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During our school days statistics used to be the simplest chapter in mathematics, we might have procrastinated it to the end because they are easy to learn. Nobody knew statistics plays a monumental role in data science and learning it would change their lives forever. In this article let’s see what is the role of statistics in data science.
A Brief Note On Statistics
For academic purposes, statistics might be some kind of simple topic which be might bring you some bonus scores, but from a professional point of view, statistics is a powerful tool used for the collection, analysis, and interpretation of data. A data analyst uses statistical tools, and computer algorithms to find trends and patterns in the data, the purpose is to add value to business organizations.
There are mainly two types of statistics are Descriptive type and Inferential type.
Descriptive statistics are used to describe datasets, using numerical and graphical methods to discover patterns in the data, summarize relevant information and present it to the concerned authorities, so that they can improve decision making. On the other hand, inferential statistics use data samples from datasets to make estimates, decisions, forecasts, and other generalizations.
In short descriptive statistics show what the data is while inferential statistics are used to reach conclusions and draw inferences from the data.
Importance Of Statistics
Statistics is a vital field of study, or even fundamental, for successful Data Mining and the appropriate adequacy of your project. A considerable lot of the exercises of these undertakings are upheld and worked with by measurable strategies and investigation. This is significant support for Data Science.
As per CRISP-DM Data Mining is organized through a hierarchy, composed of sets of tasks divided by four levels of abstraction, being: phases, generic tasks, specialized tasks, and process instances. A short note on CRISP-DM, which stands for Cross Industry Standard Process For Data Mining, is a methodology that provides a standard process model, which provides a framework to help execute DM projects, regardless of the industry and technology used.
Statistics For Data Exploration
One of the first steps defined by CRISP-DM is Understanding Data, which includes the process of exploring data. Exploring data includes finding trends and patterns inside the data, exploration of these aspects requires analysis that resorts to the human ability to understand data, using the experiences that he gained from previous projects. But the majority of these insights can only be unveiled using methods of statistics, these statistics techniques allow to gather and summarize a high number of data characteristics, highlighting the main and most influential data.
Statistics For Data Preparation
The next data preparation phase also takes advantage of statistical concepts to do tasks such as cleaning, constructing, and evaluating data, which includes evaluating results as well. Statistics eradicate unwanted information and catalog useful data in an effortless way.
Statistics For Prediction
Making predictions from the data available are the final goal of any data scientist, statistics help to do better predictions and would allow the data scientist to categorize the data according to the usage by clients.
Statistics For Data Presentation
Data visualization is a very important step in data analysis, it will help the data scientist to have a better look over the data available. Various data representation methods like bar charts, line charts,s and other tools like moving averages are also made use by the data scientist to interpret data.
Statistical Methods For Descriptive Statistics
When it comes to descriptive statistics some central parameters play a monumental role, these central parameters are Mean, Median, Mode, Standard deviation. etc You remember these terms from your high school right? along with this relatively new concept called Skweness is also used in descriptive statistics
- Mean – It is calculated by taking the sum of all the values that are present in the dataset and dividing that by the number of values in the data.
- Median – It is the middle value in the dataset that gets in order of magnitude. It is considered over mean as it is least influenced by outliers and skewness of the data.
- Mode – It is the most occurring value in the dataset.
- Standard Deviation- Is a measure of the amount of variation or dispersion of a set of values
- Skewness– Refers to a distortion or asymmetry that deviates from the symmetrical bell curve, or normal distribution, in a set of data.
Statistical Methods For Inferential Statistics
When compared to descriptive statistics the concept of inferential statistics seems to be new, so is the tools used. The most frequently used inferential statistics tools are hypothesis tests, confidence analysis, and regression analysis.
- Hypothesis tests– This make use of sample data to answer some questions like is the mean greater than or less than a particular value?
- Confidence intervals-It incorporates the uncertainty and sample error to create a range of values in which the actual population value is like to fall within.
- Regression Analysis-It describes the relationship between the independent variables and dependent variables.
Where To Learn Statistics For Data Science
Online Courses To Learn Statistics For Data Science
This course is offered by Stanford University and is expected to have a very high density of knowledge. The course instructor is Guenther Walther, who Is a professor of statistics from Stanford University. This course serves all the statistical knowledge required for your data science career.
But the problem with the course is, since this course is offered by a university, not like IBM or Google, we might not get a practical insight into the application of the knowledge, in this case, the application of statistics for data science. This can only be earned from experience and practice, thus we can conclude that this course is more like a theoretical type of course.
This is a specialization containing three courses namely: Understanding and visualizing data with python, Inferential statistical analysis with python, and Fitting statistical models to data with python. As these names indicate this specialization primarily focuses on the application of statistics using python for data science.
This specialization is enriched with hands-on projects, thus we can conclude that this course is more practice-oriented.
This course is offered by IBM since they are one of the industry leaders it is guaranteed that the course will be rich with adequate information that is required for the industry. This course will take you from noob level to industry level statistician with all the relevant information required for data science.
All the above-mentioned courses are available on Coursera, enrolment is free but for certification, payment has to be done.
E-Books To Learn Statistics For Data Science
This covers all the main topics like Data structure, Descriptive Statistics, Probability, and Machine Learning, and is suitable for complete beginners. It is filled with a lot of practical coded examples, written in R, gives very clear explanations for any statistical terms used, and also links out to other resources for further reading.
This book covers little it broader areas like statistical thinking, hypothesis testing, distributions, and correlations even though it is suitable for absolute beginners. Also contains lots of code examples written in python. It is aimed heavily at programmers and relies on using that skill to understand the key statistical concepts introduced.
The main focus areas of this book are Regression, Distribution, Factor analysis, Probability. But this book is not really suitable for beginners, non-statisticians with experience in python or R could buy this book. The book was initially composed for understudies considering a non-math-based course where comprehension of measurements is required, like the sociologies. It, consequently, covers sufficient hypotheses to comprehend the strategies yet doesn’t accept a current numerical foundation. It is, in this manner, an optimal book to peruse in case you are coming into information science without a math-based degree.
Yeah I know books are costly but it is worth it, and some of them are also available for free. So before jumping into a buying decision do check for free E-Books of the before-mentioned books. I have listed only a few resources it worth doing your own independent research in this domain.
That’s it, here comes the end of the article. We have explained statistics, the need for statistics in data science, different methods of statistics used in data science, and finally different online sources from which you can learn statistics for data science. As a final note for the conclusion, I would like to mention that statistics is the root of data science. It is through statistics that data science feeds significant insights.
That’s all for now, as always if you like the article share it with your friends and family, and all the best for your learning journey.