Data Science, Machine Learning and Data Analysis with Python

||Data science||

Data science is a multidisciplinary blend of data inference, algorithm development, and technology in order to solve analytically complex problems.

At the core is data. Troves of raw information, streaming in and stored in enterprise data warehouses. Much to learn by mining it. Advanced capabilities we can build with it. Data science is ultimately about using this data in creative ways to generate business value

How do data scientists mine out insights? It starts with data exploration. When given a challenging question, data scientists become detectives. They investigate leads and try to understand patterns or characteristics within the data. This requires a big dose of analytical creativity.

Then as needed, data scientists may apply the quantitative technique in order to get a level deeper – e.g. inferential models, segmentation analysis, time series forecasting, synthetic control experiments, etc. The intent is to scientifically piece together a forensic view of what the data is really saying.

This data-driven insight is central to providing strategic guidance. In this sense, data scientists act as consultants, guiding business stakeholders on how to act on findings.

A common personality trait of data scientists is they are deep thinkers with intense intellectual curiosity. Data science is all about being inquisitive – asking new questions, making new discoveries, and learning new things. Ask data scientists most obsessed with their work what drives them in their job, and they will not say "money". The real motivator is being able to use their creativity and ingenuity to solve hard problems and constantly indulge in their curiosity.

Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.

The five stages of the data science life cycle: Capture, (data acquisition, data entry, signal reception, data extraction); Maintain (data warehousing, data cleansing, data staging, data processing, data architecture); Process (data mining, clustering/classification, data modeling, data summarization); Analyze (exploratory/confirmatory, predictive analysis, regression, text mining, qualitative analysis); Communicate (data reporting, data visualization, business intelligence, decision making).

Effective data scientists are able to identify relevant questions, collect data from a multitude of different data sources, organize the information, translate results into solutions, and communicate their findings in a way that positively affects business decisions. These skills are required in almost all industries, causing skilled data scientists to be increasingly valuable to companies.

||Machine Learning||

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

Machine learning algorithms are often categorized as supervised or unsupervised.

Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum.

Here are a few widely publicized examples of machine learning applications you may be familiar with:

• The heavily hyped, self-driving Google car? The essence of machine learning.

• Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life.

• Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.

• Fraud detection? One of the more obvious, important uses in our world today.

Machine learning algorithms find natural patterns in data that generate insight and help you make better decisions and predictions. They are used every day to make critical decisions in medical diagnosis, stock trading, energy load forecasting, and more. For example, media sites rely on machine learning to sift through millions of options to give you song or movie recommendations. Retailers use it to gain insight into their customers’ purchasing behavior.

||Data Analysis||

Data analysis is defined as a process of cleaning, transforming, and modeling data to discover useful information for business decision-making. Whenever we take any decision in our day-to-day life is by thinking about what happened last time or what will happen by choosing that particular decision. This is nothing but analyzing our past or future and making decisions based on it. For that, we gather memories of our past or dreams of our future. So that is nothing but data analysis. Now same thing analyst does for business purposes, is called Data Analysis.

Data analysis tools make it easier for users to process and manipulate data, analyze the relationships and correlations between data sets, and it also helps to identify patterns and trends for interpretation.

There are several types of data analysis techniques that exist based on business and technology. The major types of data analysis are:

• Text Analysis

• Statistical Analysis

• Diagnostic Analysis

• Predictive Analysis

• Prescriptive Analysis

||Python||

What is Python?

Python is a popular programming language. It was created by Guido van Rossum, and released in 1991.

It is used for:

• web development (server-side),

• software development,

• mathematics,

• system scripting.

What can Python do?

• Python can be used on a server to create web applications.

• Python can be used alongside software to create workflows.

• Python can connect to database systems. It can also read and modify files.

• Python can be used to handle big data and perform complex mathematics.

• Python can be used for rapid prototyping, or for production-ready software development.

Why Python?

• Python works on different platforms (Windows, Mac, Linux, Raspberry Pi, etc).

• Python has a simple syntax similar to the English language.

• Python has syntax that allows developers to write programs with fewer lines than some other programming languages.

• Python runs on an interpreter system, meaning that code can be executed as soon as it is written. This means that prototyping can be very quick.

• Python can be treated in a procedural way, an object-orientated way or a functional way.

Good to know

• The most recent major version of Python is Python 3, which we shall be using in this tutorial. However, Python 2, although not being updated with anything other than security updates, is still quite popular.

• In this tutorial, Python will be written in a text editor. It is possible to write Python in an Integrated Development Environment, such as Thonny, Pycharm, Netbeans or Eclipse which are particularly useful when managing larger collections of Python files.

Python Syntax compared to other programming languages

• Python was designed for readability and has some similarities to the English language with influence from mathematics.

• Python uses new lines to complete a command, as opposed to other programming languages that often use semicolons or parentheses.

• Python relies on indentation, using whitespace, to define scope; such as the scope of loops, functions, and classes. Other programming languages often use curly-brackets for this purpose.