Even if you are starting as a data science learner, there is no need to feel overwhelmed by the thought of everything you have to learn. Like with most skills, you will get better with time and practice. Your focus at the beginning should be to lay the groundwork for the rest of your learning. Understanding the basics is a great way to start because you can then understand other complex concepts. These five statistics basics are a great place to start when you are still new to the subject.
1. Descriptive statistics
You can start by getting a good grasp of descriptive statistics. Understanding statistics is at the core of data science because it involves collecting and analyzing tonnes of information. Most of this information comes in statistics, which can be very informative and overwhelming, especially in raw form. Descriptive statistics is the process of summarising data and providing visual ways that make it easier to understand. For instance, statistics can be summarized into mean, median, or mode, otherwise known as finding the central tendency.
2. Probability theory
Probability theory is also at the heart of statistics, and a basic you want to master before moving on to more challenging things. Probability is used with random events, where a result cannot be predicted until the event happens. For example, when one flips a coin, they have to wait until it lands to tell whether it is heads or tails. Probability in data science evaluates what is more likely to happen when an experiment with large amounts of data is repeated several times.
3. Dimensionality reduction
Another important concept you should master is dimensionality reduction of data sets. For a considerable amount of data with many dimensions, it becomes increasingly difficult to analyze different data features in an experiment. You get too many variables, and you have to create a ton of different combinations to cover all possible outcomes. The way statisticians solve this is by reducing the dimensions within a dataset. That way, you are working with fewer factors, and you can conduct more straightforward experiments with more direct results. Also, your data will take up a lot less storage space.
4. Over-sampling and under-sampling
These two techniques are used when datasets are not uniform enough to work with. Because data scientists work with different types of information, not every set will be uniform. For groups with limited data, scientists over-sample to work with a larger dataset. Other times, some of the data overlaps and becomes redundant, so statisticians have to under-sample.
5. Statistical features
This term is used to describe the most straightforward techniques that scientists start with when exploring data. For instance, one can find the highest and lowest value in a set of information. You can also split the data into quartiles and see how much data falls under 50%, for example.
While you focus on the basics statistics, you must remember to stay sharp in the foundational subjects for data science. Do not forget to practice your mathematics, programming, and statistics. Being good at these subjects will make it much easier for you to understand the statistics basics and even create your algorithms from scratch. Even if you have not studies these school courses, you can learn on your own using the numerous resources online. You can also always reach out to the experts on this website if you need homework help with a subject like statistics. That way, you will have some spare time left to practice your data science skills with ProjectPro.
