses and strengths are before you actually pursue the degree.
Finding a Data Science Tutor
If you are finding the free Data Science course overwhelming, you know that you can always get some help in the form of a data science tutor. If you are all in favour of one-on-one tuition be sure to check out the Superprof site. If it is just Programming languages that you are struggling with, you can simply opt for programming language lessons in South Africa. You will be thrilled to know that from as little as R200/h you can even get tutored by a professional data science graduate. In that way, you will certainly get all the concepts that you need to know covered. Everything from regression analysis to forecasting in statistics can be taught to you by one of our qualified tutors who are all too eager to help.
If you are all the more intrigued to find out exactly which concepts are covered in the field of data science, be sure that you are getting to grips with statistical modelling techniques.
Masters Degree in Data Science (MSc Data Science)
When you reach the point where you are confident that pursuing the masters in data science is the right path for you, do it. You can complete the master's degree in Data Science full time over a year and while a year seems relatively short, you have to remember that you will be fully immersing yourself in the field of study. There are elective modules that you have to choose from but for the most part, the compulsory modules include:
- Research report: Data science
- Statistical foundations of data science
- Computational Intelligence
- Mathematical foundations of Data Science
- Large Scale Computing Systems and Scientific Programming
- Research Methods and Capstone Project in Data Science
- Adaptive Computation and Machine Learning
While your compulsory modules may still sound like Greek entirely at this point in time, once you enrol in the Master's programme, all these compulsory modules will become second nature.
Remember, becoming a qualified data scientist is hard work but it is surely rewarding in the end.