Fundamentals of Todays Technologies

Understanding The Basics

1. What is Analytics?

Analytics is the process of discovering, interpreting, and communicating significant patterns in data.

And business analytics defines analytics as a scientific process to discover useful and meaningful data patterns from the given dataset.

It is mostly concerned with converting raw data into meaningful data, that can result in better business insight.

2. What is Business Analytics?

Simply Business analytics is analytics applied to Business Data.

It focuses on the business implications of data and the decisions and actions that should be taken as a result.

Leading companies use analytics to monitor and optimize every aspect of their operations from marketing to supply chain in real-time.

Benefits of Business Analytics :

• Improved efficiency and productivity.
• Faster, more effective decision-making.
• Better financial performance.
• Identification and creation of new revenue streams.
• Improved customer acquisition and retention.

3. What is Data Science?

Data Science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions.

Data Science uses complex ML algorithms to build predictive models.

Data Science Generally has a five-stage life cycle that consists of:-

1. Capture:- Gathering raw structured and unstructured data from all relevant sources.

2. Maintain:- Putting the Raw data into a consistent format for Machine Learning or Deep Learning models. This includes cleansing, data preprocessing, data warehousing.

3. Process:- Examine Biases, patterns, ranges, and distributions of values within the data.

4. Analyze:- Performing Statistical analysis, Predictive analytics, Machine Learning and Deep Learning models.

5. communicate:- Finally, the insights are presented as reports, charts, and other data visualization.

Data Science Uses in Fields:-

1. Healthcare
2. Self-Driving cars.
3. Logistics.
4. Entertainment.
5. Finance.
6. CyberSecurity.

4. What is Big Data?

Big Data is a collection of data that is huge in volume, yet growing exponentially with time.

“Definition of big data is data that contains greater variety, arriving in increasing volumes with more velocity”. This is also known as the Three V’s of Big data.

The 3 V’s of Big Data:-

1. Volume:- You’ll have to process high volumes of low-density, unstructured data. This can be data of unknown values, Ex. twitter data feeds.

2. Velocity:- Velocity is the fast rate at which data is received and acted on.

3. Variety:- It means there are many types of data available such as text, audio, and video.

Big data could be in any format such as structured, unstructured, or semi-structured.

Examples of Big Data:-

1. Stock Exchange:- New York stock exchange is an example that generates about one terabyte of new trade data per day.

2. Social Media:- The statistic shows that 500+ terabytes of new data get ingested into the databases of social media sites every day.

3. Jet Engine:- A single jet engine can generate 10+ terabytes of data in 30 minutes of flight times.

Big Dat Benefits:-

• Big Data makes it possible for you to gain more complete answers because you have more information.

• More complete answers mean more confidence in the data, which means a completely different approach to tackling problems.

5. What is Machine Learning?

Arthur Samuel coined the term Machine Learning in the year 1959.
He was a pioneer in AI and Computer Gaming and defined Machine Learning as-

“Field of study that gives the computers the capability to learn without being explicitly programmed.”

Types of Machine Learning:-

1. Supervised Learning:-
   • Regression Problems.
   • Classification Problems.
2. Unsupervised Learning:-
   • Clustering.
   • Association.
3. Semi-Supervised Learning.
4. Reinforcement Learning.

Some Machine Learning Terminologies:-

1. Model:- Also Known as a “hypothesis”, an ML model is the mathematical representation of a real-world process. An ML algorithm along with the training data builds an ML model.

2. Training:- The learning algorithm finds patterns in the input data and trains the model for expected results.

3. Prediction:- Once the Machine Learning Model is ready, it can be fed with input data to provide predicted output.

4. Target(Label):- The value that the machine learning model has to predict is called the target or label.

Machine Learning Applications:-

• Facial Recognition/Image Recognition
• Automatic Speech Recognition
• Financial Services
• Marketing & sales
• Healthcare
• Recommendation Systems

6. What is the Artificial Intelligence(AI)?

Artificial Intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.

“Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable” — John McCarty-2004

Types of AI:-

1. Weak AI:-

   Also Called Narrow AI or Artificial Narrow Intelligence(ANI) - is AI trained and focused to perform specific tasks.

   Weak AI drives most of the AI that surrounds us today, such as Apple’s Siri, Amazon’s ALEXA, IBM Watson, and Autonomous Vehicles.

2. Strong AI:-

   It is made up of AGI (Artificial General Network) and ASI (Artificial Super Intelligence).

   AGI or general AI is a Theoretical form of AI where a machine would have an intelligence equal to humans, it would have a self-aware consciousness that has the ability to solve problem, learn and plan for the future.

   ASI or superintelligence would surpass the intelligence and ability of the human brain.

   But Strong AI is Entirely theoretical with no practical examples in use today.

   *But AI researchers are busy exploring its development. *

Applications of AI:-

1. Speech Recognition
2. Computer Vision
3. Automated Stock Tracing.

• Thank you for reading the article*

References :-

1. IBM Articles
2. Oracle Articles