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Data Analysis with Python

Curriculum

  • 6 Sections
  • 22 Lessons
  • 8 Weeks
Expand all sectionsCollapse all sections
  • Module 1: Introduction to Python
    3
    • 1.1
      Introduction to Python and its ecosystem
    • 1.2
      Setting up Python environment (Anaconda, Jupyter Notebook)
    • 1.3
      Basic syntax, data types, and variables
  • Module 2: Python Programming Foundations
    4
    • 2.1
      Conditional statements (if, else, elif) and loops (for, while)
    • 2.2
      Basic data structures (lists, dictionaries, sets, tuples)
    • 2.3
      Writing simple functions, understanding built-in functions
    • 2.4
      Introduction to modules and packages
  • Module 3: Intermediate Python
    2
    • 3.1
      Advanced data structures and operations with lists and dictionaries
    • 3.1
      Introduction to NumPy for numerical data
  • Module 4: Data Manipulation with Python
    3
    • 5.1
      Introduction to Pandas for data manipulation
    • 5.2
      Series and DataFrame structures in Pandas
    • 5.3
      Importing and exporting data (CSV, Excel)
  • Module 5: Data Analysis and Visualization with Python
    5
    • 6.1
      Data cleaning techniques including handling missing data and data transformation
    • 6.2
      Introduction to data visualization with Matplotlib and Seaborn
    • 6.3
      Creating plots, histograms, scatter plots, and more
    • 6.4
      Descriptive statistics with Python
    • 6.5
      Introduction to simple machine learning concepts using scikit-learn
  • Module 6: Introduction to Power BI
    5
    • 7.1
      Basics of Power BI and its interface
    • 7.2
      Connecting Power BI to various data sources
    • 7.3
      Using Power BI to create and manipulate data models
    • 7.4
      Building dashboards and interactive visualizations
    • 7.5
      Publishing reports and sharing insights within Power BI
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Introduction to modules and packages
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Introduction to NumPy for numerical data
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