Python for Data Analysis and Visualization

This course equips participants with essential skills for data analysis and visualization using Python. Suitable for beginners and intermediate learners, it provides a solid foundation in data techniques and empowers participants to create meaningful visualizations for effective communication.

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Program Curriculum
Understanding The Data
  • Chapter 1: Understanding the Types of Data - Introduces the concept of data types like numeric, categorical, ordinal, and nominal, explaining their differences and use cases.
  • Chapter 2: Working with Numeric Data - Covers techniques for analyzing and manipulating numeric data, including calculations, summarization, and visualization.
  • Chapter 3: Working with Categorical Data - Explores methods for handling categorical data, such as encoding, grouping, and analyzing frequency distributions.
  • Chapter 4: Functions to check the Data - Introduces functions to inspect data, like head(), tail(), and describe(), to understand its structure and contents.
Acquiring The Data
  • Chapter 5: Data Collection Patterns and Methods - Discusses various patterns and methods for collecting data from sources such as servers, platforms, and the field.
  • Chapter 6: Data Collection from in-buil Library/Local Files - Demonstrates how to collect data from local files and in-built libraries, such as Excel and CSV files and pandas DataFrame.
  • Chapter 7: Data Collection from Open Source Domains - Explores techniques for collecting data from open-source domains, including web scraping and API integration.
  • Chapter 8: Dealing with GitHub and Kaggle - Covers methods for accessing and utilizing data from GitHub and Kaggle repositories for analysis.
Operation in Data Analysis
  • Chapter 9: Dealing with Null Values - Discusses strategies for identifying, handling, and imputing missing values in datasets.
  • Chapter 10: Data Cleaning - Covers techniques for cleaning data, including removing duplicates, correcting errors, and standardizing formats.
  • Chapter 11: Data Pre-processing - Explores techniques for preparing data for analysis, such as scaling, normalization, and feature extraction.
  • Chapter 12: Data Wrangling - Discusses the process of transforming and reshaping data to make it suitable for analysis and visualization.
Starting with Visualization
  • Chapter 13: Identification of Plot - Introduces different types of plots and their use cases for visualizing data effectively.
  • Chapter 14: Data Visualization in Data Farme - Introduces different types of plots and their use cases for visualizing data effectively.
  • Chapter 15: Advanced Libraries for Data Visualization – Explores advanced data visualization libraries and techniques for creating complex and interactive visualizations.
  • Chapter 16: Impact of Plots - Discusses the impact of data visualizations on decision-making and communication of insights.
Data Analysis Mini-Project
  • Chapter 17: Acquiring and Exploring the Data - Covers techniques for acquiring and exploring data to understand its characteristics and potential insights.
  • Chapter 18: Data preprocessing - Explores methods for preprocessing data, including cleaning, transforming, and encoding, to prepare it for analysis.
  • Chapter 19: Data visualization Demonstrates how to create visualizations to gain insights and communicate findings effectively.
  • Chapter 20: Hypothesis Testing and Insights - Introduces hypothesis testing methods to draw conclusions from data and derive meaningful insights.

Batch 1Live Online Class

Weekday (Mon - Fri)

15th July - 9th August

Batch 2Live Online Class

Weekend (Sat & Sun)

20th July - 11th August

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  • Basic Python syntax and Python data structure understanding

  • A Mac or Windows PC with access to the internet

  • No paid software required – we will use Favo’s shared resources which will give you the exposure to understand Jupyter Notebook and Google Colab

  • Our trainers will walk you through, step-by-step how to get all the software installed, set up and libraries

Skills you will learn:

  • Understanding different types of data (numerical, categorical, etc.)

  • Working with numeric data (mean, median, etc.)

  • Working with categorical data (frequency distribution, cross-tabulation, etc.)

  • Data cleaning and pre-processing

  • Data visualization techniques

  • Data collection from various sources (local files, in-built libraries, web scraping, etc.)

  • Dealing with missing data (dropping, filling, etc.)

  • Basic understanding of statistical methods (chi-square tests, correlation coefficients, etc.)

  • Using functions for data inspection and transformation

  • Building data analysis mini-projects


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