Update pages/Intro_DS.py

pages/Intro_DS.py

@@ -1,35 +1,42 @@
 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)
+import streamlit as st
+st.markdown("<h2 style='text-align: center; color: Blue;'>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 decisions. It combines math, statistics, programming, and domain expertise to analyze data and extract meaningful information. "
+    "It is a multidisciplinary field concerned with collecting knowledge and insights from structured and unstructured data using scientific methods, procedures, algorithms, and systems. Here's a detailed look at the key components of data science."
+    "</p>", 
+    unsafe_allow_html=True
+)
+st.markdown("<h2 style='text-align: center; color: Blue;'>Key Aspects of 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>",
+    "<p style='font-size: 16px; color: Blue; font-style: italic;'>"
+    "<b>Data Collection:</b> Gathering information from various sources such as databases, sensors, web scraping, and APIs.<br>"
+    "<b>Data Cleaning:</b> Preparing raw data by correcting errors, filling in missing values, and addressing formatting issues.<br>"
+    "<b>Data Analysis:</b> Using statistical and exploratory tools to uncover patterns and trends in data.<br>"
+    "<b>Data Modeling:</b> Creating predictive or descriptive models using machine learning techniques.<br>"
+    "<b>Data Visualization:</b> Presenting data and insights using charts, graphs, and dashboards in an easy-to-understand format.<br>"
+    "<b>Decision Making:</b> Leveraging insights to solve business problems, optimize processes, or develop new products."
+    "</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("<h2 style='text-align: center; color: Blue;'>Skills Required for 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)
+    "<p style='font-size: 16px; color: Blue; font-style: italic;'>"
+    "<b>Programming Skills:</b> Proficiency in Python, R, SQL, and other programming languages.<br>"
+    "<b>Mathematics and Statistics:</b> Knowledge of probability, linear algebra, and hypothesis testing.<br>"
+    "<b>Machine Learning:</b> Expertise in supervised and unsupervised learning techniques, including regression, classification, and clustering.<br>"
+    "<b>Data Wrangling and ETL:</b> Skills in extracting, manipulating, and loading data for analysis.<br>"
+    "<b>Visualization Tools:</b> Proficiency in tools like Tableau, Power BI, Matplotlib, and Seaborn."
+    "</p>", 
+    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)
-    
+    "<p style='font-size: 16px; color: Blue; font-style: italic;'>"
+    "Data Science can be applied across various industries, including business, healthcare, finance, retail, and social media."
+    "</p>", 
+    unsafe_allow_html=True
+)
+