modified Roadmap

pages/RoadMap.py

@@ -1,107 +1,140 @@
 import streamlit as st
-from PIL import Image, ImageDraw, ImageFont
-
-# Set page configuration
-st.set_page_config(page_title="Data Analysis Roadmap", layout="centered")
-
-# Title and description
-st.title("Roadmap for Data Analysis with Python")
-st.write("""
-This roadmap guides you through the essential steps and tools for mastering data analysis with Python.
-Each step builds upon the previous one to develop your skills progressively.
-""")
-
-# Define the sequence of topics
-topics = [
-    "Statistics",
-    "Numpy",
-    "Pandas",
-    "Matplotlib",
-    "Seaborn",
-    "Plotly",
-    "Graph Visualization"
-]
-
-# Create a roadmap visualization
-def create_roadmap_image(topics):
-    # Image dimensions
-    width, height = 800, 600
-    # Box dimensions
-    box_width, box_height = 200, 80
-    # Gap between boxes
-    gap = 20
-
-    # Create a blank image with white background
-    img = Image.new("RGB", (width, height), "white")
-    draw = ImageDraw.Draw(img)
-
-    # Load a font
-    try:
-        font = ImageFont.truetype("arial.ttf", 16)
-    except IOError:
-        font = ImageFont.load_default()
-
-    # Calculate starting positions
-    start_x = (width - box_width) // 2
-    start_y = 50
-
-    # Draw boxes with topics
-    for i, topic in enumerate(topics):
-        box_x = start_x
-        box_y = start_y + i * (box_height + gap)
-        draw.rectangle(
-            [box_x, box_y, box_x + box_width, box_y + box_height],
-            outline="black", width=2
-        )
-        text_width, text_height = draw.textbbox((0, 0), topic, font=font)[2:]
-        text_x = box_x + (box_width - text_width) // 2
-        text_y = box_y + (box_height - text_height) // 2
-        draw.text((text_x, text_y), topic, fill="black", font=font)
-
-    return img
-
-# Create and display the roadmap image
-roadmap_image = create_roadmap_image(topics)
-st.image(roadmap_image, caption="Roadmap for Data Analysis with Python", use_column_width=True)
-
-# Provide detailed content for each topic
-st.header("Detailed Roadmap")
-
-st.subheader("1. Statistics")
-st.write("""
-Statistics is the foundation of data analysis. Learn descriptive statistics, probability, distributions, hypothesis testing, and regression analysis.
-""")
-
-st.subheader("2. Numpy")
-st.write("""
-NumPy is the fundamental package for numerical computation in Python. It provides support for arrays, matrices, and many mathematical functions.
-""")
-
-st.subheader("3. Pandas")
-st.write("""
-Pandas is a powerful library for data manipulation and analysis. It provides data structures like DataFrames, which allow you to handle and analyze structured data efficiently.
-""")
-
-st.subheader("4. Matplotlib")
-st.write("""
-Matplotlib is a plotting library that provides tools to create static, animated, and interactive visualizations in Python.
-""")
-
-st.subheader("5. Seaborn")
-st.write("""
-Seaborn is built on top of Matplotlib and provides a high-level interface for drawing attractive and informative statistical graphics.
-""")
-
-st.subheader("6. Plotly")
-st.write("""
-Plotly is a graphing library that makes interactive, publication-quality graphs online. It supports many types of charts and visualizations.
-""")
-
-st.subheader("7. Graph Visualization")
-st.write("""
-Graph visualization involves the representation of data as nodes and edges. Libraries like NetworkX and Graphviz help in visualizing complex networks and relationships.
-""")
-
-# Footer
-st.write("---")
-st.write("Created by [Your Name] - A Roadmap to Becoming a Data Analysis Expert with Python")
+
+# 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")