pages/RoadMap.py

import streamlit as st

# Custom CSS to change the background color and add 3D arrows
st.markdown("""
    <style>
    .main {
        background-color: #f0f2f6;
    }
    .separator {
        border-top: 3px solid #bbb;
        margin-top: 20px;
        margin-bottom: 20px;
    }
    .content {
        color: #333333;
    }
    .arrow {
        font-size: 24px;
        text-align: center;
        margin-top: -10px;
        margin-bottom: -10px;
    }
    .center-image {
        display: block;
        margin-left: auto;
        margin-right: auto;
        width: 50%;
    }
    </style>
    """, unsafe_allow_html=True)

# Initialize session state
if 'open_expander' not in st.session_state:
    st.session_state.open_expander = None

# Page title
st.title("Data Analysis Roadmap")

# Centered Image
st.image("images/data_analysis.png", use_column_width='always', output_format='PNG')

# Introduction
st.header("Introduction")
st.markdown("<div class='content'>This roadmap is designed for individuals with a basic understanding of Data Analysis. "
         "It outlines the key topics and tools essential for advancing your skills in data analysis.</div>", unsafe_allow_html=True)

# Helper function to add images with 3D arrows
def add_skill_section(title, image_path, description, key):
    if st.session_state.open_expander == key:
        with st.expander(title, expanded=True):
            st.image(image_path, width=100)
            st.markdown(f"<div class='content'>{description}</div>", unsafe_allow_html=True)
            st.session_state.open_expander = key
    else:
        with st.expander(title):
            if st.button("Expand", key=key):
                st.session_state.open_expander = key
                st.experimental_rerun()

    #st.markdown('<div class="arrow">⬇️</div>', unsafe_allow_html=True)

# Excel Section
add_skill_section("Excel", "images/excel_logo.png", """
Excel is a powerful tool for data manipulation and visualization. It's widely used due to its accessibility and robust features.

**Skills:**
- **Data Cleaning**: Removing errors and inconsistencies to ensure data quality.
  - *Example*: Cleaning a sales dataset to remove duplicates.
- **Data Visualization**: Creating charts and graphs to represent data visually.
  - *Example*: Visualizing sales trends over time with line charts.
- **Pivot Tables**: Summarizing data for easy analysis.
  - *Example*: Summarizing sales by region and product.
- **Formulas and Functions**: Automating calculations and data manipulation.
  - *Example*: Using VLOOKUP to combine data from multiple sheets.
- **Data Analysis Toolpak**: Advanced statistical analysis.
  - *Example*: Running regression analysis on marketing data.
""", key="excel")

# SQL Section
add_skill_section("SQL", "images/sql_logo.png", """
SQL is essential for querying and managing databases. It's used to extract, manipulate, and analyze data stored in relational databases.

**Skills:**
- **Basic Queries (SELECT, INSERT, UPDATE, DELETE)**: Retrieving and modifying data.
  - *Example*: Fetching customer information from a database.
- **Joins (INNER, LEFT, RIGHT, FULL)**: Combining data from multiple tables.
  - *Example*: Joining customer and order tables to get complete order details.
- **Aggregations (GROUP BY, HAVING)**: Summarizing data.
  - *Example*: Calculating total sales per region.
- **Subqueries and CTEs**: Writing complex queries.
  - *Example*: Finding customers with orders above a certain amount.
- **Indexing and Optimization**: Improving query performance.
  - *Example*: Adding an index to speed up search queries.
""", key="sql")

# Python Section
add_skill_section("Data Analysis Python", "images/python_logo.png", """
Python is a versatile language used for data analysis, offering powerful libraries for various data-related tasks.

**Skills:**
- **Libraries: pandas, numpy, matplotlib, seaborn**: Essential libraries for data manipulation and visualization.
  - *Example*: Using pandas for data cleaning, matplotlib for plotting sales trends.
- **Data Cleaning and Preparation**: Preparing data for analysis.
  - *Example*: Handling missing values in a dataset.
- **Data Visualization**: Creating detailed and interactive plots.
  - *Example*: Creating scatter plots to visualize relationships between variables.
- **Statistical Analysis**: Performing statistical tests and analyses.
  - *Example*: Running a t-test to compare means of two groups.
- **Automating Data Workflows**: Automating repetitive tasks.
  - *Example*: Writing a script to fetch and process data daily.
""", key="python")

# Data Visualization Tools Section

add_skill_section("Power BI", "images/powerbi_logo.png", """
Power BI is a business analytics tool that provides interactive visualizations and business intelligence capabilities.

**Skills:**
- **Report Creation**: Designing detailed reports.
  - *Example*: Creating financial reports for stakeholders.
- **DAX Functions**: Using Data Analysis Expressions for complex calculations.
  - *Example*: Calculating year-over-year growth.
- **Data Modeling**: Structuring data for efficient analysis.
  - *Example*: Creating a data model to analyze customer behavior.
""", key="powerbi")

# Statistics Section
add_skill_section("Statistics", "images/statistics_logo.png", """
Statistics form the backbone of data analysis, enabling data-driven decision-making.

**Skills:**
- **Descriptive Statistics (Mean, Median, Mode)**: Summarizing data.
  - *Example*: Calculating average customer age.
- **Inferential Statistics (Hypothesis Testing, Confidence Intervals)**: Making predictions and generalizations.
  - *Example*: Testing if a new marketing strategy increases sales.
- **Regression Analysis**: Understanding relationships between variables.
  - *Example*: Analyzing the impact of price changes on sales volume.
- **Probability Theory**: Assessing risk and uncertainty.
  - *Example*: Calculating the likelihood of customer churn.
""", key="statistics")