Excel charts can be used for a myriad of data visualizations, including categorical data and continuous data, like time-series data. In this course, you'll learn how to bring data into Excel and build and customize various charts. You'll start by importing data from an existing workbook into a new spreadsheet. You'll then import data from CSV and JSON file formats and Microsoft Access database files. Next, you'll use the Power Query editor to perform various operations. Moving on, you'll create column and clustered column charts and perform various formatting operations on the clustered column chart, such as adding data labels, error bars, axis titles, and trendlines. Lastly, you'll create a simple line chart, formatting various aspects, such as the line, background, title, legend, axes, and position of charts relative to each other.

Data visualizations in Excel reveal the insights uncovered by your data in easy-to-consume representations. You can identify categorical values, recognize how parts sum up to a whole, see percentages rather than absolute values, discretize continuous variables, and approximate the probability density function of variables. In this course, you'll build charts to uncover all of this information. You'll start by working with column and bar charts. You'll then create and differentiate between clustered and stacked column charts. You'll move on to formatting and customizing bar and column charts before working with 2D and 3D chart types and customizing them in various ways. Lastly, you'll work with histograms, examining how they work, what they're used for, and how to customize them to your needs.

Line charts are possibly the most common type of visualization for time-series data, enabling you to see time trends at a glance. These can be augmented with trendlines, used to visualize time trends in data. Stacked area charts are a powerful type of visualization, combining information about trends over time with information about composition and parts of a whole. In this course, you'll learn how to create and customize all of the visualization types above. You'll begin by exploring the purpose of line charts before moving on to formatting and customizing them. You'll then practice using trendlines to evaluate different regression models on data in a line chart. You'll also customize and format these trendlines. Following this, you'll work with area charts and stacked area charts, examining, in detail, the several types of stacked area charts in Excel and customizing their appearance.

Data visualization options in Excel are vast. You should choose your visualization type based on the data and what you want to show from it. For example, using High-Low-Close and Open-High-Low-Close charts (also called candlestick charts), you can summarize several stock performance aspects. Excel also lets you build radar charts - great for visualizing multivariate ordinal data, such as ratings or scores, to spot strengths or spikes. In this course, you'll not only learn how to build and customize the charts mentioned, but you'll also create treemaps to visualize hierarchical data and pie charts to display parts of a whole. You'll then generate pie-of-pie and bar-of-pie charts, both of which use a secondary visualization to complement a pie chart. Finally, you'll create donut charts to visualize composition using multiple concentric donut rings to represent points in time.

Once you grasp how to work with the scope of standard Excel chart types, you can expand into more complex visualizations. For example, you can use box-and-whisker plots to convey a wealth of information about the statistical distribution of a variable and identify outliers in a data series. You can use sunburst charts to visualize hierarchical data with differing levels of detail, waterfall charts to show the cumulative effect of positive and negative values, and Gantt charts to illustrate progress toward a goal involving multiple parallel tasks. Additionally, you can avail of band charts to quickly eyeball the trend in a line chart, scatter plots to uncover the relationship between two variables, and waffle charts to visualize progress towards KPIs. In this course, you'll create all of these charts either via Excel's built-in tools or by building them manually using nifty workarounds.

Tableau is a data visualization tool suitable for a variety of purposes and situations. Knowing the basics of this tool will help you share necessary data with stakeholders and peers in a meaningful and engaging way. This course will introduce you to Tableau's basic features and cover the fundamental operations performed with this tool. You'll start by loading data into Tableau using a variety of file types. You'll then apply transformations to your data and choose the most appropriate visualization for it. Next, you'll learn how to analyze data in multiple related tables as well as data categories. You'll then practice setting the aesthetics of charts and garnering detailed information through chart interactions. Lastly, you'll get hands-on with the Tableau data interpreter to clean a dataset.

Tableau not only has a variety of tools to visualize data, but it also allows you to redefine the fields in your data to convey rich meaning. In this course, you'll explore how multiple dimensions of the underlying data can be portrayed using shapes, colors, and labels. You'll learn to build a wide variety of charts, such as pie charts, treemaps, and many more, to convey various information types. You'll also work with different techniques, such as hierarchical, combined, and calculated fields, to arrange the fields in the underlying dataset into hierarchies and groups, portraying relationships and facilitating data drill-downs for your users. Lastly, you'll learn how you can generate sets from combined data, so this data can be analyzed with the context of the set, as well as outside it.

In addition to everything you'd expect from a data visualization tool, Tableau has extensive support for many advanced visual customization and user interaction features. This course gives you a taster of how these features work. You'll start by using Tableau maps to render country-specific information and then further customize these maps, rendering their appearance and using map layers. You'll then assemble multiple charts together to collectively convey information using dashboards. Next, you'll learn how to change the aesthetic appearance of Tableau dashboards and update them to include charts and other interactive objects and settings, such as navigation buttons, text labels, and download options. Lastly, you'll learn how to share a story publicly in the form of visualizations comprising charts and a dashboard using Tableau's publish feature.

• Power Platform, automates basic business processes with Power

• Automate, performing basic data analysis with Power BI, acting more
• effectively by creating simple Power Apps experiences, and creating
• powerful chatbots by using Power Virtual Agents. This course covers
• an overview of the platform, common data services, as well as a
• platform overview demo.

• Power Platform, automates basic business processes with Power
• Automate, performing basic data analysis with Power BI, acting more
• effectively by creating simple Power Apps experiences, and creating
• powerful chatbots by using Power Virtual Agents. This course
• covers: an introduction to power apps, building solutions, common
• data services, build an app and managing records.

Power Platform, automates basic business processes with Power Automate, performing basic data analysis with Power BI, acting more effectively by creating simple Power Apps experiences, and creating powerful chatbots by using Power Virtual Agents.

Power Platform, automates basic business processes with Power Automate, performing basic data analysis with Power BI, acting more effectively by creating simple Power Apps experiences, and creating powerful chatbots by using Power Virtual Agents.

Most businesses have an enormous amount of data in their possession. But this data is only as valuable as the quality of the processes used to understand it. So how do you gather seemingly disparate data from multiple sources and turn it into digestible insights for all to use? One way to do this is to use Power BI, Microsoft's business analytics service. Use this course to comprehend exactly what's involved in data analysis, including specific tasks and job roles. Examine how Power BI's suite of features can facilitate these tasks efficiently and effectively. Furthermore, see the steps involved in taking the PL-300 certification exam, which tests the learner's ability to analyze data using Power BI. Upon completion, you'll have a theoretical understanding of how Power BI is used to carry out data analysis.

Using Power BI for data analysis means you can pull data from multiple places into one location and turn that combined data into meaningful insights. Use this course to learn how to connect different data sources to Power BI and the types of transformations that can be applied to this data. Install Power BI desktop. Load data from CSV, XML, and JSON files as well as from SQL Server. Use Power Query within Power BI to apply transformations. Then, explore transformations crucial to building meaningful, responsive, and informative visualizations, such as trimming out unnecessary data and merging columns containing related information. Upon completion, you'll be able to load data into Power BI and transform it for analysis.

A vital step between gathering data and creating a report or visualization from it is transforming it into a cohesive form. Use this course to gain hands-on experience in transforming raw data for visualizations. Practice combining related data into a single table. While doing so, perform a preliminary analysis of the data so you can imagine what visualizations can be created from it. Next, combine data from several files into a single table and conduct several transformations, from aggregating data to filtering out unnecessary rows. Finally, build a simple column chart visual from the contents of a Power BI table. While doing so, identify the different storage modes in Power BI and how they affect your visuals. Upon completion, you'll be able to prepare data for visualization using a variety of transformations.

Data as a form of currency is an analogy that's been around since the early 2000s. However, data itself is worthless. The real value is in the insights you gain from it; this is where data analysis comes in. Data modeling is a crucial part of data analysis, helping turn raw data into accurate insights and meaningful reports. Use this course to learn how to design and optimize data models through Power BI Desktop. Examine the star and snowflake schemas. Learn about the DAX formula language. Then, conduct a range of tasks, including combining tables, defining calculated columns, creating measures, and counting values. Upon course completion, you'll know how to use data modeling to transform raw data into powerful visuals and reports. You'll also be further prepared for the PL-300: Power BI Data Analyst certification exam.

Power BI does the heavy lifting when it comes to analyzing data. One way it does this is through the DAX formula language. By learning how to use this language, you can vastly improve the insights you get from data. Use this course to learn how to conduct several data analysis operations using the DAX formula language. Begin by performing aggregation operations, such as sum and average. Learn how to generate and work with blank data in DAX. Explore a variety of counting functions, ranging from simple counts of values to conditional counts based on filters. Lastly, practice using the IF and SWITCH functions. Upon course completion, you'll know how to use DAX to calculate values and reference table data in Power BI. You'll also be further prepared for the PL-300: Analyzing Data with Microsoft Power BI certification exam.

The more you master DAX in Power BI, the better the insights and reports you'll get from data. The filter context is a need-to-know DAX concept. Use this course to get hands-on experience working with this aspect of Power BI. Start by exploring the relationships between tables in Power BI. Move on to using the CALCULATE function. Then, see how a filter context can be defined so that it only points to a subset of the overall data and how you can continue to perform operations within this filter context. Moving on, examine a breadth of options for ignoring the filter context for specific use cases, including how to bypass filters using various DAX functions. When you're done, you'll be able to use DAX functions to perform operations on a subset of data. You'll also be further prepared for the PL-300: Analyzing Data with Microsoft Power BI certification exam.

A big part of data analysis is evaluating how data behaves over time. Power BI's DAX language includes time intelligence functions that enable you to aggregate, compare, and manipulate data using time periods. Use this course to learn how to model data using time-based analysis. Begin by creating a date table. Then use DAX time intelligence functions to extract values from a date. Also, learn how to define dates as a hierarchy, which can then be applied in visualizations. Moving along, practice operating on a range of dates that are shifted from the range in the current context by a specified interval. Finally, perform year-to-date and month-to-date computations from the date in the current context. When you're done, you'll be able to apply a variety of time functions to your data. You'll also be further prepared for the PL-300: Analyzing Data with Microsoft Power BI certification exam.

A well-designed data model eliminates irrelevant and uninterpretable analyses and ensures insightful and well-performing reports. Use this course to step up your data analysis using Power BI's advanced data modeling features. Begin by loading a sales dataset containing a large fact table and multiple dimension tables and modeling it into a snowflake schema. See how Power BI automatically detects relationships. Then, learn how to apply detailed configurations of relationships, from setting the right cardinality to applying bi-directional cross-filtering. Next, explicitly define a hierarchy in your data to model a set of geographical values. Learn further detailed configurations, such as setting a default aggregation operation on fields and hiding specific ones from the reports view. Lastly, learn how to implement row-level security. When you're done, you'll be able to use Power BI's advanced data modeling capabilities. You'll also be further prepared for the PL-300: Analyzing Data with Microsoft Power BI certification exam.

Microsoft Power BI is a powerful and versatile visualization technology widely used in data analytics, especially business data analysis. Business analysts can use this service to build and publish interactive reports for executive audiences as well as collaborators. Get your head around the specifics of data visualization in this introductory course. Explore different types of visualizations and their use-cases, covering standard charts and those for more specific cases. Learn about the structure of Power BI and how it helps with data visualization. Then, examine report creation in Power BI. When you've finished, you'll understand the theory behind data visualization in Power BI and be ready to move on to creating and formatting charts in Power BI. This is one of a series of four paths that can be used to prepare for the PL-300: Analyzing Data with Microsoft Power BI exam.

Workhorse visualizations, such as line charts, column charts, and bar charts, are beneficial in all visualization use cases. Microsoft Power BI does a great job of making these common charts easy to put together and thoroughly customizable. Take this hands-on course to learn how to create and customize various chart types using Power BI. Learn how to create and customize column charts and bar charts from imported data. Explore how to import time-related data using Power Query and transform it into line charts. Then, experiment with multiple formatting operations on the line chart. Upon course completion, you'll be able to build line, bar, and column charts that are engaging and meaningful and be ready to learn how to create more advanced charts, including ribbon and pie charts. This is one of a series of four paths that can be used to prepare for the PL-300: Analyzing Data with Microsoft Power BI exam.

In addition to the ubiquitous line, bar, and column charts, Microsoft Power BI supports other compelling visualizations. These include ribbon charts, a clever way to plot the evolution of both ranks and absolute quantities over time, and funnel charts, useful for representing a linear sequence of events. Advance your Power BI chart-building skills with this hands-on course. Learn how to visualize various kinds of data in the most beneficial way using line and stacked column charts (a combination of line charts and stacked column charts) as well as ribbon, pie, donut, and funnel charts. When you've finished this course, you'll be able to build both standard and specialized charts in Power BI. You'll then be ready to work with maps, waterfall charts, and scatter plots. This is one of a series of four paths that can be used to prepare for the PL-300: Analyzing Data with Microsoft Power BI exam.

Microsoft Power BI is used in some of the world's largest enterprises. Its powerful map visualizations are great for representing global business data. Its waterfall charts benefit financial planners and analysts, while its scatter plots are ideal for data scientists. Through this practical course, learn how to use Power BI to visualize global business data. Work with Power BI maps, applying various customization techniques. Create waterfall charts for drilling through hierarchical data and representing cumulative processes. Then, create scatter charts to show the relationship between two variables and clusters within data. Lastly, use bubble charts to convey information via the size and color of each point in the chart. When you're done, you'll be able to use multiple advanced charts in Power BI and be ready to work with matrix and treemap controls. This is one of a series of four paths that can be used to prepare for the PL-300: Analyzing Data with Microsoft Power BI exam.

As datasets grow larger and more complex, there is an ever-increasing need to organize data into categories and hierarchies. This is what the matrix and treemap controls in Power BI are designed to do. In this course, learn how to utilize matrices and treemaps to their full potential. Create a matrix widget and add aggregations. Create row and column hierarchies from hierarchical fields. Drill through hierarchical treemaps. And format matrices using color scales, data bars, and icons. Additionally, visualize the composition of a whole using treemaps. Learn how to create and filter data through slicers. And sync slicers across different pages and widgets in a report. Upon course completion, you'll have an advanced ability to use Power BI visualizations, ready to move on to using the Power BI service. This is one of a series of four paths that can be used to prepare for the PL-300: Analyzing Data with Microsoft Power BI exam.

Microsoft was a pioneer in desktop applications. But when it adapted to the cloud, it had spectacular success. Similarly, Microsoft's Power BI service has a powerful desktop app in addition to a multifunctional web service. Learn to use the Power BI web service in this hands-on course. Start with publishing reports for other people to view. Then pin visualizations and reports to a dashboard, even live-pinning an entire report to the dashboard. Finally, learn how to use various dashboard features, including the Q&A, which uses natural language processing to answer queries about your data. When you're done, you'll be able to leverage the Power BI Software as a Service and be ready to move on to analyzing and sharing data through Power BI. This is one of a series of four paths that can be used to prepare for the PL-300: Analyzing Data with Microsoft Power BI exam.

Analyzing data can throw light on game-changing insights. To action these insights, it's imperative the appropriate data is shared with the right people. Learn how to analyze and share data using Power BI in this PL-300 preparatory course. Explore considerations when building Power BI reports and collaborating with others. Learn how to streamline report deployment and sharing using Power BI pipelines, which help manage large and complex report deployments. And examine critical practices for securing reports by limiting the data accessible to viewers. Moving on, examine essentials to keep in mind when preparing for the PL-300 certification. Finally, get hands-on with Power BI Desktop, performing some preliminary analysis to identify outliers in your data. Upon completion, you'll know how to analyze data and share reports competently. You'll also be more prepared for the PL-300 certification exam.

Getting insights from data isn't always straightforward. However, using a tool like Power BI helps with heavy lifting. Through this hands-on course, learn to obtain insights from data using a combination of Power BI's visualization and analysis features. Explore how and why numeric data is summarized. Discover how the sum, mean, and median statistics, among others, give different views of the same data. Practice modeling data into customized groups to better represent it to end users. Use scatter charts to identify clusters and enable analysis of snapshots of data with a play axis. Finally, use the key influencers visual to identify how certain metrics are influenced by values in other fields. Upon completion, you'll know how to take advantage of the analytics features of Power BI visuals. You'll also be more prepared for the PL-300 certification exam.

It can be overwhelming to think about analyzing data to meet all pre-defined requirements. Luckily, advancements in technology can help with this analysis. Learn to use the Power BI features made possible by AI and other advanced techniques in this demo-based course. Use Power BI's Analyze feature to understand values and metrics in sales data. Apply the forecasting feature to a line chart to estimate future sales. Then find outliers in terms of daily sales using the anomaly detection feature. Next, use the Q&A visual to allow users to extract insights from data using plain English. Finally, create a decomposition tree to break down the sales data across different categories. Upon completion, you'll know how to use advanced analysis features in Power BI to extract pertinent insights from your data. You'll also be more prepared for the PL-300 certification exam.

Analyzing data and building reports is only useful if the right people can access and understand those reports. Take this course to learn how to involve others in your Power BI reports, be it collaborators in report development or consumers of your analysis. Begin by building a report configured with row-level security. Then, test it in Power BI Desktop before publishing it to the Power BI service. Via the Power BI service, share your workspace with teammates and other collaborators using different roles and permissions. Then use the Pipeline feature to streamline report deployment. Finally, learn to enhance report performance and collaboration by adding endorsements and using the native performance analyzer. Upon completion, you'll know how to use advanced analysis features in Power BI to extract pertinent insights from your data. You'll also be more prepared for the PL-300 certification exam.

Master SQL concepts. Learning the core fundamentals creates a foundation. Discover relational databases and Structured Query Language (SQL) database concepts.

Master SQL tables. Take your first steps in learning about relational databases and Structured Query Language (SQL) tables. Discover how to manage tables, and queries, including complex tables, changing tables, and deleting tables.

Manipulating databases is a necessary skill. Explore Structured Query Language (SQL) and dive into the architecture. Discover efficient and easily manageable databases using features like SELECT, data types, UPDATE, and ORDER BY.

Fundamentals create foundation. Get a clear picture of what SQL is all about. Discover table functions including ALTER and CREATE, as well as column functions and math operators.

Master the core functions of SQL strings. Strings are a key component to any database, and you can work magic using fundamental SQL commands. Explore string functions like LENGTH, REVERSE, and TRIM, as well as how to put them together.

Work with multiple tables. Mastering the fundamentals of SQL creates a foundation. Explore multi-table database architecture and design, connecting several tables, using inner/outer joins, and equijoins and non-equijoins.

Master advanced queries. Queries in SQL allow you to manipulate databases like a pro. Discover advanced techniques including subqueries, correlated queries, difficult queries, and learn query tips for better efficiency.

Discover SQL views. Explore SQL views in detail, defining various views and their management, from creating views to view types, updating, and dropping views.

Master SQL transactions. Explore transactions and how to group statements, Transact-SQL, and transaction logs.

Master SQL transaction locks. Explore transaction locks and explore the different levels of locking.

Mastering security is crucial. Explore one of the most important aspects of SQL security. Discover the lock system, levels of security access and management, privileges and rights, and the setup and installation of security architecture.

Python is a beneficial language for use in a lot of development projects, particularly Java/C++ development. In this course, you'll learn the basics of Python programming. You'll start by installing Python on your local machine and practice writing code using the Python shell. Next, you'll perform basic math and logical operations in Python. You'll create Python variables and see how you can assign and access values stored in these variables. You'll then use built-in functions, which are part of the core Python programming language, to perform simple calculations and operations. Finally, you'll explore strings in Python work, creating strings using single, double, and triple quotes depending on the use case. You'll then briefly examine the use of complex data types, such as lists, tuples, sets, and dictionaries. When you're finished with this course, you'll be able to execute simple Python commands on Jupiter notebooks.

All values in Python are classified into data types. One of these, known as complex data types, facilitates using complex numbers. In this course, you'll learn how to work with complex data types in Python. You'll start by exploring the list data type, which contains an ordered collection of elements. You'll then perform several different operations on lists, such as accessing, adding, and removing elements and implementing slicing operations. Next, you'll work with tuples and examine how tuples contain an ordered collection of elements but are immutable in nature. You'll also work with sets and dictionaries. Finally, you'll explore the nuances of the copy operation for complex data types. When you're finished with this course, you'll be able to use the right Python data type to store your data and perform basic operations using these complex data types.

A handy procedure in Python for controlling the execution order of program statements is to implement branching operations using conditional statements, such as 'if' and 'else'. In this course, you'll learn how to use statements, loops, and comprehensions. First, you'll implement the conditional if statement. Then you'll use the else and elif statements. Moving on, you'll use Python's looping constructs, including the for-loop to iterate over elements in complex data types as well as over lists, tuples, and dictionaries. You'll use the while-loop and the break, continue, and pass keywords to further control loop execution. Finally, you'll implement list comprehension in Python, an elegant and efficient way of generating lists using 'for loops.' When you're finished with this course, you'll be able to write conditional statements in your code and perform looping and branching operations using for and while loops.

In Python, functions are essentially first-class citizens. They are objects in Python, just like other primitive and complex data types, and have a valuable purpose. In this course, you'll learn how to define and invoke functions in Python. First, you'll define a function using the def keyword and specify input arguments and return values from functions. You'll then work with positional arguments and keyword arguments. Next, you'll define functions with default values for arguments and a variable number of arguments. Along the way, you'll also examine how arguments can be pass-by-value or pass-by-reference. Finally, you'll explore the characteristics of Python functions that make them first-class citizens. When you're finished with this course, you'll have a solid grasp of the foundations of support for functions in Python and be able to use Python functions in your development work.

Lambdas are great for on-off use and, once stored in a variable, behave exactly like other function objects in Python. In this course, you'll learn how to create anonymous functions in Python using lambdas. You'll start by creating generator functions in Python to generate infinite sequences using the yield keyword. You'll then illustrate how these generator functions can be resumed from just after the previous yielded value. Moving along, you'll demonstrate how closures in Python are nested functions that keep track of local variables in the outer function. You'll also illustrate how decorators - bits of code allowing you to modify other pre-existing code in your program - can be implemented using closures. When you're finished with this course, you'll have a good grip of functions in Python, which allow you to perform some incredibly complex and powerful operations.

Python classes act like blueprints for establishing a new type of object with its own set of properties and methods. In this course, you'll learn how to define and instantiate classes in Python. You'll start by using the init() method to initialize your class's member variables and the self keyword to reference a class's current instance. You'll then illustrate the differences between the self keyword in Python and the "this" keyword in Java. Next, you'll examine how errors in Python can be handled using the try-except-finally block and how the error handling mechanism in Python is similar to Java exception handling. Finally, you'll import other Python libraries into your current Python program, using classes and functions defined in one Python file in another file using the import statement. When you're finished with this course, you'll be able to set up Python classes for various uses in your development projects.

In this practice lab, learners will be presented with a series of exercises to practice developing in Python. Exercises include tasks such as performing basic math operations, working with data types and strings, using lists, sets, tuples and dictionaries, and using control statements, loops and comprehensions. Learners will also practice working with function and arguments, using lambdas, generators, clousers and decorators, working with classes and importing modules in a program. Learners can also use the environment as an open sandbox. No installation or configuration is required, so you can gain immediate hands-on experience. Create new files or upload your own from a storage location of your choice, such as GitHub, and you can practice coding right away! You can even download a copy of your work when you're done. Whether you're looking to dive into the code presented within our courses or you want to work on your own coding projects, this lab environment will provide you with everything you need. So, go ahead and start coding today! See below for a complete list of available software. - Python 3.7 - Python 2.7 - Tkinter GUI - Python 3 libraries: - Django - Flask - Flask-Migrate - Flask-SQLAlchemy - Flask-WTF - beautifulsoup4 - selenium - Python 2/3 libraries: - ipython - pytest - testbook - Pip 2 and 3 - Node.js - npm - Git - Vim editor - Standard Python libraries This lab is part of the Python for Developers track of the Skillsoft Aspire Pythonista to Python Master Journey.

This 9-video course explores the concept of web frameworks and how they can speed up development of web applications, and examines the Django framework, a widely used framework written in the Python language. Learners begin by studying fundamentals of web requests, the steps and software required when processing web requests for static and dynamic websites. This leads into examining tasks involved in building a website and how web frameworks can speed up the process. Next, you will look at the Django framework and its features that can help to simplify web development, and the components of a Django application that are involved in processing web requests. Continue by observing what templates are in the context of Django and their use cases, and comparing Django models to database tables; then look at the role of the Django object-relational mapping layer (ORM) in mapping the two. Conclude the course by examining some of the built-in Django apps that developers can integrate into their own projects.

Explore fundamentals of Django applications, from installation and the structure of a project, to implementations such as views, URLs, and templates, in this 12-video course. Begin by learning how to create a virtual Python environment and install Django, then how to generate a new Django project and describe various files that are created. You will discover how to start the built-in Django development server on the default port, as well as a specified port; define a view and URL pattern in Django to render the text "Hello World" in a web page; and generate a new app within a Django project. Learn about migrations in Django and using the manage.py script of a Django project to propagate model definitions to the database. Then observe working with Django URLs by configuring project and app-level URLs in Django; defining a view that renders an HTML file in its response; and downloading and using boilerplate HTML, CSS, and Javascript files in a Django project. Conclude by learning how to modify boilerplate HTML files to suit Django project requirements.

Explore the steps involved in a web request and the role of web applications in this web development process in this 8-video course examining various pieces that can make up a web application, and the role of the web framework in defining it. Begin by observing a widely used framework, often defined as a microframework, written in the Python language, which is Flask. You will then explore the features of the Flask framework that are available either out of the box or via extensions. Following on from this, you will delve into the roles of routes in a Flask application and the options available when defining a route function. You will learn how to recognize the need for templates when defining a web site and describe the use of jinja for this purpose. The final tutorial in this course focuses on some of the commonly used extensions in the Flask applications and recalls the purposes they serve.

You will begin this 12-video course by learning how to install Flask-a widely used web framework written in Python language-in a virtual environment on your development machine, and then write the code for a simple "Hello World" website by using Flask. You will explore how route definitions can be altered and the benefits of running your Flask app in debug mode. Next, define a route that renders an HTML page when a URL is accessed; download and use some boilerplate HTML files so your website definition need not begin from scratch, and modify the boilerplate cascading style sheet (CSS) and HTML definitions to customize the look of a website. Learn how to generate URLs dynamically by using the url_for function; create a base Jinja template that can be inherited by other templates, along with placeholders that can be overridden; and explore how to inherit the elements from a base Jinja template in a child template HTML file. Finally, learn how to define multiple routes to point to the same route function.

The free and robust statistical package R has been decades in the making and is worth learning for serious statistical operations, such as conducting new medical data analysis. This course teaches you everything you need to know to get started with R, from installing R to running R from the command line. You'll grasp how to invoke basic functions and view the documentation on those. You'll create variables in R and explore various reserved words and the = and <- operators. You'll then perform basic arithmetic operations on variables, invoke built-in functions, and work with various atomic data types, such as character, integer, double, logical, complex, real, and raw. By the end of this course, you'll have the skills you need to get working with R.

Vectors are the easiest type of data structures in R. However, to use them successfully, it's important to appreciate their restrictions, recognize the types available, and identify their members - or components as they're officially called in R. This course shows you how to create and generate vectors using the c() and vector() functions, respectively. You'll perform vectorized operations on elements in vectors. Practice filtering and slicing vectors. And use the which(), any(), and all() functions on vectors. Furthermore, you'll perform naming and indexing operations on vectors and work with different length vectors using vector recycling. On completing this course, you'll have the knowledge and know-how to utilize vectors for their intended purpose.

Vectors are a great basic data structure in R, but they have important limitations on the dimensions and types of data they contain. Matrices, arrays, and lists are powerful R structures that mitigate these limitations. This course will help you distinguish each of these three elements' purpose and show you how to use them. You'll start by using matrices to store two-dimensional data. You'll then differentiate between row-major and column-major matrices. You'll learn how to use arrays and how you can easily create three-dimensional arrays as you can two-dimensional arrays. You'll then move on to the use of lists and how they differ from vectors. After taking this course, you'll be able to identify when and how to use a matrix, a list, and an array.

Data frames are an R abstraction for tabular data similar to that contained in spreadsheet files or database tables. Data frames can work directly with files in the CSV, JSON, and Excel format, all common formats used to store data. This course outlines the characteristics of data frames in the R programming language and demonstrates how to use them. You'll learn to create basic R data frames from multiple vectors. You'll use factors - similar to enums or enumerated types in other programming languages and great for categorical variables. You'll also learn how to perform various string manipulation operations, such as splitting and joining strings and changing case. You'll then practice the important topic of printing precisely formatted strings with placeholders for variable values. When you're done, you'll be able to use data frames, factors, and strings professionally in your R programming projects.