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Data Analysis using Python

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About Course

Master the complete data analysis workflow using Python — from importing and cleaning data all the way to deep analysis and stunning visualizations.
This beginner-friendly, hands-on course takes you step-by-step through the essential tools every data analyst needs, using real datasets and practical examples inside Jupyter Notebook.

You will start with the basics of Python and Jupyter, then learn how to import data, clean it, analyze it using statistics and grouping techniques, and finally visualize your insights with different chart types.

By the end of the course, you will have a solid foundation in Python for Data Analysis and be able to manipulate datasets, detect outliers, handle missing values, build correlations, and communicate your findings visually with confidence.


What You Will Learn

  • Set up Jupyter Notebook and understand the workflow of Python analysis.

  • Import, inspect, and prepare datasets from various sources.

  • Perform essential data cleaning:
    statistical measures, outlier detection, and handling missing values.

  • Analyze datasets using groupby, correlation, and descriptive stats.

  • Create professional visualizations:
    column charts, bar charts, line charts, pie charts, histograms, and heatmaps.

  • Build complete analysis projects from start to finish.

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What Will You Learn?

  • By the end of this course, you will be able to:
  • 📌 1. Work Comfortably with Jupyter Notebook
  • Set up Jupyter Notebook correctly
  • Write, run, and manage Python cells
  • Navigate and organize your analysis workflow
  • 📌 2. Import and Inspect Data
  • Load datasets from CSV, Excel, and other formats
  • Understand dataset structure, columns, and data types
  • Identify issues that need cleaning
  • 📌 3. Clean and Prepare Data for Analysis
  • Calculate statistical measures (mean, median, mode)
  • Detect and handle outliers
  • Replace, remove, or impute missing values
  • Prepare “analysis-ready” datasets
  • 📌 4. Analyze Data to Extract Insights
  • Use groupby to summarize and segment data
  • Explore relationships using correlation
  • Perform descriptive statistics to understand patterns
  • 📌 5. Create Professional Data Visualizations
  • Build charts that clearly communicate insights
  • Create column & bar charts
  • Create line charts for trends
  • Create pie & histogram charts for distribution
  • Build a heat map to visualize complex relationships
  • 📌 6. Build a Complete Data Analysis Project
  • Start a new analysis from scratch
  • Clean → Analyze → Visualize a real dataset
  • Present insights in a clear and professional way

Course Content

Introduction

01- Jupyter Basics

02-Importing the data

03-Data cleaning

04-Analyzing the data

05-Data Visualization

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ME
5 days ago
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