pages/Introduction Of Data Analysis.py

import streamlit as st
import numpy as np
import pandas as pd


st.title("**Introduction Of Data Analysis**")
st.subheader(":blue[What is Data?]")
st.write("Data is a collection of information.where we can collect the data through texts(number.alphabate,character),videos,audios.")

st.header("**There are different types of Data**")
st.image("https://d8it4huxumps7.cloudfront.net/uploads/images/65112857711d3_types_of_data.jpg", caption="Types of data")

st.subheader(":blue[**Structured data-**]")
st.write("The data that is already present in a row and column format or which can be easily converted to rows and column so that later it can fit nicely into a Database is known as structured data.**Example**-SQL.")
st.image("https://blog.skyvia.com/wp-content/uploads/2022/04/table-structured-data.png", caption="Structured Data")

st.subheader(":blue[**Unstructured data-**]")
st.write("The data which don't have any formatted structured.They are called as Unstructured data. **Example**-image,texture,audio,video. All are comes under unstructured data.")
st.image("https://www.ashishmathur.com/wp-content/uploads/2020/02/Untitled-1-1024x576.png")

st.subheader(":blue[**Semistructured data-**]")
st.write("Semi-structured- Semi-structured data is a type of data that is not purely structured but also not completely unstructured called semi-structured data.**Example**- CSV file,JSON file etc.")

st.subheader(":blue[What do you mean by Data Analysis?]")
st.write("Data analysis is the process of applyig statistical and logical techniques to describe data. It involves inspecting(view data for verification and debugging purpose),cleaning(removing errors,handling missing data),Transforming(modifying data into a suitable format for analysis) and modeling(appyling statistical models or machine learning alogorithms to identify patterns,relationship or make predictions)data with the goal of discovering useful information,reaching conclusion.")

st.header("**Steps Involved Data Analysis**")

st.subheader(":blue[**1.With Problem Statement**]")
st.write("In first step in data analysis is problem statement means the question asked by a client to data analyst. They have given problem statement we have to do take problem statement, try to understand in depth means we need to understand what is problem statement? what I need to do in problem statement? and what is the major role in this problem statement?")

st.subheader(":blue[**2.Data collection**]")
st.write("in second step we will have to understand our problem statement than start collecting data where this steps will depend upon structured,unstructured and semi-structured data.")

st.subheader(":blue[**3.Simple EDA (Exploratory Data Analysis)**]")
st.write("In this step we are going to perform simple EDA means understand the collected data where this exploration will tell you that your data is having null values,missing value,duplicates,correctly formatted all this information is given by simple EDA where this steps will actually tell you, your data cleaned or not.")

st.subheader(":blue[**4.Pre-processing (Convert row data to clean data)**]")
st.write("In this stage where we are going to convert raw data into a clean data based upon what ever the exploration,insight we got from sources,we have to remove duplicates,if we have null values we have to build another values.and we have to do structured our unstructured format.we need to give correct data types to correct data.")

st.subheader(":blue[**5.EDA (Exploratory Data Analysis)**]")
st.write("This stage we are gonna perform actual analysis on our clean data and this analysis depends upon problem statement.")

st.subheader(":blue[**6.Visualization(visualized insights on dashboard to describe client)**]")
st.write("ones we got insights,the next step is to visualize them on dashboard with the help of chart,graphs,or other visualization tools for best represent insights.")

st.subheader(":blue[**7.Story telling**]")
st.write("In this stage we need to create a storytelling to your client we will have to tell all this steps to client so that they understand what they gave what they got now from their data.")

st.subheader(":blue[**If there are no problem statement given by client**]")
st.write("1.Data collection")
st.write("2.Simple EDA (Exploratory Data Analysis)")
st.write("3.Pre-processing (Convert row data to clean data)")
st.write("4.EDA (Exploratory Data Analysis)")
st.write("5.Visualization(visualized insights on dashboard to describe client)")
st.write("6.Problem Statement")
st.write("7.Story Telling")

st.header("**There are four types of Data Analysis**")
st.image("https://media.geeksforgeeks.org/wp-content/uploads/20230302162400/Data-Analytics-and-its-types.png", caption="Four types of Data Analysis")

st.subheader(":blue[**Descriptive Analysis**]")
st.write("Descriptive analysis tells us what happened. This type of analysis helps describe or summarise quantitative data by presenting statistics. For example, descriptive statistical analysis could show sales distribution across a group of employees and the average sales figure per employee.Descriptive analysis answers the question, “What happened?”")

st.subheader(":blue[**Diagnostic Analysis**]")
st.write("If the descriptive analysis determines the “what,” diagnostic analysis determines the “why.” Let’s say a descriptive analysis shows an unusual influx of patients in a hospital. Drilling into the data might reveal that many of these patients shared symptoms of a particular virus. This diagnostic analysis can help you determine that an infectious agent—the “why”—led to the influx of patients.Diagnostic analysis answers the question, “Why did it happen?”")

st.subheader(":blue[**Predictive Analysis**]")
st.write("Predictive analytics uses data to form projections about the future. Using predictive analysis, you might notice that a given product has had its best sales during September and October each year, leading you to predict a similar high point during the upcoming year.Predictive analysis answers the question, “What might happen in the future?”")

st.subheader(":blue[**Prescriptive Analysis**]")
st.write("Prescriptive analysis takes all the insights gathered from the first three types of analysis and uses them to form recommendations for how a company should act. Using our previous example, this type of analysis might suggest a market plan to build on the success of the high sales months and harness new growth opportunities in the slower months.Prescriptive analysis answers the question, “What should we do about it?”")