Learn to use Pandas, create pivot table on pandas dataframe, filter / sort dataframe, derive fields, run SQL commands
Why take this course?
π Crash Course: Data Analysis using Pandas in Python π
Course Description:
Embark on a transformative journey into the world of data analysis with our comprehensive “Crash Course: Data Analysis using Pandas in Python.” This course is meticulously designed to guide you through every crucial aspect of data manipulation and analysis. Whether you’re a beginner or looking to sharpen your skills, this course promises to equip you with the knowledge and techniques essential for effective data handling.
What You Will Learn:
- Section 1: Getting Started with Python π
- Installation of Anaconda distribution and writing your first code.
- A comprehensive walkthrough of the Spyder Platform to familiarize you with your new data analysis environment.
- Section 2: Working on Data π
- Running SQL commands from within Python.
- Understanding dataset contents and adding comments for better code readability.
- Handling missing values, whether numeric or date-related, and creating copies of dataframes while filtering out records with missing values.
- Mastering numerical variable analysis, including group by operations and transposing results.
- Executing frequency distribution counts, including the percentage of missing values, and delving into functions and substring manipulations.
- Section 3: Working with Multiple Datasets π οΈ
- Creating dataframes dynamically and appending or concatenating them.
- Merging datasets and mastering the art of removing duplicates.
- Advanced sorting techniques, finding records for max/min values, and leveraging
iterrowsto solve complex problems. - Deriving new variables from both numerical and character fields, as well as analyzing data based on date fields.
- Section 4: Data Visualization π¨
- Generating histograms, bar charts, line charts, pie charts, and box plots to visualize your data effectively.
- Revisiting some Python fundamentals to ensure you have a solid grasp of the language’s capabilities.
- Understanding variable scope and utilizing range objects, casting, string slicing, and lambda functions.
- Section 5: Statistical Procedures & Advanced Topics π
- Identifying and treating outliers.
- Creating Excel-formatted reports.
- Crafting pivot tables on pandas dataframes, renaming column names, reading from/writing to SQLite databases, and more!
- Performing linear regression and conducting a chi-square test of independence.
Course Highlights:
β Practical Approach: Learn by doing with real-world datasets and hands-on exercises.
β Expert Guidance: Gopal Prasad Malakar, an experienced instructor, will be your guide through this data analysis journey.
β Flexible Learning: Access the course materials at your convenience and learn at your own pace.
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β Community Support: Engage with peers in our community forums to share insights and challenges.
Who Should Take This Course?
This course is designed for:
- Data analysts seeking to enhance their skill set.
- Aspiring data scientists looking to start a career in data analysis.
- Students who want to dive into the world of Python and data manipulation.
- Professionals from any domain interested in leveraging data to drive decision-making.
Join Us Today!
Embark on your data analysis adventure with our “Crash Course: Data Analysis using Pandas in Python.” Enroll now to transform your approach to handling and analyzing data, and unlock the power of data insights! ππ
Add-On Information:
- Unlock the Power of Data with Python: This intensive crash course equips you with the essential Python skills needed to navigate, manipulate, and extract meaningful insights from diverse datasets. Dive deep into the world of data science and analysis, transforming raw information into actionable intelligence.
- Master the Art of Data Wrangling: Go beyond basic data handling. You’ll learn to efficiently clean, transform, and prepare your data for analysis. This includes sophisticated techniques for identifying and addressing data inconsistencies, missing values, and duplicate entries, ensuring the integrity of your findings.
- Harness the Versatility of Pandas: Gain proficiency in Pandas, the de facto standard library for data manipulation in Python. You’ll master its core functionalities, enabling you to load, inspect, and structure data with unparalleled ease and speed.
- Develop Advanced Data Visualization Foundations: While not explicitly covered, the skills acquired in data manipulation and structuring lay the critical groundwork for creating compelling data visualizations. Understanding how to filter, sort, and derive fields prepares you to effectively communicate your data’s story through charts and graphs in subsequent learning.
- Build Robust Analytical Pipelines: Learn to construct efficient data processing workflows. From initial data loading to the preparation of derived fields, you’ll be able to build a repeatable and scalable process for analyzing your data, saving you significant time and effort.
- Seamlessly Integrate with SQL Databases: Bridge the gap between Python and relational databases. You’ll learn how to execute SQL commands directly from your Python environment, allowing you to query and manipulate data stored in databases with confidence.
- Empower Data-Driven Decision Making: Equip yourself with the practical skills to tackle real-world data challenges. Whether you’re a student, a professional looking to upskill, or a curious individual, this course will empower you to make informed decisions based on data.
- Acquire Foundational Data Science Competencies: This course provides a solid introduction to the fundamental techniques that underpin modern data science practices, setting you on a path for further exploration in machine learning, statistical modeling, and advanced analytics.
- Pros:
- Rapid Skill Acquisition: Designed for quick learning, allowing you to gain valuable data analysis skills in a short timeframe.
- Industry-Relevant Tools: Focuses on Pandas, a widely used and in-demand library in the data science industry.
- Practical, Hands-On Approach: Emphasizes practical application and problem-solving, ensuring you can immediately apply what you learn.
- Cons:
- Introductory Depth: As a crash course, it may not cover every nuance or advanced application of Pandas and data science concepts in exhaustive detail.
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