Statistics is a crucial part of our everyday life; it helps us uncover new things and get assistance on many issues. Moreover, it is the basis of many scientific breakthroughs; hence it is a branch of mathematics that should be taken seriously. In this article, we shall answer questions such as what exactly is statistics? How do statistics correlate with ML (machine learning)? To get answers to such questions, follow along as we discuss more on this exciting topic.
Data statistics help us in so many situations; however, it could lead to drastic adverse effects if manipulated wrongly. Hence we have prepared this article to provide an insight on how to learn statistics for machine learning.
Now let us look at learning tips to help you in your quest.
What statistics do
Statistics provide meaning to data. It is the act of making raw data have meaning.
It is of two categories:
- Inferential: provides ways to go through experiments performed on a small scale.
- Descriptive: used to convert raw data into information
How statistics correlate to machine learning
Machine learning primarily uses information that is got from statistics to evaluate, interpret and select predictive models.
Machine learning has statistics as its base for better functionality. I mean, you do not expect to solve any real-world issue with raw data to work with. Instead, you require information, and that is where statistics skills come in.
Many students find learning statistics challenging due to all the equations, concepts, and Greek notations. However, once you change your perspective focus on learning statistics, you will find it easy to master statistics.
Why should I study statistics?
Statistics plays a significant role in many organizations, from calculating profits and losses, learning future impacts on a change made today. However, to achieve such complex analysis, you require to have more knowledge of statistics.
Machine learning and statistics projects
Once you commence an ML project, you had better be ready to apply statistics concepts; they go hand in hand. Here is how:
- It defines the problem statement
Finding the actual problem to solve is one hectic thing in terms of machine learning. Statistics gives your project purpose in society; without it, your machine is just a composition of thousands of lines of code.
Statistics help you define the problem statement with ease. Using raw data collected from society, you can be able to come up with an objective for your program.
However, the solution will not always be direct; sometimes, you will need to perform data mining and exploratory analysis of data.
- Data exploration
To understand data better, you must have an in-depth understanding of values and relationships between them.
- Data cleaning
Once an experiment is carried out, you are left with a list of disorganized values that have no use in their initial state. You hence use statistics to “clean” the data and create a record of information that has meaning.
You also get to deal with data corruption, missing values, and data errors.
Crucial statistics concepts
Statistics involves a couple of concepts that are pretty crucial in its functionality, include:
- Getting started
- Statistics distribution
- Data distribution and sampling
- Statistical experiments
- Nonparametric methods of statistics
Learning tips for statistics
The incorporation of statistics can be divided into two, namely:
- Top-down – whereby you start by understanding the question and then solving it using statistical methods.
- Bottom-up – you start by learning the theoretical part first, then you implement it later.
If you find it hard to solve any statistical question, you can always look for help online. For example, if you are a student, you should use the hundreds of online statistics assignment help resources available. They make learning less of a hassle and more fun.
Learning statistics is not an easy feat; however, it is not an impossible one. Using the tips provided in this article, you can learn statistics quickly and incorporate it into machine learning to produce more effective programs.