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import streamlit
import pandas
import numpy
st.markdown("<h1 style='text-align: center; color: Balck;'>DATA SCIENCE INTRODUCTION</h1>", unsafe_allow_html=True)
st.markdown("<h2 style='color: Black;'>What is data science</h2>"
<div class="section">
<h2>What is Data Science? </h2>
<p>
It combines techniques from statistics, computer science, mathematics, and domain expertise to analyze and interpret data effectively.
In essence, data science transforms raw data into actionable insights, driving smarter decisions across industries.
There are some several types of explanations which are given below :
</p>
<ul>
<li>Analyzing customer reviews to identify product sentiment</li>
<li>Techniques Used: Natural Language Processing (NLP), sentiment analysis.</li>
<li>Optimizing delivery routes for logistics companies</li>
<li>Techniques Used: Graph algorithms, optimization techniques </li>
</ul>
</div>
<div class="section">
<h2>Few Important steps in DS </h2>
<p>
Data Science has several steps ,such as :
</p>
<ul>
<li><strong>Understanding the given problem:</strong>First thing is you need to understand the given type of problem</li>
<li><strong>Data Collection:</strong> Gather relevant data from various sources to address the defined problem.</li>
<li><strong>Data Cleaning and Preprocessing: </strong> Prepare the raw data for analysis by handling inconsistencies, missing values, and errors. </li>
<li><strong>Exploratory Data Analysis (EDA):</strong>Gain an initial understanding of the datas main characteristics through visualization and summary statistics.</li>
<li><strong> Deployment: </strong Implement the model into a production environment where it can provide actionable insights or make decisions in real-time.> </li>
</ul>
</div>
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