One does not need a degree in mathematics to pursue data science. It becomes more fun if you have a mathematical background. However, the experience is not hindered in any way, even if you do not. Fret not, because you have the best tips and resources right here to dive in. Statistical analysis is a necessary tool for anyone to become a successful data scientist. Once you go through the concepts one by one, you will begin to realize it is a simple aggregation of various small techniques that are, honestly, really easy to remember. Here is the ultimate list of resources and tips to master statistics specifically for data science

Understand the fundamentals of statistics
Before getting your fluffy white tail dirty by jumping into the rabbit hole that is data science, you must develop a keen understanding of statistics to be able to develop models, visualize data and use appropriate algorithms where needed. And to do all that it is imperative to know the basics, which you can then combine later on. Here is the documentation you will need to study and simply revise the basics.

Learn Python
Yes, to learn statistics for data science you need to know Python. You will spend a lot of time analyzing data sets and segregating schemas. You need to be able to code your way through Python and its libraries so that you can implement statistics in it. That is the end goal. Youtube is a good place to start learning Python.

Take up courses – statistics.com
Whenever in doubt, refer to a syllabus. Retrace your footsteps in any problem and you’ll notice the solution is close at hand. That is the beauty of following a dedicated and detailed syllabus. You can always trust statistics.com to help you get your basics right and enable you to fearlessly pursue data science.

Learn to plot different data charts
Only when you know what type of data you have, you may apply an algorithm or statistical test on it. By being able to visualize datasets correctly, you will be able to recognize the probabilities in which various analyses could be performed. Here is a detailed guide to various data visualization techniques available.

Bayesian thinking & modelling
To be able to predict all possible contingencies of a solution to a problem is a prized quality of a data scientist. And to be able to do that you need to think with a mindset of probabilities. When you visualize a problem, think of different ways it can be achieved. And what better way to do this than using Bayes theorem. It helps you change your thinking in such a manner that you begin seeing entities around you in probabilities. It is an extremely useful application of statistics in data science and you can visit here to learn all about it.

Master correlation and covariance
A simple explanation would be this. When we are comparing data sets from dissimilar populations, covariance is used to determine how much the two random variables vary together. On the other hand, correlation is used to determine whether a change in one variable can result in a change in another. These are concepts of vital importance and play a big role in machine learning. To go through these terms thoroughly, visit this page.

Learn to estimate confidence intervals
There is a lot of jargon going around about what confidence intervals are, but no more. Simply put, a confidence interval is how much uncertainty there is in any particular statistic. Most confidence intervals in today’s date can be found using the tdistribution, especially for small samples. However, there are quite a few practices you should be aware of before you start applying it to data science. Learn all about estimating confidence intervals here.

Everything is just a Regression
Models in statistics, and therefore data science, are an abstraction and simplification of the complexities of the real world. Since they are a simplification they are always false, that does not mean they are useless. They may capture something useful. Regression helps you realize what is useful and what is not. Learn about it here if you would like to avoid some difficult statistical jargon as a beginner.

Recognize all regression techniques, a lifesaver list
Now that you know why it is important to use regression analysis on data sets, it is important to learn all the different techniques, use cases and application of regression. Browse through this page and you will instantly realize how simple it is to switch techniques based on your needs. It is one of the prized capabilities of regression that makes it so useful.

Gain handson experience for applying stats to Python and R
R and Python go hand in hand not only when you begin learning statistics but also when you start applying it in data science problems. It is often required to switch between variables of the coding languages and knowing both of them will certainly put you way ahead than most aspiring data scientists. DataCamp is an excellent place to begin this journey which will enable you to use statistics in R, and by extension in data science.

Learn to calculate the measures of central tendency
It is important to know the centrality of the data you are given to analyze. This means that you need to be familiar with simple techniques for finding mean median and mode for given data. It is simple for a small data set but becomes naturally difficult for larger data sets. Here is how you can learn about it in simple terms.

Familiarize yourself with descriptive statistics
Since you are making predictions all the time based on your data, it is for you to be able to comprehend and describe your data in various forms. Not doing descriptive analysis can make you lose valuable insights. Here is why and how you should proceed to learn about it.

Calculating Variability in statistics
Another important key concept in statistics is Variability. It helps you to see how much spread out a data set is. Knowing the extent of your data set through Variability can enable you to think in more or fewer probabilities. Using Variability can save you time by decreasing the redundant possibilities. Here is how to learn all about it.

Perform hypotheses testing
As the name suggests, hypotheses testing involves you mapping out deduced possibilities and analyze their feasibility on various grounds. Learn to test hypotheses here with correct methods and practices.

Back to books and other resources
We cannot emphasize this enough – read more books. It is important to practice statistics offscreen and on paper to help you remember the key concepts and problems that arise while solving problems. Here is a good recommendation to read up on, which will help you boost your statistics skills and excel in data science.
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