In this blog, Codeavail experts will explain in detail the basics of statistics. It is one of the important tools to make the art of Data Science (DS).
According to a high-level view, it is the mathematical branch used to perform technical data analysis. A basic visualization can provide you with some high-level data. With the help of this blog, you can perform data specifically.
A basic visualization such as a bar chart can provide you with some high-level data, but with statistics you can work on the data in a much more informative and objective way. Instead of just guessing, math helps us form strong. conclusions of data. In this blog you will get the perfect information about the basic concept of statistics.
By using statistics, we can gain a better and deeper understanding of how data can be formatted exactly and, based on that structure, how we can apply other data science methods to gain even more knowledge.
Similarly, you'll see 3 of the basics of statistics that every data scientist should understand and how these basic statistical concepts can be used in the most effective way.
Some basics of statistics
Table of contents
Statistical Definition
It is one of the essential and strongest mathematical parts. Statistics is the mathematical part that is used to work with the organization, collection, presentation, and schema of data.
In other words, statistics are about achieving some methods on raw information to make it easier to understand.
The Statistics model helps to apply statistics of scientific, industrial and social problems.
Example of statistics
Let's say you've asked to calculate the average weight of 80 students in your class. It is not easy to calculate the average student weight manually. This is where statistics play an essential role. To calculate the average weight of 80 students, you can use the statistics features. With the help of many statistical functions, you can calculate the average weight of the student.
Probability distributions
Probability can be defined as the probability percentage of how many events will happen. In data science, the scale from 0 to 1 is generally calculated, where 0 indicates that we are sure this will not happen and 1 indicates that we are sure it will happen. A probability distribution function describes all the probabilities of possible values in the experiment.
Uniform distribution:
For a better understanding of uniform distribution, let's go back to the example of throwing a dice where possible outcomes are likely to appear than the other.
This type of probability distribution is considered a uniform distribution.
Uniform distribution
Poison distribution:
It is related to Normal Distribution but to an aggregate asymmetry factor. With an asymmetry, less for the Poisson distribution value will have an almost uniform range in all directions just like the Normal distribution.
The asymmetry value is large in magnitude, the range of our data will change in several directions.
distribution of poison
Bernoulli distribution:
The result here has only two possible directions. Two possible outcomes are 0 and 1 respectively. This means saying that a random variable Y can be a failure if it takes the value 0 or success if it takes the value 1. Here the probability of failure and success may not be the same.
Bernoulli distribution
Bernoulli distribution
Bayesian Statistics
For a better understanding of Bayesian statistics, you must first know where frequency statistics fail.
Frequency statistics are a type of statistic that the individual thinks when the word "probability" comes to mind.
Bayes Theorem Formula
Understand the bayes theorem by formula:
Bayes Theorem
Bayes Theorem
P (A/B) previous probability
p (B/A) probability of evidence "B" if the hypothesis "A" is true
P (B/A) subsequent probability of "A" given the evidence
P (B) prior probability that the evidence itself is true
In this equation, probability P (A) is your frequency analysis. The P (B/A) is so likely in this equation. It is essentially the probability that your evidence is accurate, given the data from your frequency analysis.
For example, if you throw the dice 10,000 times, and you get 6 in the first 1000 pitches. P (B) is the probability that the original evidence is correct.
Sampling below and above
Low and oversampling, methods are applied for the problem type.
conclusion :
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Retail, IT, Marketing and Banking businesses are currently in the middle of a wave of Big Data which is quickly becoming harder to not only manage, but also make sense of. These businesses will often have more data than they need to solve a problem, and parsing it into an easy-to-understand and digestible formats have become difficult. This is where data visualization comes in.
Contents
- 1 What is Data Visualization
- 2 Why Visualize
- 3 Data Visualization Examples Made by Vironit
- 4 Free and Open Source Data Visualization Tools
- 5 How do we make a good data visualization?
- 6 How to Build Stronger Customer Relationships Using Data Visualization
- 7 The Future of Data Visualization
- 8 Why Outsource Data Visualization to VironIT
What is Data Visualization
Data visualization is the presentation of quantitative information in a graphical form. In other words, data visualizations turn large and small datasets into visuals that are easier for the human brain to understand and process.
The concept of using pictures to understand data has been around for centuries, from maps and graphs in the 17th century to the invention of the pie chart in the early 1800s. Several decades later, one of the most cited examples of statistical graphics occurred when Charles Minard mapped Napoleon’s invasion of Russia. The map depicted the size of the army as well as the path of Napoleon’s retreat from Moscow – and tied that information to temperature and time scales for a more in-depth understanding of the event.
It’s technology, however, that truly lit the fire under data visualization. Computers made it possible to process large amounts of data at lightning-fast speeds. Today, data visualization has become a rapidly evolving blend of science and art that is certain to change the corporate landscape over the next few years.
Why Visualize
How long do you think your attention span is? According to a recent Microsoft study, we now have an average attention span of 8 seconds – that’s one second less than the average attention span of a goldfish, by the way.
This Goldfish Effect affects all business decisions ever made and those yet to be made. Think of the amount of time you need to concentrate and focus on enough information to gain insight to make the best possible decision.
The other studies show that humans respond to visuals better than any other type of stimulations. The human brain processes visual information 60,000 times faster than text. In fact, visual data accounts for 90 percent of the information transmitted to the brain.
Other reasons to use data visualization include:
According to SAS, data visualization allows also to:
- Identify areas that need attention or improvement
- Clarify which factors influence customer behaviour
- Help you understand which products to place where.
- Predict sales volumes
Data visualization have been used by people for decades to understand and communicate gathered information clearly, quickly, efficiently and easily. Today data collection is exploding due to internet activity and an expanding number of sensors in the smartphones, satellites, cameras, etc., which increases the need for better data using.
However, data visualization has become more than infographics. Data is no longer just something we can view, it is something we can interact with experience.
Data Visualization Examples Made by VironIT
Could data visualization influence the customer’s final decision?
Wrapping your brain around data can be challenging, especially when dealing with huge volumes of information. But data visualizations can make all of that much easier, allowing you to see the concepts that you’re learning about in a more interesting, and often more useful manner.
This in mind, effective visuals can be impactful for other purposes than sales. Below are great data visualizations that could reveal you the answer.
Interactive online 3D Map
This map, created by VironIT, an IT software development company, allows you to edit the basic information about world countries.
Data Analysis Software
Microarray analysis is a high-throughput method that has been widely used to profile gene expression. DNA microarrays are assays for quantifying amounts of mRNA transcripts present in a sample from which RNA is extracted and the mRNA is isolated. The mRNA transcripts quantified and the number of mRNA molecules derived from transcription of a given gene is an approximate estimate of the level of expression of that gene. This platform , designed by VironIT, aims at performing the complete analysis of gene expression microarray data.
Car related crime statistics in Latvia
This following map, created by AutoNams, allows you to see and explore various crime-related data and thus to better understand the risks of having a particular type of a car and to boost sales of anti-theft solutions.The solution combines the map with other charts, enabling search and filtering by different properties.
New Zealand: How Expensive is your School Zone?
How expensive are houses within different school-zones in New Zealand? Explore this viz to find out. The visualization was made with the Tableau.
The Flow of International Trade
This world map is created with tool Blueshift to visualize the trade of global goods, such as food and clothing. The dots stem from one country and flow to others, showing which goods the country specializes in and what other countries buy from them. Clicking on individual countries shows their exports and imports.
Free and Open Source Data Visualization Tools
Data visualization tools can help you turn data into something both powerful and beautiful. Here are the 8 best data viz tools:
How do we make a good data visualization?
While data analysts find the data visualization ideas, and information designers visualise those ideas, Vironit’s development team do the hard work of building an accurate, interactive and flexible digital product.
Where infographics, charts and pics are static, more sophisticated data visualizations allow the audience to interact with the data, manipulating and exploring it in their own way. They’re more likely to stay engaged and more likely to remember what they’ve learned, which is really what makes data visualization so powerful.
To make it all possible our seasoned developers write the high-quality code behind accurate charts, maps and graphs that offer the audience opportunities to play with the data, and, crucially, the possibility for the data set to be modified.
How to Build Stronger Customer Relationships Using Data Visualization
Data visualization can be applied to dozens of situations and industries. Marketing is one of the businesses that strongly depend on interactive data visualization. In order to understand their targets, marketers go through tremendous amounts of consumer data, ranging from psychographic to demographic traits. But this unwieldy data means nothing without insights, context and proper presentation. Data visualizations allow marketers to transform Big Data into convincing and memorable stories that sell concepts and strategies to clients and consumers.
Modern businesses have access to vast amounts of data generated from both inside and outside the organization. Data visualization helps you make sense of it all. It enables you to look at data from a different perspective to discover new answers and insights to:
Tell a Visual Data Story
Go beyond simply presenting numbers and facts. Capture insights as visual stories. Build narratives around your data so that its relevance is clearly communicated and easy to understand.
Here’s a great example that illustrates the growth and expansion of Walmart in the U.S. from 1960 to 2006:
Recognize the Signals from the Noise
A picture really is worth a thousand words. A single graph conveys complex issues far more clearly—and enables users to identify outliers and anomalies much faster.
This example illustrates the varied success of films released between June 2007 and February 2008:
Master a Growing Volume of Data
The digital universe is expected to double in size every two years. But you don’t have to be overwhelmed. By transforming data into simple visuals, it becomes more meaningful to decision-makers. Visual interaction with large datasets simplifies the analytic process, revealing new business insights that can be turned into a competitive advantage.
The Future of Data Visualization
Winter is coming and the future looks dark in the Game of Thrones. In VironIT, a software development company, the future’s so bright you gotta wear shades.
There is no doubt that data visualization has changed the way we present and understand data. Though it is difficult to predict exactly how data visualization will change in the future, experts agree that it will become more prominent, enhance real-time data, and become more interactive – allowing consumers to play around and adapt the data to their situation and needs.
Why Outsource Data Visualization to VironIT
Our company operates 13 years since 2004, with 500+ project behind.
Now we know how to save time and money for our customers. This is true.
If you have a data visualization software project in mind, just drop us a line at [email protected]
Quicken is one of the financial management software developed by Quicken Inc.
This softwares basic functions are very useful for personal and business usage.
Quicken is one of the financial management software developed by Quicken Inc.
This softwares basic functions are very useful for personal and business usage
This course introduces R programming environment as a way to have hands-on experience with Data Science.
It starts with a few basic examples in R before moving onto doing statistical processing.
The course then introduces Machine Learning with techniques such as regression, classification, clustering, and density estimation, in order to solve various data problems.
This course is for beginners, but it helps to have some basic understanding of statistics (mean, median, scatter plot) and preliminary knowledge of any programming.
The course also assumes that you know how to download and install various programs/apps, and you are able to edit and debug simple programs
Writing simple R programs to do basic mathematical and logical operations
