It is indisputable that skills in Python and data science are in high demand today, and in many ways, these skills are now often a gateway to a fast-track career in both technology and business. While it is a highly flexible and powerful programming language, the beauty of Python lives in the fact it is easy to learn, easily written, and executed much faster than other programming languages. In this course, we take you through the foundations of Python, and then into its application in data science.
It is indisputable that skills in Python and data science are in high demand today, and in many ways, these skills are now often a gateway to a fast-track career in both technology and business. While it is a highly flexible and powerful programming language, the beauty of Python lives in the fact it is easy to learn, easily written, and executed much faster than other programming languages. In this course, we take you through the foundations of Python, and then into its application in data science.
Skills You Will Gain
Big Data
Data Analysis
Pandas
Python
Learning Outcomes (At the end of this program you will be able to)
Understand the object model of Python
Leverage the REPL
Master math with Python
Understand Unicode and string manipulation
Use conditionals and loops
Master the basic data structures like lists and dictionaries
Create functions to enable code reuse
Learn how to slice sequences
Understand how to create and leverage classes
Learn about exceptions and exception handling
Create environments and load libraries
Who Should Attend
Professionals looking to develop a solid understanding of Python and its use in the data science field.
Curriculum
Instructors
Frequently Asked Questions
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Yes, we do offer a certification upon completion of our course to showcase your newly acquired skills and expertise.
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No, we don't offer any free courses, but we do offer 5-day trial only on our subscriptions-based plans.
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Our course is designed with three levels to cater to your learning needs - Core, Intermediate, and Advanced. You can choose the level that best suits your knowledge and skillset to enhance your learning experience.
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*Where courses have been offered multiple times, the “# Students” includes all students who have enrolled. The “%Recommended” shown is also based on this data.
This Pandas course prepares teaches the fundamentals of data analysis. It shows how to load data, inspect it, deal with missing values, use statistical summaries, plot, pivot and more. This course contains 18 hours of materials, taught by Matt Harris
In the course, we will cover all the foundational concepts needed to begin your journey in data science, and business analytics and learn the concepts and techniques required to use statistics effectively to make better decisions.
In this course, you will be introduced to the DataFrame and the Series, the two primary containers of data within pandas. You will learn the components of these objects and a few basic operations and also know what subset selection methods you should
As the founder of Dunder Data, I have dedicated my career to advancing the field of data science and machine learning. My passion for education and expertise in Python programming are reflected in my comprehensive corporate training programs and the influential books I have authored, including Master Data Analysis with Python, Master Machine Learning with Python, Master the Fundamentals of Python, and Pandas Cookbook. Through Dunder Data, I have had the privilege of delivering tailored training to prestigious organizations such as Microsoft, NASA, and the Federal Reserve, and have empowered thousands of students with practical, hands-on knowledge in data science.
With a solid background in both industry and academia, I bring a wealth of experience to the classroom. My journey includes roles as a Quantitative Developer, Lead Data Scientist, and Credit Risk Professional, where I tackled complex problems from predictive modeling to financial analytics. My work has not only focused on developing innovative solutions but also on teaching and mentoring, fostering a deep understanding of data science principles and applications.
In addition to my professional endeavors, my teaching extends to a wide audience through various platforms. I have taught live courses and workshops, providing in-depth insights into Python programming and data science methodologies. My goal is to make sophisticated concepts accessible and engaging, guiding learners through the intricacies of data analysis and machine learning with clarity and enthusiasm.
With a passion for mathematics and education, I have dedicated my career to helping learners of all levels achieve their mathematical goals. I am the founder of "Mathematics with Woody," an initiative designed to provide tailored online mathematics courses. My work focuses on supporting school students preparing for A-levels, as well as adults seeking to reskill or enhance their mathematical expertise for industry-specific applications. My courses are crafted to make complex mathematical concepts accessible and engaging, ensuring that learners not only understand but also apply their knowledge effectively.
In addition to my role as an online educator, I have extensive experience teaching mathematics in various educational settings. My tenure includes teaching at Wren Academy, where I have been guiding students through their mathematical studies for nearly seven years. Previously, I served at King's College London Maths School Trust, where I taught both mathematics and economics, enriching my teaching approach with a broad perspective on the application of mathematical principles.
My academic background includes a Bachelor’s degree from the University of Oxford, a Master’s degree from King's College London, and a Postgraduate Degree in Education from the University of Roehampton. This blend of rigorous academic training and practical teaching experience enables me to offer comprehensive and effective instruction tailored to the diverse needs of my students.
About this course: Overview, Learning Outcomes, Who Should Enroll...
Instructor bio - Matt Harrison
Module 2- Series objects
Module 2
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Module 1- Getting Started – Installation, Jupyter, and Example
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Segment 13 - Line Graphs
Segment 14 - Bar Charts
Segment 15 - Dual Axis Charts
Segment 16 - Pie Charts
Segment 17 - Histograms
Segment 18 - Box Plots
Segment 19 - Cumulative Frequency
Segment 20 - Comparing Visualizations
Segment 21 - Populations and Samples
Segment 22 - Random sampling 1
Segment 23 - Non-Random Sampling
Resources
Module 1.1 - What is Pandas?
Segment 1 - What is Pandas
Segment 2 - Which Version of Pandas to Use
Segment 3 - Pandas Examples
Module 1.2 - The DataFrame and Series
Segment 4 - Introduction to the DataFrame and Series
Segment 5 - DataFrame Components
Segment 6 - Selecting a Series
Segment 7 - Components of a Series
Segment 8 - Getting Help in a Jupyter Notebook
Segment 9 - Exercises
Modules 1.3 - Data Types and Missing Values
Segment 10 - Introduction to Data Types and Missing Values
Segment 11 - Finding the Data Type of Each Column
Segment 12 - Getting More Metadata
Segment 13 - Exercises
Module 1.4 - Setting a Meaningful Index
Segment 14 - Setting an Index of a DataFrame
Segment 15 - Accessing the Index, Columns, and Data
Segment 16 - Accessing the Components of a Series
Segment 17 - The Default Index
Segment 18 - Setting an Index on Read
Segment 19 - Choosing a Good Index
Segment 20 - Exercises
Module 1.5 - Five-Step Process for Data Exploration
Segment 21 - Five-Step Process for Data Exploration
Module 3 - DataFrame objects
Module 3
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Segment 1 - Mean Average
Segment 2 - Median Average
Segment 3 - Mode Average
Segment 4 - Comparing Averages
Segment 5 - Quantiles, Range and IQR
Segment 6 - Standard Deviation and Variance
Segment 7 - Coefficient of Variation
Segment 9 - Kurtosis
Segment 8 - Skew
Segment 10 - Correlation
Overview
Instructions: Using Jupyter Notebooks
Instructions: Virtual labs (For Colaboratory)
Labs: Module 1
Labs: Module 2
Labs: Module 3
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Labs: Module 6
Module 4 - Grouping
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Matt Harrison
With a rich background in Python and Data Science spanning over a decade, I bring a passion for empowering learners to unravel the complexities of programming and data analysis. My journey began with a deep dive into Computer Science at Stanford University, where I honed foundational skills that underpin my expertise today. From designing predictive models that classify job postings to architecting scalable systems on AWS and Docker, my hands-on experience extends across diverse domains, including machine learning, natural language processing, and web application development.
My commitment to education extends beyond traditional classrooms. As an instructor, I've crafted engaging courses that demystify Python for Finance and Essentials of Stats with Python, ensuring students grasp both theoretical concepts and practical applications. At the heart of my teaching philosophy lies a belief in fostering critical thinking and problem-solving skills essential for today's data-driven world. I thrive on creating environments where learners of all backgrounds can explore, experiment, and ultimately excel in Python programming, data analysis, and beyond.
In addition to my role as an educator, I'm deeply invested in community engagement and knowledge sharing. Whether speaking at renowned conferences like PyCon and SciPy or contributing to esteemed platforms such as O'Reilly Media and Pluralsight, I continuously seek to push the boundaries of what's possible in Python and Data Science education. My dedication to this field is underscored by a commitment to authenticity and a genuine desire to bridge the gap between theoretical learning and practical application.
