Python for Data Analysis and Visualization
This 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: 13th May 2024
Course Duration: 20 Hours
Class Timings: 7PM to 9PM IST
Course Fee: ₹20,000 + GST /-
This Course Includes:
- Live Classes: Class led live by an instructor. Recordings available if you miss a class.
- Practical Assignments: Assignments at the end of every chapter set in a way that they will be combined to form a mini project
- Notes: Detailed notes including summary for every chapter
- Quiz: Quiz at the end of every chapter to check the understanding of important concepts
- Certificate: Earn a Certificate upon completion
Requirements:
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
Course Video:
- 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.
- 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.
- 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.
- 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.
- 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.
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