Master Python for Data Handling [2025] from Udemy

What's inside

Learning objectives

Master python programming - data types, native data structures, data transformers, functions, and logic
Data handling - understand data handling, transform, manipulate, and calculate data. move data between common file formats and data structures
Advanced data handling - understand and use advanced multi-dimensional uneven data structures how to generalize functions

Python object oriented programming - understand object orientation, create custom advanced objects, methods, and and functions, learn to generalize functions
Cloud computing - use anaconda cloud notebook (jupyter notebook). learn to use cloud computing resources
Optional: use anaconda distribution's jupyter notebook and conda package management system

Master python programming - data types, native data structures, data transformers, functions, and logic
Data handling - understand data handling, transform, manipulate, and calculate data. move data between common file formats and data structures
Advanced data handling - understand and use advanced multi-dimensional uneven data structures how to generalize functions
Python object oriented programming - understand object orientation, create custom advanced objects, methods, and and functions, learn to generalize functions
Cloud computing - use anaconda cloud notebook (jupyter notebook). learn to use cloud computing resources
Optional: use anaconda distribution's jupyter notebook and conda package management system

Syllabus

Introduction

Introduction to Master Python for Data Handling

This video describes the setup procedures for using the Anaconda Cloud Notebook

Using Anaconda Cloud Notebook requires internet access and an email address

Note: Anaconda often updates its resources and this may cause minor differences in graphics and procedures

This video describes the procedures to download and install the Anaconda Distribution for use with this course.

Download requires internet access.

Video is optional.

Note: Anaconda often updates its resources and this may cause minor differences in graphics and procedures.

This video describes the Conda Package Management System.

Conda requires internet access.

Video is optional.

Note: Conda is a speedily developing environment and this may cause minor differences in graphics and procedures.

This video provides an overview of "Python for data handling", teaches you some Python and Data Handling theory, and presents a table of contents for Python for Data Handling as well as some basic information about the Jupyter IDE with dynamic typing, Python programs organization, and some fundamental Python language syntax

Learn to use Python Integers

Learn to use Python Floats

Learn to use Python Strings

Learn to use some Python string methods to test, search, transform, change, and manipulate string data

Learn to use date and time data with Python's Datetime module. Learn to calculate time durations and time event data. Learn advanced knowledge about date and time data plus how computers and Python handle datetime data

This video provides an overview of the part of this section about Python's data storage abstractions, the set, tuple, dictionary, and the list

Learn to use Python's Set

Learn to use Python's native Tuple and how to unpack Tuples

Learn to use Python's native Dictionary

Learn to use Python's native List

An overview of the contents of this subpart of the section, Python's data transformers, and functions

Learn to use Python's native while-loop with some practical examples

Learn to use Python's native for-loop with some practical examples

Learn some theory on Python's List Comprehensions. Learn to use Python's List Comprehensions from 1D to 3D with comparisons to ordinary Python Lists and For-Loops.

Learn to use some of Python's logic operators and conditional code branching. Use your learned knowledge to edit and tailor basic descriptive statistics at a detailed level

This video lecture describes the theoretical advantages of Python's functions

Learn practical coding with Python's functions. You are introduced to functions and basic protections for functions. You will learn how to create functions from code-examples from earlier video lectures, and you will learn how to generalize functions up to advanced uneven-multitype-object 2-dimensional list of lists.

Learn to create your own functions!

Learn Python OOP theory relevant for data handling tasks and how object-oriented data structures may affect data handling

Learn to code object-oriented programming with Python, and to handle Python object-oriented code and custom objects within the ambit of data handling

Learn to save files in Python and the practical process of converting custom Python objects to tabular form and saving these into .csv, and Excel files and to load files to Pandas Data Frames

This video lecture is a recap and extension of earlier video lectures. You will assemble knowledge from earlier lectures into more powerful knowledge. You will learn to construct a tabular data form with additional calculated variables and how to use the tabular data form for plotting, etc. You will learn how Data Handling fits with advanced object-oriented program structures.

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Provides a strong foundation in Python 3, which is essential for data science and machine learning tasks, making it ideal for those starting in these fields

Focuses on data handling, a critical skill that data scientists and machine learning engineers spend a significant portion of their time on, potentially boosting their productivity

Teaches Python's native data structures and object-oriented programming, which are useful for learners who want to deepen their understanding of Python

Covers advanced multi-dimensional uneven data structures, which are relevant for handling complex datasets in data science and machine learning projects

Includes optional videos on Anaconda Distribution and Conda package management, which may be subject to updates that cause minor differences in graphics and procedures

Requires internet access for Anaconda Cloud Notebook and optional Anaconda Distribution download, which may pose a barrier for learners with limited connectivity

Reviews summary

Master python for data handling [2025] review analysis

According to learners, this course provides a solid foundation in Python programming specifically tailored for data handling tasks. Many appreciate the clear explanations of core Python concepts like data types, structures, loops, and functions, finding the material on transforming and manipulating data particularly useful and practical. Students often mention the value of the hands-on coding exercises. While some note that the pace can be fast for absolute beginners, the overall consensus highlights the course's effectiveness in building skills needed for data-centric roles and frequently recommend it as a starting point for those aiming for data science or machine learning careers.

Provides practical coding exercises.

"The hands-on coding activities really helped reinforce the lessons."

"I learned best by doing the coding examples alongside the instructor."

"Working through the practical examples made the concepts stick."

"The exercises provide valuable practice for handling data in Python."

Instructor explains complex ideas simply.

"The instructor explains things in a very clear and understandable manner."

"I appreciated how the lectures broke down complex topics into digestible parts."

"Concepts that seemed confusing before were made clear through the explanations."

"The teaching style makes it easy to follow along, even with new material."

Focuses on real-world data manipulation.

"The practical examples of transforming and manipulating data were directly applicable to my work."

"Learning how to move data between file formats like CSV and Excel was incredibly helpful."

"I found the sections on handling complex data structures very insightful for real-world problems."

"The course gives practical tools and strategies for handling data effectively."

Builds essential Python skills for data.

"It really helped solidify my understanding of core Python concepts needed for data tasks."

"I got a strong foundation in Python basics and data structures from this course."

"This course is excellent for learning Python fundamentals if you're focused on data handling."

"The way it explains Python's building blocks is very clear and builds confidence for data work."

May feel rushed for absolute beginners.

"As a complete beginner, I sometimes felt the pace was a bit too fast in later sections."

"If you have no programming background, be prepared to pause and rewatch some parts."

"While clear, the sheer amount of information covered quickly might overwhelm total newcomers."

"It's great if you have some prior logic skills, but maybe fast for zero experience."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Master Python for Data Handling [2025] with these activities:

Review Python Basics

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Reviewing Python basics will ensure a solid foundation for understanding the more advanced data handling concepts covered in the course.

Browse courses on Python Basics

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Review data types (integers, floats, strings).
Practice using basic operators and control flow statements.
Familiarize yourself with Python's built-in functions.

Review 'Python Data Science Handbook'

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Reading 'Python Data Science Handbook' will provide a strong foundation in the core libraries used for data handling in Python.

View Python Data Science Handbook: Essential Tools... on Amazon

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Read the chapters on NumPy and Pandas.
Work through the examples provided in the book.
Experiment with different data manipulation techniques.

Create a Data Handling Tutorial

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Creating a tutorial will solidify your understanding of data handling concepts and improve your communication skills.

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Choose a specific data handling topic (e.g., data cleaning, data transformation, data aggregation).
Write a clear and concise tutorial that explains the topic and provides practical examples.
Include code snippets and visualizations to illustrate your points.
Share your tutorial on a blog, forum, or social media platform.

Four other activities

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Practice Data Manipulation with Pandas

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Practicing data manipulation with Pandas will reinforce your understanding of data structures and transformation techniques.

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Load data from various file formats (CSV, Excel) into Pandas DataFrames.
Perform data cleaning and preprocessing tasks (handling missing values, data type conversions).
Apply filtering, sorting, and grouping operations to DataFrames.
Create new columns and perform calculations on existing data.

Data Cleaning and Transformation Project

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Starting a data cleaning and transformation project will allow you to apply your knowledge and skills to a real-world problem.

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Find a publicly available dataset that requires cleaning and transformation.
Define the goals of your project and the specific data transformations you want to perform.
Write Python code to clean, transform, and analyze the data.
Document your code and findings in a report or presentation.

Review 'Effective Pandas'

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Reading 'Effective Pandas' will help you write more efficient and maintainable data handling code.

View Effective Pandas 2 on Amazon

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Read the chapters on performance optimization.
Experiment with different Pandas techniques to improve code efficiency.
Refactor existing code to apply the principles learned from the book.

Contribute to a Data Handling Library

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Contributing to an open-source data handling library will provide valuable experience working on a real-world project and collaborating with other developers.

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Find an open-source data handling library on GitHub or GitLab.
Identify a bug or feature that you can contribute to.
Fork the repository and create a new branch for your changes.
Write code to fix the bug or implement the feature.
Submit a pull request with your changes.

Career center

Learners who complete Master Python for Data Handling [2025] will develop knowledge and skills that may be useful to these careers:

Data Scientist

A data scientist uses programming skills and statistical methods to analyze data and extract meaningful insights. This course on mastering Python for data handling directly supports a data scientist's work by teaching the core Python skills and data handling techniques that are crucial for success. The course specifically addresses transforming, manipulating, and calculating data, which is a large part of a data scientist's daily tasks. The course's focus on using multi-dimensional data structures is especially useful for data scientists who work with complex datasets. Using the skills taught in the course, a data scientist will be better equipped to clean, preprocess, and analyze data efficiently, which translates to greater productivity and more impactful results.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine learning engineers build and deploy machine learning models. This role requires a strong foundation in programming and data handling, exactly what this course provides in the Python programming language. The course teaches how to work with different data types and structures, along with custom functions and objects, all of which are essential for machine learning tasks. The 'Master Python for Data Handling' course also covers transforming, manipulating, and calculating data, which is critical when machine learning engineers need to prepare data for model training. The ability to use cloud-based resources like Anaconda Cloud Notebook is very relevant to this role where cloud computing is commonly used in machine learning.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

A data analyst interprets data to identify trends and draw conclusions. This requires proficiency in data manipulation and analysis, which this Python course delivers. The course helps you learn to handle data using Python’s native structures and to transform, manipulate, and calculate data, skills that are vital for any data analyst. The course emphasizes moving data between common file formats and structures, making it easier for a data analyst to work with information from various sources. Furthermore, the course teaches how to use advanced multi-dimensional data structures, which are helpful when working with complex datasets. A data analyst will benefit substantially from the skills developed in this course.

See salaries and explore the career path for Data Analyst

Business Intelligence Developer

A business intelligence developer designs and implements systems to extract, transform, and load data for business reporting. As a business intelligence developer, you will benefit from this course which teaches data handling using Python. The course provides the specific skills necessary in data transformation, manipulation, and calculation. A business intelligence developer needs to be able to use different data structures, and this course covers Python's native data structures as well as how to move data between file formats. The course's lessons on creating custom functions and objects are also useful for those who need to write custom scripts for data processing tasks. A business intelligence developer's capabilities will be significantly improved by this course.

See salaries and explore the career path for Business Intelligence Developer

Quantitative Analyst

A quantitative analyst, or quant, develops mathematical and statistical models for financial analysis. This requires a solid foundation in programming and data handling, which this Python course provides. The course helps you to master Python's native data structures, and you will learn to transform and manipulate data, which is necessary when working with financial datasets. The ability to create custom functions and objects is vital for building quantitative models. Furthermore, the course's focus on advanced multi-dimensional data structures is essential for quants who handle complex and large datasets. A quantitative analyst will find this course instrumental in their work.

See salaries and explore the career path for Quantitative Analyst

Research Scientist

A research scientist conducts experiments and analyzes data to advance scientific knowledge. This work often involves handling large datasets, and the skills taught in this course are highly relevant. As a research scientist, you will be able to use Python for data handling, and this course teaches core concepts of data transformation, manipulation, and calculation. The course's lessons on working with different data formats and structures make it easy to manage various types of data. Additionally, the ability to create custom Python functions and objects is important for a research scientist to customize their data analysis workflows. A research scientist will find this course a worthwhile addition to their skillset.

See salaries and explore the career path for Research Scientist

Software Developer

A software developer creates and maintains software applications. This Python course, while focusing on data handling, provides a strong foundation in Python programming, which is helpful for many software development tasks. The course teaches the core concepts of Python programming including data types, structures, and how to create custom functions and objects. Although a software developer may not always be focused on data manipulation, these skills, especially in Python, can be useful for many software engineering tasks and data-related components. A software developer might find this course particularly useful, especially if they want to work on data-heavy applications or projects with data analysis components, or to expand their skillset.

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Database Administrator

A database administrator manages and maintains databases. While not directly focused on databases, this course can be useful for enhancing a database administrator's ability to work with data. This Python course teaches data handling and the use of Python's native data structures, which are helpful in understanding how data is structured and processed. While not directly related to database administration, knowing Python at this level helps an administrator interact with data, perform data migrations and transformations, and perform data quality checks. A database administrator may find that this course provides valuable supplementary skills, making them more well rounded.

See salaries and explore the career path for Database Administrator

Bioinformatician

A bioinformatician uses computational tools to analyze biological data. This course, which focuses on mastering Python and data handling, may be helpful for a bioinformatician. In this course, you will learn how to manipulate data using Python, and the course also teaches how to work with different data types and structures. Since bioinformaticians often work with large and complex biological datasets like DNA sequences, the skills learned here for data transformation, calculation, and management of different file formats can be useful. A bioinformatician might find this course to be a useful addition to their skillset, allowing them to handle data more efficiently.

See salaries and explore the career path for Bioinformatician

Financial Analyst

A financial analyst analyzes financial data to provide insights for business decisions. This course, while not directly aimed at financial analysis, may be useful for a financial analyst looking to expand their data skills. This Python course focuses on data handling, teaching core Python concepts and how to transform, calculate, and manipulate data. These skills are useful when a financial analyst needs to work with large datasets. The course also covers moving data between various file formats and structures which are skills that may come in handy when working with financial data in diverse formats. A financial analyst may find this course useful to enhance their ability to handle data and improve efficiency in data analysis.

See salaries and explore the career path for Financial Analyst

Statistician

A statistician collects, analyzes, and interprets numerical data. While this Python course is more about data processing, it may still be helpful to a statistician. The core Python concepts and skills taught in the course, such as data transformation, manipulation, and calculation, are useful for a statistician. Additionally, the ability to move data between file formats and structures is important for the statistician who might receive data in various forms. The course's lessons on custom functions and objects can also be useful when developing analysis tools. A statistician might find this course a useful tool to improve their ability to handle data, though many of their tasks might require other tools.

See salaries and explore the career path for Statistician

System Analyst

A system analyst evaluates and improves computer systems. While this Python course is not directly focused on system analysis, it might be useful for a system analyst who needs to work with data. A system analyst might use these skills to write scripts to automate tasks, analyze data coming from systems, or create data transformation pipelines. The course provides a solid foundation in Python, which could become useful to a system analyst. Though a system analyst's job will not focus on data handling, learning Python and its data handling capabilities may be helpful for those who want to expand their skill set.

See salaries and explore the career path for System Analyst

Market Research Analyst

A market research analyst studies market conditions and consumer behavior. While the focus of this Python course is not directly market research, a market research analyst may still find it useful. The analyst could apply their knowledge of Python to handle and transform datasets of marketing data. The course's lessons in data structures, transformation, and manipulation could help the analyst work more efficiently with the kind of data they regularly encounter. Although this course is more focused on data handling, certain aspects of it will help a market research analyst and may be worthwhile for expanding their skill set.

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Project Manager

A project manager plans, executes, and closes projects. This Python for Data Handling course may be useful for a project manager who needs to understand data-related project components. The project manager will gain an appreciation of the complexities of moving data between file formats and the data wrangling necessary for data handling, especially if they manage data-centric projects. Although project management does not require data handling skills directly, this course may give a project manager a new frame of reference.

See salaries and explore the career path for Project Manager

Technical Writer

A technical writer creates user manuals and documentation. This Python for Data Handling course has very little applicability for a technical writer. Although this course could, in special cases, help a technical writer working on a technical manual related to Python or data handling, this is highly specialized. The course's focus is programming and data handling, which are not central to a technical writer's work. As such, the course is unlikely to be a useful skill-building exercise for a technical writer.

See salaries and explore the career path for Technical Writer

Master Python for Data Handling [2025]

What's inside

Learning objectives

Syllabus

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Reviews summary

Master python for data handling [2025] review analysis

Activities

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