Python for Data Analysis

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Python for Data Analysis

Category: Programming

Duration: 35 Hrs

Transform raw data into powerful insights using Python’s most versatile libraries. This beginner-to-intermediate course equips learners with the essential tools and techniques to clean, analyze, and visualize data using Python. Through hands-on projects and practical exercises, students will master pandas, Matplotlib, and Seaborn while working with real datasets. Whether you're preparing for a data science path or simply want to make smarter decisions with data, this course delivers clarity, confidence, and career-ready skills.

Key Outcomes:

  • Understand the role of Python in modern data workflows
  • Manipulate and analyze data using DataFrames and pandas
  • Clean, transform, and prepare datasets for analysis
  • Create compelling visualizations with Matplotlib and Seaborn
  • Work with CSV, Excel, and JSON formats
  • Conduct exploratory data analysis (EDA) on real-world datasets
  • Present findings through a final project and receive expert feedback

Ideal For: Aspiring data analysts, business professionals, and learners curious about data-driven decision-making.

Course Modules

Description:

Learners are introduced to the role of Python in data analysis, setting up their environment with Jupyter Notebook and exploring key libraries like pandas, numpy, and matplotlib.

Skills Learned:

  • Install and configure Python for data work
  • Navigate Jupyter Notebook
  • Understand the purpose of pandas and numpy
  • Load and inspect datasets

Description:

This module dives into pandas DataFrames—how to create, manipulate, and explore structured data efficiently.

Skills Learned:

  • Create and modify DataFrames
  • Indexing, slicing, and filtering data
  • Rename columns, sort values, and reset indices
  • Handle missing values and data types

Description:

Learners tackle messy data by applying cleaning techniques and transforming datasets for analysis.

Skills Learned:

  • Drop or fill missing data
  • Apply functions across rows/columns
  • Group and aggregate data
  • Merge, join, and concatenate datasets

Description:

Students learn to create visual representations of data using Matplotlib and Seaborn, enhancing interpretability and storytelling.

Skills Learned:

  • Plot line graphs, bar charts, histograms, and scatter plots
  • Customize plots with labels, legends, and styles
  • Use Seaborn for heatmaps, boxplots, and pair plots
  • Save and export visualizations

Description:

This module focuses on reading and writing structured data formats commonly used in real-world scenarios.

Skills Learned:

  • Read/write CSV files using pandas
  • Import/export Excel sheets
  • Parse and manipulate JSON data
  • Handle file paths and encoding issues

Description:

Learners apply all skills to a real dataset (e.g., sales, weather, or COVID data), uncovering insights through cleaning, analysis, and visualization.

Skills Learned:

  • Perform full-cycle EDA
  • Identify trends, outliers, and correlations
  • Create a data narrative using visuals
  • Document findings and present results

Description:

Students present their project, receive feedback, and reflect on their learning. This module reinforces best practices and prepares learners for real-world applications.

Skills Learned:

  • Present data findings confidently
  • Review and improve code quality
  • Receive and apply constructive feedback
  • Understand next steps in data science journey