Fundamental of Data Science (Basic Intro for Beginners)
Published: 2023-05-11 10:48:16



> I have always been fascinating by data, especially in my role as a software engineer where I design databases and write code to create dynamic websites for various e-commerce purposes. Recently, my interest has shifted towards exploring the field of data science and gaining a deeper understanding of it.So, here I will share what I've learned about data science starting from the very beginning, but I'll try to explain it in simple terms that anyone can understand.
## Data 

Data is just collection of fact.
 Data can be numbers, words, pictures, or any other type of 
 information. We get data from different sources like 
 surveys, machines, or social media.

In data science, data is the information we use to learn and make smart decisions.

## Data Mining 
It is the process of discovering useful patterns and extracting knowledge from datasets ,it may be large datasets.

Basically, there is life cycle of Data Mining and it is the series of steps or stages followed in a typical data mining project.Below are short explanations of its steps. Let me explain it for you:

1.Problem Definition: 
   Clearly define the project's objective and scope.

2.Data Gathering: 
   Collect relevant data from various sources.

3.Data Preprocessing: 
   Clean and prepare the data for analysis.

4.Exploratory Data Analysis:
   Gain insights and understand the dataset.

5.Data Modeling:
   Apply algorithms to extract patterns and knowledge.

6.Model Evaluation:
   Assess the performance and effectiveness of the models.

7.Model Deployment:
   Integrate models into operational systems.

8.Model Maintenance:
  Continuously update and refine the models over time.

## Machine Learning Algorithm
Machine learning algorithms are computational techniques that enable computers to learn from data and make predictions or decisions. Some common types:

1. Classification: 
  Predicting or assigning data to specific categories or classes.

2. Regression:
  Predicting continuous numerical values or estimating relationships between variables.

3. Clustering: 
 Identifying inherent patterns or groupings within data.


But, result have to be evaluated and know Representation(using simple or complex graphs).

## Data Mining Task 
It involve applying various techniques and algorithms to extract valuable patterns, knowledge, or insights from large datasets. 
Here are some common data mining tasks:

1. Anomaly Detection
   It is the identification of unusual patterns.

2. Association Rule Mining
   This method is used for finding interesting association among different entities.

## Machine Learning 
Machine learning is a branch of artificial intelligence where computers learn from data and improve their performance without being explicitly programmed. It involves creating algorithms and models that enable machines to learn and make predictions or decisions based on patterns in the data they receive. In simpler terms, it's a way for computers to learn and get better at tasks on their own by analyzing data.
It can be classified into 3 types.

1.Supervised learning
   It involves training algorithms with labeled data to make predictions or classifications.
       It further  can be classified to two types.
        a) Regression 
           It is used to estimate the relationship between different entities. It associates with dependent variable and independent variable.Example : y=f(x)
        b) Classification 
         It is the process of predicting the class of new variable.It have answer of yes or no.
            
2.Unsupervised learning
  It finds patterns in unlabeled data without predefined outputs.

3.Reinforcement learning
  It teaches agents to maximize rewards by interacting with an environment.