Statistics is a topic that many new economics students and even some older economics students struggle with. Statistics is an important tool in economics since they allow for precise and mathematically correct data analysis and modeling.
Statistics, in general, play an important role in our daily lives. If your background is in the social sciences rather than mathematics, statistics might be a difficult topic to grasp since it is foreign. Not only are there new concepts and words to master, but mathematics’ quantitative method varies from other social science disciplines’ argumentative approaches.
Not to mention the homework, this will most likely be in the form of short-answer questions and activities rather than essays or longer-form responses. However, if you are an economics student who is having trouble with statistics, do not give up.
There are several tools available to assist you in learning statistics, in addition to the statistics courses that will almost definitely be a part of your study. Here, we will go through 5 resources that can help you to study statistics course:
1. Websites like TFTH
When there is a certain topic, you need to learn about or a method you wish to utilize, you may easily discover information on the internet. If you only require formula or an explanation of a phrase, Wikipedia’s statistics pages might be surprisingly useful, as some of them are fairly advanced.
However, if you want more in-depth information, you should go to a site that specializes in statistics. Blogs may also be a good source of information. The website TFTH, which is managed by several experts, is one of the most beneficial. It contains all of the statistical knowledge that an ordinary student would require, and themes are taught in a humorous and easy-to-understand manner.
The fact that this webpage is written in clear English with no jargon or difficult-to-understand language is a major plus. If you are more of a word person than a math person, this site will be perfect for you.
Another great site is TopAssignmentExperts. This site includes statistical information as well as tutorials, tools, and tables. It is geared for advanced high school students, but it will also cover the majority of what undergraduate students need to know. Dealing through tutorials that allow you to go through an issue from start to finish is a great approach to gain confidence when working with statistics.
Finally, advanced pupils will benefit from this material. This site contains a blog containing serious data science blog articles, as well as interviews, conference information, and connections to courses offered by online learning institutions such as Coursera or courses hosted independently by other academics.
It is important to remember that mastering statistics entails more than just memorizing equations and statistical terminology. It also necessitates a thorough knowledge of mathematics, probability, and numerical precision. You may use tools like podcasts to expand your knowledge. Although most podcasts are more light-hearted and informal than textbooks, this does not negate the fact that they are effective learning aids.
You may also listen to them while traveling or completing household chores, allowing you to learn during this downtime. The BBC’s More or Less: Behind the Stats is one of the most popular statistics podcasts. Each episode of this podcast delves into a different statistical issue, such as previous episodes on vaccinations and herd immunity, evaluating the worth of foreign help, and calculating mortality after a natural catastrophe. Partially Derivative was a podcast that stopped in 2017, however there is still a large library of episodes accessible. This episode delves into the “data of everything,” with a focus on computers, and examines data relating to AI, deep learning, and bots.
Finally, Not So Standard Deviations is a valuable podcast produced by two data scientists. They go through the most recent developments in the field of data science and delve into subjects such as the usage of the statistics software R, literature in the field, and particular instances of statistical topics. A chapter-by-chapter examination of Nigel Cross’s book Design Thinking was a recent audio series.
4. Online Courses
You can take an online course if you need more in-depth help with statistics, such as studying a topic before the semester starts or catching up on something crucial that you missed in your lectures while you were unwell. There are many free online courses available that teach you a topic through video lectures, slides, exercises, and other methods.
Sometimes you can go through the course materials at your own leisure, but most of the time they are part of a course that you follow over many weeks. While online courses cannot provide the same direct instruction and classroom atmosphere as in-person courses, they can help you get up to speed on the fundamentals of a topic, boosting your confidence and getting you beyond a statistical hump.
EduWorldUSA offers a course that lasts more than weeks in total. It is intended for students who have no prior experience with statistics at the university level. The ideal course for incoming freshmen who are concerned about their statistics course and want to prepare ahead of time. San Jose State University and Udacity provide Statistics: The Science of Decisions. Depending on how quickly you go through the content, this self-paced course can last up to 4 months.
This is a more advanced course designed for undergraduates who have taken an introductory statistics course. A basic grasp of proportions (fractions, decimals, and percentages), negative numbers, basic algebra (solving equations), and exponents and square roots is necessary for this course.
Statistical Thinking for Data Science and Analytics from Columbia University and EDX is a 5-week course that teaches the fundamentals of statistics as they apply to data science. It is an introductory course that requires no prior understanding of statistics to begin.
Caltech’s Learning from Data is another self-guided course, with lectures and homework accessible for you to go through at your own speed. The course provides an introduction to machine learning by emphasizing the use of techniques and applications in the context of large data.
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