pages/Intro_DS.py

import streamlit as st
import pandas as pd
import numpy as np
st.markdown("<h2 style='text-align: center; color: ;'>What is Data Science</h2>", unsafe_allow_html=True)
st.markdown(
    "<p style='font-size: 16px; color:Blue; font-style: italic'>"
    "Data Science is the practice of using data to acquire insights, solve issues, and make choices. It uses math, statistics, programming, and domain expertise to analyse data and extract meaningful information."
    "Data science is a multidisciplinary subject concerned with collecting knowledge and insights from organised and unstructured data utilising scientific techniques, procedures, algorithms, and systems. Here's a more detailed look at the major components of data science."
    "</p>",
    unsafe_allow_html=True
)
st.markdown("<h2 style='text-align: center; color: ;'>Key Ascepts of Data Science</h2>", unsafe_allow_html=True)
st.markdown(
    "<p style='font-size: 16px; color:Blue; font-style: italic'>"
    "Data Collection: Collecting information from various sources such as databases, sensors, web scraping, and APIs."
    "Data cleaning is the process of preparing raw data by eliminating mistakes, filling in missing numbers, or addressing formatting problems."
    "Data analysis involves using statistical and exploratory tools to uncover patterns and trends in data."
    "Data modelling entails creating predictive or descriptive models using machine learning methods."
    "Data Visualisation: Using charts, graphs, and dashboards to show data and conclusions in an understandable format."
    "Decision Making: Applying insights to address corporate problems, improve procedures, or create new products."
    "</p>",
    unsafe_allow_html=True)
st.markdown("<h2 style='text-align: center; color: ;'>Skills required for DS</h2>", unsafe_allow_html=True)
st.markdown(
    "<p style='font-size: 16px; color:Blue; font-style: italic'>"
    "Skills required include Python, R, SQL, and other programming languages.Mathematics and statistics skills include probability, linear algebra, and hypothesis testing."
    "Machine Learning includes both supervised and unsupervised learning techniques such as regression, classification, and clustering.Data wrangling and ETL include extracting, manipulating, and loading data for analysis."
    "Visualisation tools include Tableau, Power BI, Matplotlib, and Seaborn."
    "</p>",
    unsafe_allow_html=True)
st.markdown("<p style='font-size: 16px; color:Blue; font-style: italic'>"
           Data Science may be applied in a variety of areas or businesses, including business, healthcare, finance, retail, and social media.
         "</p>",
    unsafe_allow_html=True)