Python for Data Analysis and Visualization

Introduction: The "Data Analysis and Visualization with Python" course is designed to equip participants with the essential skills and tools required to analyze and visualize data using the Python programming language. This comprehensive course caters to both beginners and intermediate-level learners, providing a solid foundation in data analysis techniques and empowering participants to create meaningful visualizations for effective data communication.

Starting From: 22nd January 2024

Course Duration: 20 Hours

Class Timings: 6:50PM to 9:20PM

Course Fee: ₹20,000/-


About Course

Course Objectives:

  • Fundamental Python Skills:

    • Participants will begin by strengthening their Python programming skills, ensuring a solid grasp of the language's fundamentals.
    • Emphasis will be placed on key libraries such as NumPy and Pandas for efficient data manipulation and management.

  • Data Cleaning and Preprocessing:

    • Learn techniques for cleaning and preprocessing raw data, ensuring it is suitable for analysis.
    • Address common data quality issues, handle missing values, and transform data into usable formats.

  • Exploratory Data Analysis(EDA):

    • Gain insights into exploratory data analysis techniques to understand the structure and patterns within datasets.
    • Utilize statistical measures, visualizations, and descriptive statistics to uncover hidden trends and relationships.

  • Data Visualization Libraries:

    • Explore popular Python libraries such as Matplotlib, Seaborn, and Plotly for creating a wide range of static and interactive visualizations.
    • Understand the principles of effective data visualization for conveying insights to diverse audiences

  • Advanced Statistical Analysis:

    • Delve into advanced statistical methods using libraries like SciPy and Statsmodels
    • Apply hypothesis testing, regression analysis, and other statistical techniques to make informed decisions.

  • Machine Learning Integration:

    • Integrate basic machine learning concepts into data analysis workflows using scikit-learn
    • Learn how to apply machine learning algorithms for predictive modeling and classification tasks.

  • Real-world Case Studies:

    • Engage in hands-on, real-world case studies to apply learned concepts in practical scenarios
    • Tackle challenges commonly encountered in data analysis projects and develop problem-solving skills

  • Collaboration and Reporting:

    • Explore tools and techniques for collaborative data analysis, including version control systems like Git.
    • Learn to create professional reports and interactive dashboards using tools such as Jupyter Notebooks and JupyterLab.

  • Ethical Considerations:

    • Discuss ethical considerations and best practices in data analysis, including privacy concerns, bias mitigation, and responsible data handling.

Course Format:

The "Python for Data Analysis and Visualization" course employs a combination of lectures, hands-on coding exercises, and projects to reinforce learning. Participants are encouraged to actively engage with the material and seek assistance through Q&A sessions and forums.

By the end of the "Python for Data Analysis and Visualization" course, participants will have gained the skills and confidence needed to navigate the entire data analysis pipeline, from data cleaning and exploration to advanced visualization and machine learning integration. This course serves as a valuable asset for professionals seeking to enhance their analytical capabilities and make data-driven decisions in various industries.


Earn a Certificate upon completion

Online Classes

Start instantly and learn at your own

Life Time Accessibility

Set and maintain flexible deadlines.

Beginner Level

No prior experience required.

Program Curriculum
  • Introduction to Numpy
Data Analysis and Visualization Foundation
  • Numpy Arrays
  • Numpy Array Indexing
  • Numpy Array Operations
  • Numpy Exercise
  • Introduction to Pandas
  • DataFrames

Our Program Focuses On Three Key Areas

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