Exploring Data Science: Comprehensive Pandas Course for Data Analysis and Visualization
About This Course
Exploring Data Science: Comprehensive Pandas Course for Data Analysis and Visualization
Welcome to the “Data Science | Data Analysis Pandas Complete Course” instructed by Faisal Zamir. This comprehensive course is designed to equip students with the essential skills and knowledge required for data analysis using Pandas in the field of data science.
With a video length exceeding 10 hours and 30 minutes, this course offers a deep dive into the intricacies of Pandas, enabling students to become proficient in data analysis techniques.
The course is structured to cover both theoretical concepts and practical applications, providing a well-rounded learning experience. Under the guidance of Faisal Zamir, an experienced instructor in the field of data science, students will gain a solid understanding of how to effectively analyze and manipulate data using Pandas.
By the end of this course, students will have the confidence and expertise to perform data analysis tasks, draw meaningful insights, and make informed decisions based on data-driven approaches.
Whether you are a beginner or an aspiring data scientist, this course will equip you with the necessary skills to excel in the field of data analysis using Pandas.
Course Outlines
These are the outlines that will be covered in this Pandas course for Data Science and Data Analysis.
Note: There may be some modification in outline for chapter 12, 15, 16
Chapter 01
- Introduction
- What is Pandas
- Why need of Pandas
- What we can do with Pandas
- Pandas Installation
- Pandas Basic Program
Chapter 02
- Data Structures
- Types of Data Structure
Chapter 03
- Series
- Series different OperationS
- Series Attributes
- Series methods
- DataFrame
- Panel
Chapter 04
- DataFrame
- DataFrame different OperationS
- DataFrame Attributes
- DataFrame methods
- Panel
Chapter 05
- Descriptive Statistics
- Descriptive Statistics Methods & Programming Examples
- Inferential statistics functions
Chapter 06
- Function Application
- Element-wise
- Row or Column-wise
- Table-wise
Chapter 07
- Reindexing
- Reindexing Method with Programming Examples
- Iteration
- Iteration Method with Programming Examples
- Sorting
- Sorting Method with Programming Examples
Chapter 08
- String Methods
- lower()
- upper()
- title()
- capitalize()
- swapcase()
- strip()
- lstrip()
- rstrip()
- split()
- rsplit()
- join()
- replace()
- contains()
- startswith()
- endswith()
- find()
- rfind()
- count()
- len()
Chapter 09
- Customization Options
- Customizing display options
- Customizing data types
- Customizing data cleaning and manipulation
- Indexing & Selecting
- Label-based or integer-based indexing (.loc[] and .iloc[] )
- Boolean indexing
- Based on a string (.query())
Chapter 10
- Window Function
- Rolling window
- Expanding window
- Exponentially Weighted window
- Weighted window
Chapter 11
Groupby operations
- Splitting Data
- Appling function on that data
- Combining the results
Operations on subset data
-
- Aggregation
- Transformation
- Filtration
Chapter 12
- Categorical Data
- Benefits
- Purpose
- Methods used in Categorial Data
- astype()
- value_counts()
- unique()
- reorder_categories()
- set_categories()
- remove_categories()
- add_categories()
- rename_categories()
- remove_unused_categories()
- ordered
- min(), max()
Chapter 13
- Visualization
- Line plot
- Bar plot
- Histogram
- Scatter plot
- Box plot
- Area plot
- Heatmap
- Density plot
Chapter 14
- I/O Tools
- Reading CSV
- Writing CSV
- Reading Excel
- Writing CSV
- Reading JSON
- Writing CSV
Chapter 15
- Sparse Data
- Features
- Programming Example
Chapter 16
- Date Time Functions
- to_datetime()
- date_range()
- strftime()
- pd.Timestamp()
Lifetime Support
- At Jafricode, we provide lifetime support to our learners. If you lose your account or video content, we will promptly provide them again to ensure uninterrupted access to the course.
- We understand the importance of study materials and missing files. If you misplace them, our team will assist you by promptly supplying the necessary resources.
- If you have any confusion in understanding course concepts, our instructors will offer additional guidance and explanations related to the course outline. We are committed to helping you grasp the material effectively.
Learning Objectives
Understand the fundamentals of Pandas, its importance in data analysis, and the wide range of tasks it can accomplish.
Install Pandas and execute basic programs to perform data analysis.
Familiarize themselves with different data structures in Pandas, such as Series, DataFrame, and Panel.
Apply descriptive statistics methods and inferential statistics functions for data analysis.
Utilize various function application techniques, including element-wise, row or column-wise, and table-wise operations.
Master reindexing, iteration, and sorting techniques to manipulate and organize data effectively.
Explore the rich set of string methods available in Pandas for text data processing and manipulation.
Customize display options, data types, and data cleaning/manipulation processes according to specific requirements.
Learn indexing and selecting techniques using label-based or integer-based indexing, boolean indexing, and string-based querying.
Perform window functions such as rolling, expanding, and exponentially weighted moving average (ewm) for data analysis.
Understand the concept of grouping data, apply aggregation functions, and filter and transform data based on groups.
Handle categorical data using various methods, including type conversion, value counting, reordering categories, and handling missing categories.
Visualize data using line plots, bar plots, histograms, scatter plots, box plots, area plots, heatmaps, and density plots.
Master I/O tools for reading and writing data in different formats such as CSV, Excel, and JSON.
Explore sparse data features and learn to handle and analyze data with missing values efficiently.
Material Includes
- PPT Slides
- Source Code
- Video Lectures
Requirements
- Data Science Enthusiasts: Individuals interested in the field of data science and eager to learn data analysis techniques using Pandas.
- Data Analysts: Professionals working in data analysis roles who want to enhance their skills and expand their knowledge of data manipulation and analysis with Pandas.
- Data Scientists: Practitioners in the field of data science who want to leverage Pandas as a powerful tool for data preprocessing, cleaning, and exploratory data analysis.
- Programmers and Developers: Individuals with programming experience who aim to incorporate data analysis capabilities into their projects using the Pandas library.
- Researchers and Academics: Professionals in research or academic settings who need to analyze and interpret data efficiently using Pandas.
- Business Professionals: Professionals from various industries who want to gain insights from their business data and make data-driven decisions using Pandas.
- Students and Aspiring Data Scientists: Students pursuing degrees or courses in computer science, data science, or related fields who want to acquire solid skills in data analysis using Pandas.
- Freelancers and Consultants: Independent professionals offering data analysis services or consulting who want to enhance their expertise in Pandas for more effective data analysis.
- Anyone with an Interest in Data Analysis: Individuals with a curiosity and passion for data analysis, looking to acquire practical skills in utilizing Pandas for exploring and analyzing data.
Target Audience
- Data Science Enthusiasts: Individuals interested in the field of data science and eager to learn data analysis techniques using Pandas.
- Data Analysts: Professionals working in data analysis roles who want to enhance their skills and expand their knowledge of data manipulation and analysis with Pandas.
- Data Scientists: Practitioners in the field of data science who want to leverage Pandas as a powerful tool for data preprocessing, cleaning, and exploratory data analysis.
- Programmers and Developers: Individuals with programming experience who aim to incorporate data analysis capabilities into their projects using the Pandas library.
- Researchers and Academics: Professionals in research or academic settings who need to analyze and interpret data efficiently using Pandas.
- Business Professionals: Professionals from various industries who want to gain insights from their business data and make data-driven decisions using Pandas.
- Students and Aspiring Data Scientists: Students pursuing degrees or courses in computer science, data science, or related fields who want to acquire solid skills in data analysis using Pandas.
- Freelancers and Consultants: Independent professionals offering data analysis services or consulting who want to enhance their expertise in Pandas for more effective data analysis.
- Anyone with an Interest in Data Analysis: Individuals with a curiosity and passion for data analysis, looking to acquire practical skills in utilizing Pandas for exploring and analyzing data.
Curriculum
111 Lessons10h 40m
Chapter 01
01 Pandas Chapter 01 Outlines00:3:41
02 What is Pandas00:4:34
03 Where we can use Pandas00:3:36
04 What we can do with Pandas00:2:26
Pandas Installation00:00:00
06 Pandas Basic Program00:6:49
Chapter 02
Chapter 03
Chapter 04
Chapter 05
Chapter 06
Chapter 07
Chapter 08
Chapter 09
Chapter 10
Chapter 11
Chapter 12
Chapter 13
Chapter 14
Chapter 15
Chapter 16
Your Instructors
Faisal Zamir
Programming Course Provider
"I am Faisal Zamir, a seasoned educator with over 7 years of experience in teaching web design, development, and programming. I employ coding examples and their solutions to facilitate effective learning among my students. To date, I have had the privilege of instructing over two lakh students across 170+ countries. My extensive teaching experience also includes working in colleges, schools, and academies."
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Material Includes
- PPT Slides
- Source Code
- Video Lectures
-74%
-74%
