## Introduction

Data science is the study of data, it may be structured or unstructured. It involves understanding, extracting values, and visualizing the data. Various machine learning algorithms and statistical methods are used for this. It’s the hottest topic of the 21st century, and the goal of it is to predict information from existing data. Business intelligence (BI) is to make an analysis and report with data, it’s a subset of data science. Building predictive models helps the market to grow with great acceleration.

The following skills are required to be data scientist:

- Data Mining
- Data Analysis
- Data Visualisation
- Statistics
- Machine learning
- Programming Language

## Data Mining

Data mining is the technique of discovering patterns and extraction of useful information from the data. The other name of data mining is the Knowledge Discovery of Data (KDD). For an accurate model, we require more data.

## Stages of Data Mining

**Data Exploration**

This is the first stage of data mining, it consists of collecting data along with cleaning and transforming according to the need of the problem. It can be done automatically as well as manually. For manual data exploration, queries and script in programming languages can be used.

**Modeling**

Data modeling is to apply the algorithms on the data and the goal is to choose the best data model based on the problem. Different models on the same data set is applied for

choosing the best. Bagging, Boosting and Meta-Learning are some popular techniques

**Deploying Model**

The final stage is the deployment of the model which is the best in the previous stage. It is important because the whole study is based on this. Before deployment, we ensure the model is with the least noise

## Data Analysis

Data analysis is the process of discovering useful results. Mined and cleaned data goes to analytic tools where it finds patterns. In simpler terms, its analysis of past or future data. Data analysts use various techniques for analyzing data it can be done manually as well as automatically. Programming languages and analytic tools like R and Python are used.

## Types of Data Analysis

**Text Analysis**

The analysis which is done on text data is called text analysis.It is a method used for converting data into important information that can be used in multiple industries. Sentimental analysis and lexical analysis are the part of text analysis. Text analysis help us to sort and rank the webpages

**Predictive Analysis**

Predictive analysis is the analysis of the unknown future result. It uses many techniques from machine learning and artificial intelligence. It combines the statistics with computational intelligence and results in the expected future values. Fraud detection and Risk management are some application of the predictive analysis

## Data Visualisation

Data visualization is a technique for visualizing the analyzed data. Large amounts of data are very difficult to understand, that’s why we use data visualization techniques as graphs and charts are easier to understand trends and pattern

## Types of Data Visualisation

- Charts
- Tables
- Graphs
- Maps

There are also many data visualization tools like Qlickviews and FusionCharts

Which helps us to visualize the data without running any program. Manual data visualization can be done by Python and R.

## Statistics

Statistics is the building block of all machine learning algorithms. It helps us get a deep and precise knowledge of data which helps us to study the data. Without statistics, we can’t do machine learning and data science

## Two Categories of Statistics

**Descriptive Statistics**

It provides information/description about the data. Data is categorized and organized based on the given parameter. It can be through the numerical value, table or by graphs

**Inferential Statistics**

It predicts the output based on past data. The methods of inferential statistics are based on the estimation of parameters and testing of hypotheses.

## Machine Learning

Machine learning is a part of data science, the learning is on the data and its by computational machine. Machine learning algorithms are used for classification, regression, and clustering.

## Regression

It is a technique used to predict the dependent variable in a set of independent variables.

**Classification**

It is a technique used for approximating a mapping function (f) from input variables (X) to discrete output variables (y)

**Clustering**

It is a technique for dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group and dissimilar to the data points in other groups

## Programming language

Knowledge of programming language is a must for writing the program to perform the art data science. There are many languages which we can use. Python and R are the most popular and used languages.