Difference between Data Mining and Data Analytics

Data Mining– Data mining is a systematic and sequential process of identifying and discovering hidden patterns and information in a large dataset. It is also known as Knowledge Discovery in Databases. It has been a buzz word since 1990’s


Data Analysis– Data Analysis, on the other hand, is a superset of Data Mining that involves extracting, cleaning, transforming, modeling and visualization of data with an intention to uncover meaningful and useful information that can help in deriving conclusion and take decisions. Data Analysis as a process has been around since the 1960's

Difference of Data Analytics and Machine Learning

Data Analysis

“Data science is to gain actionable insights from data”. The data that we deal with can be historical data or Real-time data. Actionable insights are important, because at the end, every data analytics project is performed with an aim to make a decision making process easier. Anything that helps to make the decision making process easier is an Actionable Insight.

Examples:

 1. Providing a CEO with a comprehensive dashboard to monitor the market performance of a new product, by visualization sales data.

Figure 1: Where Data Analytics located in the cross domains.

2. Predicting the sales of the new product in the next quarter. 

Machine Learning

Machine Learning can be thought of as a collection of techniques which enables computers to perform expertise tasks.

Examples: 

1. Our Previous example, predicting the sales of a product, is one of the most common use-case of Machine Learning termed as predictive modelling.

2. Image Classification - Google photos can identify the objects in a picture and even the occasion in which the picture was taken. Explore Computer Vision to know more.


Similarities There are many task, predictive modelling for instance,that involve both Data Analysis and Machine Learning. Data Analysis is performed to clean, transform the data, and extract the right information from the data (Feature Engineering) to input it to the Machine Learning technique. Machine learning model performs the expert task on the input, in case of predictive modelling to predict a value or classify each input record.

Difference In both the fields there are some cases in which the involvement of the other is minimum. For Instance, our first example of dashboard does not involve Machine Learning, because there no expert task involved.Likewise, our last example of computer vision, have a very minimum involvement of data analytics, and much of Machine Learning (read neural networks).

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What are the usage of Data Analytics?

Usage of Data Analytics


Broadly, predictive analytics can be used to:

1. Description: Provide an overview and summary of the existing state of the world. For example: what is the average age of our customers?How much do they spend, on average, each time they buy? What is the distribution of amounts spent? etc.

2. Comparison: is group A different in some meaningful way from group B, and if so, in what way and by how much? Examples: Do men spend more than women? Does one advertisement work better than others?

3. Clustering / Grouping / Co-occurrence: Group together things that are “similar” according to some definition of “similar”. Example: Are there groups of customers with similar buying/purchase habits? If you know some marketing, cluster analysis is what is used to divide customers into “segments”.

4. Classification: assign a probability that something belongs to 1 of several mutually exclusive classes. Example: Is this credit card trans-action fraudulent? (A: probability Yes/No) Will this person donate to my charity? (A: probability Yes/No) Is this person suffering from a heart attack, or some other mimic condition? (A: probability of Attack)

5. Prediction: predict the most likely value of a continuous variable.Example: what will sales be next quarter? How much will this group of customers spend over the next year? What will be the market share of our new product? 

What are the applications of Data Analytics?

Applications of Data Analyticsˆ 

• Policing/Security
• Transportationˆ
• Fraud and Risk Detection
• Delivery Logistics
• Proper Spendingˆ
• City Planning
• Healthcare
• Internet/web search
• Basket Analysis
• Sales Forecasting
• Inventory Planning

What is Data Analytics? Write down three ways that data analytics is impacting business today.

What is Data Analytics?

Data Analytics mainly helps you to take rapid and better decision based on data.

Data as a collection of facts, observations or other information related to a particular question or problem.

Data can be structured or unstructured. Structured data is information with a high degree of organization that could be included in databases or spreadsheets and is easily searchable by simple search engine algorithms.

Unstructured data is the opposite and is usually text heavy though it may contain video, data or numbers and facts as well. Think of an open field text box that allows you to provide additional comments on a survey.Adding to the complexity Data can also come from a variety of internal and external sources for organizations.

Analytics is the science of examining raw data in order to draw conclusions about the information.

It’s an exciting field, and is dramatically impacting how organizations in many industries are making decisions. The availability of huge volumes of structured and unstructured data sets, combined with advanced computing capabilities. Low cost storage and powerful visualization technology is enabling organizations to gain from market research and social media, to the network of physical objects we call the internet of things. The world we live in today is creating a constant and ever-increasing stream of data. For most organizations, the data they can access is increasing at a rate of 40%each year which creates significant challenges in the way data is captured and secured, organized, analyzed and reported.

 Three ways that data analytics is impacting business today:

Let’s quickly touch on three ways that data analytics is impacting business today.

First, data is enabling new products and services, creating markets that didn’t previously exist and bringing new capabilities to existing markets.Wearables, such as your Fitbit or Apple watch are some examples of new products.

Second, it is disrupting existing markets with innovative upstarts unseating traditionally secure businesses, think of Uber.

Third, data and analytics is driving increased efficiency. For example,retailers have the ability to automate and optimize their supply chain.

In short data is providing the organizations the ability to identify growth opportunities, drive innovation, operate more efficiently, and manage risk in new ways.