It is recommended to only take this course if you have completed Python Fundamentals or have equivalent experience.
This course shows you how to move beyond straight line code and write programs that require complex decisions. These might occur within a business workflow or a compex scientific computation. You will write conditional, try-except, for-loop, and while-loop statements, as well as use them to design functions.
It is recommended to only take this course if you have completed Python Fundamentals, User-Defined Functions in Python, and Developing a Currency Converter or have equivalent experience.
This course introduces you to mutable data structures, which are advanced Python types that enable faster updating and search than basic types like ints and strings. These types are necessary for working with large data sets but can be difficult to master. You will explore multiple methods to work with these objects, which include lists, sets, and dictionaries. You will also write expressions and employ extensive use of visualization.It is recommended to only take this course if you have completed Python Fundamentals, User-Defined Functions in Python, Developing a Currency Converter, and Controlling Program Flow or have equivalent experience.
You will begin by examining several types of files and objects. You will then apply the concepts you have learned in the previous courses to solve a real-world business problem: auditing an organization's regulatory compliance. Working with heterogeneous data, you will first read a series of disparate data files and determine how to integrate the data. You will then write a sequence of scripts that pull information from these files and inform the user on whether the organization has fully complied with regulations.
This course serves as a capstone experience to five courses:
- Python Fundamentals
- User-Defined Functions in Python
- Developing a Currency Converter
- Controlling Program Flow
- Mastering Data Structures
Python is much more than a programming language. In this course, you will leverage the comprehensive Python ecosystem of libraries, frameworks, and tools to develop complex data science applications. Throughout this course, you will practice using the different Python tools appropriate to your dataset. You will leverage library resources for data acquisition and analysis as well as machine learning. Dataframes will be introduced as a means of manipulating structured data tables for advanced analysis. Additionally, you will practice basic routines for data visualization utilizing Jupyter Notebooks.
It is recommended to only take this course if you have completed Constructing Expressions in Python and Writing Custom Python Functions, Classes, and Workflows or have equivalent experience.
Decision-makers generally do not use raw data to make decisions; they prefer data be summarized in easily understood formats that facilitate efficient decision-making. This course introduces data manipulation and visualization, both critical components of any data science project. This course introduces two commonly used data manipulation tools in the Python ecosystem: NumPy and Pandas. In addition, the Python ecosystem also includes a variety of data plotting packages such as Matplotlib, Seaborn, and Bokeh — each of which specialize in particular aspects of data visualization. This course will give you experience integrating NumPy, Pandas, and the plotting packages to create rich, interactive data visualizations that help drive efficient decision-making.
It is recommended to only take this course if you have completed Constructing Expressions in Python, Writing Custom Python Functions, Classes, and Workflows and Developing Data Science Applications or have equivalent experience.
Most data science projects that use Python will require you to access and integrate different types of data from a variety of external sources. This course will give you experience identifying and integrating data from spreadsheets, text files, websites, and databases. To prepare for downstream analyses, you first need to integrate any external data sources into your Python program. You will utilize existing packages and develop your own code to read data from a variety of sources. You will also practice using Python to prepare disorganized, unstructured, or unwieldy datasets for analysis by other stakeholders.
It is recommended to only take this course if you have completed Constructing Expressions in Python, Writing Custom Python Functions, Classes, and Workflows, Developing Data Science Applications, and Creating Data Arrays and Tables in Python or have equivalent experience.
In order to be useful within a professional environment, data must be structured in a way that can be understood and applied to real-world scenarios. This course introduces using Python to perform statistical data analysis and create visualizations that uncover patterns in your data. Using the tools and workflows you developed in earlier courses, you will carry out analyses on real-world datasets to become familiar with recognizing and utilizing patterns. Finally, you will form and test hypotheses about your data which will become the foundation upon which data-driven decision-making is built.
It is recommended to only take this course if you have completed Constructing Expressions in Python, Writing Custom Python Functions, Classes, and Workflows, Developing Data Science Applications, Creating Data Arrays and Tables in Python, and Organizing Data with Python or have equivalent experience.
In this course, you will explore some of the machine learning tools you can use to magnify the analytical power of Python data science programs. You will use the scikit-learn package — a Python package developed for machine learning applications — to develop predictive machine learning models. You will then practice using these models to discover new relationships and patterns in your data. These capabilities allow you to unlock additional value in your data that will aid in making predictions and, in some cases, creating new data.
It is recommended to only take this course if you have completed Constructing Expressions in Python, Writing Custom Python Functions, Classes, and Workflows, Developing Data Science Applications, Creating Data Arrays and Tables in Python, Organizing Data with Python, and Analyzing and Visualizing Data with Python or have equivalent experience.
With the number of applications available today, it is easy to create an assortment of graphs, charts, and other visualizations of data. This does not, however, guarantee that the data and the story behind it are being compellingly conveyed; without pinpointing that story in the data, it is impossible to communicate it effectively with visuals. In this course, you will examine how to frame the narrative in your data, determining the right visualization for the right question. Next, you will explore design principles that consider human attention and perception, then apply these concepts to your own visualizations in order to create simple, effective visuals that illustrate the key points in your data. Finally, you will compile your visual narratives and prepare them for professional presentation.
You will be required to purchase “Storytelling with Data: A Data Visualization Guide for Business Professionals” by Cole Nussbaumer Knaflic to complete your coursework.
Human-computer interfaces have become a part of everyday life, whether we consider technology that we use at home or at work. People rely on technology to help them achieve a goal or solve a problem, and this idea is central to the emerging and rapidly expanding field of human-centered design: Who is using the interface, and for what purpose? How can we help them do that better? Answering these questions should be at the heart of the design process, as technologies are ultimately for people to use, and designers need to make this as intuitive and smooth as possible.
Design doesn't happen in a lab; it happens in the world, and gathering information about the users of your product ensures better design. In this course, you will be introduced to human-computer interaction design, use practical methods for applying sound design principles, and execute the entire process. You'll discover the basics of how to identify a human need, how and why you need to keep that need at the center of the design process, uncover what can be measured to improve the design, and ensure that you conduct your research fairly and ethically.
When giving a presentation, you want to ensure you communicate all of your critical ideas while you have your audience's attention. There are more effective ways of doing so beyond the standard large amounts of text and bullet points.
In this course, you will have the opportunity to rethink the way you design your presentations and slides. You will discover that there are straightforward ways to use your slide decks to serve two purposes: support your technical and business presentations while making your slide decks reusable and valuable resources inside your organization. You will then examine the life cycle of your presentations and begin to document who uses your slides, when they are used, and what clearances are needed to share and use them. You will also consider legal issues or proprietary concerns that may exist. Finally, you will start to build a process to help you protect proprietary information before you share it with external parties. As part of your study, you will review various selections from Dr. Traci Nathans-Kelly's book “Slide Rules,” which provides helpful insights and enlightening examples that you can apply in your own presentations.
It is recommended to only take this course if you have completed “Redesigning Slides for Impact” and “Engaging Presentation Techniques,” or have equivalent experience.
Your work in a technical field likely means that you periodically interact with colleagues, customers, suppliers, and other stakeholders who live in a different part of the world, speak a primary language different from your own, or have expertise in a different or non-technical field.
As a technical expert, your ability to anticipate the needs of audiences from diverse backgrounds and communicate effectively with them is essential.
In this course, you will have an opportunity to explore how you can prepare to meet the needs of audiences with differing backgrounds, primary languages, and levels of expertise, and even varying degrees of receptivity to your message. You will examine principles of persuasion and consider how and when to apply them both effectively and ethically. As part of your studies, you will also review pertinent selections from Dr. Traci Nathans-Kelly's book “Slide Rules,” and you will look at how you can prepare for the unexpected in your talks and maintain your composure when disruptions occur.
By the end of this course, you will have gained techniques and insights that you can apply as you prepare and develop presentations for a wide range of audiences with varying needs and interests.
It is recommended to only take this course if you have completed “Redesigning Slides for Impact,” “Engaging Presentation Techniques,” and “Designing Slides for Live and Legacy Use,” or have equivalent experience.
In this course, you will practice making informed decisions based on statistical results. You will be introduced to the techniques you will use to view statistical tests critically and recognize the limitations of statistical conclusions. Next, you will examine statistical reports in order to identify the underlying research question. You will then use these insights to compare tests and rate their validity. Finally, you will prepare a report for stakeholders, providing recommendations based on your interpretation of statistical results.
It is recommended to only take this course if you have completed "Interpreting and Communicating Data" or have equivalent experience.