Spaces:
Sleeping
Sleeping
File size: 3,336 Bytes
542f510 776f6b3 48e5ab2 776f6b3 48e5ab2 542f510 776f6b3 542f510 fe74648 48e5ab2 fe74648 776f6b3 16a7951 776f6b3 0d5e209 fe74648 776f6b3 16a7951 776f6b3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 |
import streamlit as st
import pandas as pd
import numpy as np
import streamlit as st
st.markdown("<h2 style='text-align: center; color: Black;'>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: Black;'>Key Aspects of Data Science</h2>", unsafe_allow_html=True)
st.markdown(
"<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: Black;'>Skills Required for Data Science</h2>", unsafe_allow_html=True)
st.markdown(
"<p style='font-size: 16px; color: Blue; font-style: italic;'>"
"<b style='color: Black;'>Programming Skills:</b> Proficiency in Python, R, SQL, and other programming languages.<br>"
"<b style='color: Black;'>Mathematics and Statistics:</b> Knowledge of probability, linear algebra, and hypothesis testing.<br>"
"<b style='color: Black;'>Machine Learning:</b> Expertise in supervised and unsupervised learning techniques, including regression, classification, and clustering.<br>"
"<b style='color: Black;'>Data Wrangling and ETL:</b> Skills in extracting, manipulating, and loading data for analysis.<br>"
"<b style='color: Black;'>Visualization Tools:</b> Proficiency in tools like Tableau, Power BI, Matplotlib, and Seaborn."
"</p>",
unsafe_allow_html=True
)
st.markdown("<h2 style='text-align: center; color: Black;'>Applications</h2>", unsafe_allow_html=True)
st.markdown(
"<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."
"<b style='color: Black;'>Real World Examples:</b> Real-world Examples Spotify and Netflix utilise user behaviour and preferences to propose music and films.<br>"
"<b> Forecast demand, optimize supply chains, and improve customer experiences in Retail Industry</b>."
"</p>",
unsafe_allow_html=True
)
|