Panda with Matplotlib
Overview
Course Objective
By the end of the course, participants will be able to:
Use Pandas to load, clean, and analyze data.
Calculate statistics (mean, median, max, etc.) and answer questions about datasets.
Handle missing data and filter data based on conditions.
Visualize data with Matplotlib to create insightful charts and graphs.
Save transformed data to CSV or other formats for further use.
Who Should Attend
Data Scientists and Analysts who want to improve their data processing and visualization skills.
Anyone interested in learning how to manipulate and visualize data using Python.
Beginners with basic knowledge of Python looking to apply it to data analysis tasks.
Prerequisites
Basic knowledge of Phyton prior is needed for better learning experience.
Training Calendar
Intake
Duration
Program Fees
Module
Module 1 - Python Refresher (Max 4 hours)
• Python Syntax
• Variables & Collections
• Operators
• Functions
Module 2 - Numpy
• NumPy Arrays
• Numpy Operations
• Numpy Indexing
Module 3 - Pandas
• Series
• DataFrames
• Missing Data
• Group By with Pandas
Module 4 - Visualization with Matplotlib and Pandas
• Matplotlib Basics
• Pandas Visualization Overview
• Pandas Time Series Visualization
FAQs
General Questions:
Q: What is the Pandas with Matplotlib course about?
This 3-day course teaches you how to clean, analyze, and visualize data using the Pandas and Matplotlib libraries in Python. You will learn how to load data, calculate statistics (like mean, median, etc.), clean datasets by handling missing values, and create visualizations such as bar charts and line plots. This course provides essential skills for working with data, making it ideal for Data Scientists and Analysts.
Q: Who should attend this course?
Data Scientists and Analysts who want to enhance their data manipulation and visualization skills.
Python enthusiasts interested in learning how to apply Pandas and Matplotlib to real-world datasets.
Beginners in data science who want to learn data analysis and visualization with Python.
Developers looking to improve their skills in handling and visualizing data.
Q: What are the prerequisites for this course?
Basic understanding of Python programming.
Familiarity with Python’s core data structures (e.g., lists, dictionaries).
No prior experience with Pandas or Matplotlib is required, although familiarity with data analysis concepts (e.g., NumPy, CSV handling) will be helpful.
Q: How long is the course?
The course spans 3 days.
Q: What key topics are covered in this course?
Module 1: Python Refresher
Module 2: NumPy
Module 3: Pandas (Series, DataFrames, Missing Data, Grouping)
Module 4: Visualization with Matplotlib and Pandas (Basic plots, Time Series Visualization)
Case Study: Real-world data analysis and visualization project
Q: Will I receive a certification after completing the course?
Yes, participants will receive a certificate of completion, recognizing their skills in data manipulation and visualization with Pandas and Matplotlib.
Program Content & Skills:
Q: What foundational NLP concepts will I learn in this course?
You’ll learn core NLP concepts like text data cleaning, tokenization, stemming, lemmatization, part-of-speech tagging, and advanced techniques such as bag of words and TF-IDF. The course will also cover text classification, word embeddings, and working with text corpora using NLTK.
Q: How does the course help me apply NLP to real-world problems?
You’ll work with real-world text datasets to apply NLP techniques like sentiment analysis, text classification, and word embeddings. This hands-on approach prepares you to solve problems in areas like social media analysis, customer feedback, and information retrieval.
Q: What skills will I develop in managing NLP models?
You’ll learn how to process and clean text data, apply machine learning algorithms for text classification, and optimize NLP models. Skills include working with bag of words, TF-IDF, evaluating classifiers, and using pre-trained word embeddings for different tasks.
Q: Will I learn how to handle different types of text data?
Yes. You’ll learn how to work with various text data, including social media posts and tweets. Techniques for preprocessing, such as tokenization, stop word removal, and lemmatization, will be covered, along with handling noisy data, missing values, and formatting challenges.
Q: How does this course prepare me for NLP in a professional context?
The course gives you the technical and practical skills to apply NLP in real-world scenarios like automated customer support, sentiment analysis, and social media monitoring. A hands-on project will equip you to handle NLP challenges in industries like marketing, healthcare, and finance.
Submit your interest today !
