You will explore several key data lifecycle frameworks, including the stages of workflow, and the tools and best practices that support them. The ability to perform data lifecycle management is crucial in order to successfully take data projects effectively from inception to completion. This knowledge will equip you with a structured approach to handle data projects, ensuring that each phase is executed efficiently.
Building on this foundation, you will delve into the tools and best practices that enhance each stage of the lifecycle. You will discover various software, documentation, and syntax rules that streamline data management and analysis, ensuring high-quality outcomes. By integrating lifecycle frameworks with the right tools and best practices, you will be able to manage the entirety of a data project more proficiently. This holistic approach will give you the foundation you need to understand more advanced data science concepts. This course prepares students to take the CompTIA DataX DY0-001 national certification exam.
This course includes access to the official CompTIA course content, official CompTIA hands-on live lab cloud content, and official CompTIA practice certification exam.
Course Outline:
Lesson 1: Illustrating the Data Science Lifecycle
In this module, you will explore several key data lifecycle frameworks, including the stages of workflow, and the tools and best practices that support them. The ability to perform data lifecycle management is crucial in order to successfully take data projects effectively from inception to completion. This knowledge will equip you with a structured approach to handle data projects, ensuring that each phase is executed efficiently. Building on this foundation, you will delve into the tools and best practices that enhance each stage of the lifecycle. You will discover various software, documentation, and syntax rules that streamline data management and analysis, ensuring high-quality outcomes. By integrating lifecycle frameworks with the right tools and best practices, you will be able to manage the entirety of a data project more proficiently. This holistic approach will give you the foundation you need to understand more advanced data science concepts.
Lesson 2: Analyzing Business Problems
In this module, you will learn how to select the appropriate solutions for various data challenges that exist in the business world. This involves assessing the needs, wants, and realities of a business idea in order to determine the best fit for your specific situation. Solutions should be evaluated in order to ensure your selection is one that will optimize performance, efficiency, and scalability. This knowledge will empower you to make informed decisions that align with your project goals and organizational requirements. Equally important, you will delve into the critical topics of data privacy and security. In today’s data-driven world, safeguarding sensitive information is paramount. You will learn best practices for masking data, ensuring compliance with regulations and mitigating risks associated with data breaches. By understanding the importance of data privacy and security, you will be better equipped to implement solutions that not only meet your analytical needs but also uphold the highest standards of data protection. Together, these topics will provide you with a comprehensive skill set to analyze business problems.
Lesson 3: Collecting Data
In this module, you will learn about the various factors to consider when collecting data. Recognizing the type of data you’re collecting is crucial for any data-related task, as it has downstream ramifications when manipulating, analyzing, and working with data. This knowledge will help you make informed decisions about how to handle and interpret data in various contexts. Building on this foundation, you will delve into the practical aspects of storing and manipulating data in order to ensure that your data is ready to be processed efficiently. You will recognize how the format of the data affects the way you handle it, as well as consider the workflows, compression techniques, and operations that can be performed to meet your needs. By understanding both the considerations of data and the methods for storing and manipulating it, you will be well-equipped to manage data effectively and derive valuable insights from it. These interconnected topics will provide you with a comprehensive skill set for collecting data in a professional setting.
Lesson 4: Cleaning and Preparing Data
In this module, you will learn the essential skills of data wrangling, which involve cleaning, transforming, and/or reducing raw data into a usable format to prepare it for analysis. You will explore techniques for encoding, preprocessing, and augmenting data to meet your analytical needs. By mastering these skills, you will be able to ensure that your data is accurate, consistent, and ready for meaningful analysis, setting a strong foundation for any data-driven project.
Lesson 5: Describing Data Features
In this module, you will begin by exploring the basics of time series, a crucial concept for working with data collected over time. You will learn how to identify patterns, trends, and seasonal variations in time-dependent data, enabling you to make informed predictions and decisions. This knowledge will equip you with the skills to analyze various types of time series data, providing valuable insights into temporal dynamics. You will also learn to identify and address common issues in data. Real-world data often comes with challenges ,such as misalignment, outliers, and insufficient features that can affect the accuracy and reliability of your analysis. By understanding both time series and common data issues, you will be well-prepared to describe data features and derive meaningful insights from them.
Lesson 6: Exploring Data
In this module, you will embark on a comprehensive journey through essential data analysis techniques, starting with Exploratory Data Analysis (EDA). Using EDA, you will visualize and summarize your data, uncover patterns, spot anomalies, and formulate hypotheses. This foundational skill will give you a thorough understanding of your dataset’s structure, setting the stage for more advanced analyses. Building on the insights gained from exploratory data analysis, you will delve into the application of various statistical tests and methods to draw meaningful conclusions from your data. You will then explore unsupervised learning techniques, which allow you to analyze data without predefined labels. Finally, you will learn to implement clustering algorithms to group similar data points. By integrating these techniques, you will enhance your ability to explore data in meaningful and interesting ways.
Lesson 7: Navigating the Model Selection Process
In this module, you will begin by learning how to optimize the model selection process. You will explore various model design constraints in order to select the most appropriate model based on your data and project goals. Building on this, you will delve into key mathematical areas, such as linear algebra and calculus, which are an essential component of the advanced analytical techniques used to evaluate models. You will also learn to use temporal models for analyzing time-dependent data, enabling you to predict future trends and understand temporal dynamics. Finally, you will address research questions requiring causal explanations, which is another element that can affect your choice of model. By integrating these topics, you will be well-equipped to navigate the model selection process and draw robust explanations from your findings, making these skills highly complementary to advanced data research.
Lesson 8: Employing Machine Learning Methods
In this module, you will begin by exploring the fundamentals of machine learning methods. You will gain a comprehensive understanding of what machine learning is, how it works, and the different types of machine learning methods. This will take you into more specialized concepts, such as looking at metrics and evaluating for model drift. Building on this foundation, you will delve deeper into supervised machine learning, covering some of the most widely used techniques in this critical domain. You will learn how to train models using techniques like linear regression and ensemble learning, as well as others. By mastering these supervised methods, you will be equipped to tackle a variety of real-world problems that come with complex datasets. Together, the lessons in this module will provide a robust skill set for employing machine learning methods and models.
Lesson 9: Experimenting with Deep Learning
In this module, you will begin by exploring the fundamentals of neural network architecture. This foundational knowledge will provide you with a solid understanding of how neural networks are structured and how they can be used to solve a variety of real-world problems. Building on this, you will delve into performing neural network activation functions, which are crucial for determining the output of each node in a network. Knowing how the different kinds of activation functions impact the performance and learning capabilities of these networks will enable you to choose the appropriate activation functions for your specific tasks. Finally, you will learn how to train neural networks and use advanced deep learning concepts. Training involves fine-tuning pieces of the network to minimize errors and improve accuracy – for example, tuning layers and adjusting hyperparameters. These topics will enable you to experiment with deep learning and prepare you for the evolving world of neural networks.
Lesson 10: Evaluating and Refining Data Models
In this module, you will learn how to optimize models and resources, which plays a large role in ensuring that your machine learning models perform as intended. You will begin by learning about benchmarking and the analysis of business requirements, to verify that your model is appropriate for the problem you’re trying to solve. After this, you will delve into different types of optimization problems and how to address them. Additionally, you will learn how to tune hyperparameters, which are critical settings that influence the behavior and performance of machine learning models. Together, these topics will equip you with a skill set for evaluating and refining data models, enabling you and your stakeholders to make data-driven decisions with confidence.
Lesson 11: Communicating for Business Impact
In this module, you will learn how to prepare data in a manner that ensures your data insights are clear, accurate, and actionable—making them accessible and understandable for non-technical audiences. You will also explore techniques for ensuring the quality and integrity of your data outcomes so that you may present data in a way that highlights key findings and supports informed decision-making. Building on this, you will then delve into the art of delivering the data story. You will learn how to craft compelling narratives that make your data come alive and drive impactful decisions. These skills will equip you with the ability to not only analyze data but also to communicate the value of your insights in a way that will positively impact businesses.
Lesson 12: Deploying Data Models
In this module, you will begin by learning best practices for preparing data models for deployment. You will explore various techniques for data replication, an essential skill for ensuring data consistency and reliability across different environments. Building on this, you will delve into deployment methodologies, where you will learn how to transition your machine learning models from development to production environments using CI/CD pipelines. A full understanding of these methodologies will help you minimize downtime and mitigate risks associated with deploying new models. Next, you will learn how to implement ML Ops to monitor workflows, improve testing, and ensure continuous delivery of high-quality models. Finally, you’ll become familiar with the different deployment methodologies. By mastering these interconnected topics, you will be well-equipped to manage the entire lifecycle of machine learning models, ensuring that your deployments deliver consistent value and performance.
Lesson 13: Discovering Specialized Data Science Applications
In this module, you will explore some of the more specialized concepts in the data science industry. These niche concepts might not be a part of every data scientist’s daily life, but their popularity is trending. Thus, it is important to have an understanding of how they work and for what purpose. You will start by exploring the fascinating field of Natural Language Processing (NLP), which enables computers to understand, interpret, and generate human language. Building on your understanding of NLP, you will delve into the realm of computer vision, which enables computers to interpret and process visual information from the real-world. NLP and computer vision facilitate the extraction of critical insights from both text and visual data in everyday scenarios. Graph theory uses links between objects to augment relationships, which you’ll see is particularly useful when combined with machine learning. Additionally, you will learn about techniques for unique events—those that require specialized analytical approaches, such as anomaly detection. By integrating these advanced concepts, you will be well-equipped to address complex data challenges.
All necessary course materials are included.
Certification(s):
This course prepares students to take the CompTIA DataX DY0-001 national certification exam.
