Professional Data Science

This course gives you a basic understanding of Python. As one of the most popular programming languages, and Python-based web frameworks such as Django on the rise, the ability to read and write this legacy language is becoming increasingly important in the software industry. Python is used to power web services such as YouTube, DropBox, Google, Reddit, Yahoo, Pinterest, and Instagram and many small companies are taking advantage of this powerful, full-featured programming language.

In this course you will cover the installation of the integrated development environment as well as the use of variables, operators, loops, and decision-making. You will also learn to manipulate data, file I/O and exception handling. Prior programming experience would be beneficial but is not required.

This course introduces you to basic data science techniques using Python. You'll explore core concepts like Data Frames and joining data. You'll also explore how to use data analysis libraries like Pandas, NumPy and Matplotlib. This course provides you with an overview of loading, inspecting and querying real-world data and how to answer basic questions about that data. You'll gain skills in data aggregation and summarization, as well as basic data visualization.

In this course, you will look at how data is organized and leveraged through the lens of key performance indicators (KPIs) to make data-driven decisions. You will also look at defining the roles of data for effective decision-making in the modern workplace, as well as how to break down a data fluency framework. Finally, you will learn to effectively identify where data can add value to an organization.

This course will help you identify the elements and processes of a well-designed transactional database. Terminology and design objectives of relational databases will also be covered. You will learn how to execute basic T-SQL Queries on a Microsoft SQL Server Database, as well as explore Azure Cloud Data Solutions and identify common data workloads.

This course introduces you to how data solutions are delivered using cloud services. You'll learn how to use Power BI data analysis tools and query databases. Further, you'll examine Azure cloud-based storage types and identify common elements of large-scale data analytics. Finally, you'll consider the use of real-time data analytics and explore data-analytics pipelines.

This course helps you understand cloud-based data storage and ingestion. You will explore partitioning strategies, data serving layers, and physical and logical structures of a data solution. You will also learn to run interactive queries using cloud analytics serverless SQL and explore computer and storage options in a cloud-based data environment.

In this course, you will continue learning about data engineering in a cloud-based service. You will explore how to ingest and transform data while learning how to design and develop a batch solution and scale resources.

In this course, you'll complete the final modules, where you'll learn about data policies and standards and how to monitor data storage and data processing. Finally, you'll explore data pipelines and deployment, challenges in data engineering and infrastructure provisioning related to Continuous Integration and Deployment (CI/CD).

In this course, you'll learn how to use data analysis tools for enterprise data analysis and reporting while also exploring key aspects of governing and administering a data analytics environment. You'll learn how to integrate an analytics platform into an existing Information Technology (IT) infrastructure and manage the analytics development lifecycle. Furthermore, you'll learn how to query data, design and build tabular data model and optimize enterprise-level data models.

In this foundations course, you will learn the fundamentals of Artificial Intelligence (AI) and Machine Learning (ML). From the basics to more advanced techniques, you will learn to identify opportunities for AI. You will explore how to clearly define ML/AI problems and use data science techniques to analyze and solve business problems. Focusing on tabular data, you'll build supervised and learning solutions while exploring the modelling process which includes how to build classification and evaluate classification models.

In this course, you'll gain practical skills using regression and time series analysis techniques to solve real-world problems. Discover how to identify and define regression problems, how they work and their different parameters. You'll also learn the best techniques and practices for training and validating regression models and how to evaluate and improve the model's performance. No coding experience is required.

You'll also explore time series data, including univariate and multivariate time series and how to apply these techniques to business challenges, such as forecasting sales trends, projecting customer demand or predicting insurance claims based on historical data. By the end of the course, you'll have the skills to build regression models and use time series analysis to make informed decisions in various industries and to help companies reduce their risk of financial loss.

In this course, you will learn the fundamentals of neural networks including how to adjust for different variables, train a neural network with backpropagation, and prepare images and text data for modelling.

Through demonstrations and applied learning, you will explore how to build a complete neural network architecture to solve different types of problems as well as how to improve a neural network’s performance.

You will also learn about proper techniques and best practices in training and validating clustering models through unsupervised learning. Finally, you will work through the complete process of solving a clustering problem and learn how to evaluate and improve the performance of clustering algorithms.

In this course, you will gain insight into the operationalization of Machine Learning (ML) solutions and engage in practical, hands-on learning to discover the best practices for constructing ethical AI solutions. Through this exploration, you will learn to recognize and understand the framework for managing models, documentation, explainability and reproducibility.

To acquire the ability to apply developed models to real-world scenarios in a production environment, you will deploy models as microservices and integrate them into end-user applications. Finally, with a focus on how end-users consume data, you will learn to present scored data through an interactive dashboard.

For those interested in coding, there is an optional component on using Python to build, assess and deploy machine learning models.

This course gives you the opportunity to apply Machine Learning (ML) skills to a project practically. You will apply ML to solve problems, leverage opportunities and build a basic ML model.

This course gives you the opportunity to apply practical data science skills to a project. You will use data science skills to solve problems, leverage opportunities and build a data solution.