Module 01 - The Role of Analytics
The first module of the course “Getting Started with Analytics and Data Engineering” consists of 14 videos. During this module, we will get acquainted with the subject of study and learn about key roles in data and what they do, as well as the other names they go by.
Book recomendation for the module: Lean Analytics by Alistair Croll & Benjamin Yoskovitz
Module 1.0 Introduction
The first module in the Surfalytics course, designed for anyone aspiring to work with data or lead data teams, dives into the world of analytics. The module explores the Return of Investment (ROI) and goals of analytics, the architecture of analytics solutions, and the various roles and responsibilities within data teams. It also provides a comprehensive mind map of analytical solutions, tools, and approaches, along with career roadmaps for those seeking to build a successful data career. The module further delves into the agile methodology commonly used by data teams and introduces common data types and formats. Additionally, it offers a hands-on project involving spreadsheets and concludes with a historical overview of the evolution of analytics over the past 30 years.
Video lesson - Module 1.0 - Introduction
Module 1.1 Evolution of Analytics
The 3 videos series on the history of analytics and data engineering provides a captivating journey through the evolution of the field over the past 30 years. Dmitry Anoshin shares his personal insights and experiences, tracing the development of key concepts, technologies, and roles that have shaped the industry. The series delves into the origins of data warehousing, business intelligence, and big data, highlighting the pioneers and their groundbreaking contributions. It also explores the impact of cloud computing, the rise of open-source tools, and the emergence of new paradigms like lake houses and data observability. The lessons offer a unique blend of historical context, technical explanations, and practical advice, making it a valuable resource for anyone seeking to understand the foundations of analytics and data engineering.
Video lesson - Module 1.1 - Part 1 Video lesson - Module 1.1 - Part 2 Video lesson - Module 1.1 - Part 3
Module 1.2 The Role of Analytics in an Organization
The lesson delves into the diverse landscape of data roles, offering a comprehensive overview of the skills and knowledge required for various positions in the field.Dmitry, provides a clear categorization of roles, distinguishing between traditional roles like BI developers and data analysts, data engineering roles, and specialized roles in data science and IT. The lesson emphasizes the importance of understanding the specific responsibilities and skill sets associated with each role, enabling viewers to make informed career choices. It also highlights the core skills and tools that are essential for success in the data industry, regardless of the specific role. The lesson serves as a valuable resource for anyone seeking to navigate the dynamic and ever-evolving world of data careers.
Video lesson - Module 1.2 - The Role of Analytics
Module 1.3 The Data Analytics Goal
In The lesson discussed the fundamental goals of analytics, emphasizing its connection to core business objectives. Dmitry highlights the three primary goals of analytics: increasing revenue, decreasing costs, and mitigating risks. He explains how these goals tie into the broader business context, using real-world examples to illustrate how data-driven insights can lead to improved decision-making and ultimately, business success. The lesson also touches on the importance of utilizing both internal and external data sources to gain a comprehensive understanding of the business landscape and identify opportunities for growth and optimization.
Video lesson - Module 1.3 - The Data Analytics Goal
Module 1.4 Analytics Architecture Framework
The lesson analyzes the architecture of a real-world data analytics framework, moving beyond the limitations of spreadsheets. Dmitry Anoshin introduces a comprehensive framework that encompasses various layers, including the source layer, data processing layer, storage layer, presentation layer, and ML/AI layer. He explains the components within each layer, such as transactional databases, APIs, data warehouses, data lakes, BI tools, and machine learning pipelines. The lesson also touches upon the complementary technical layer, highlighting the importance of version control, CI/CD, cloud platforms, and data observability tools in building a successful analytics solution. The framework provides a holistic view of the data analytics landscape, emphasizing the interconnectedness of different components and their roles in driving business value.
Video lesson - Module 1.4 - Analytics Architecture Framework
Module 1.5 Key Roles in Analytics
The lesson explores the diverse world of data roles, tracing their evolution from traditional positions like BI developers and ETL developers to modern roles such as data engineers and analytics engineers. Dmitry emphasizes the importance of understanding the core value each role brings to an organization, highlighting the need to align technical skills with business objectives. The lesson also explores the dynamic nature of data roles, acknowledging the fluidity and overlap between different positions, particularly in the context of data science and machine learning. It encourages viewers to focus on acquiring foundational skills and knowledge, while remaining adaptable to the ever-changing demands of the data industry.
Video lesson - Module 1.5 - Data Job Roles
Module 1.6 Analytics and data engineering MindMap
The lesson offers a deep dive into the world of analytics through a comprehensive mind map, exploring the various layers of an analytic solution, the roles involved, and the essential skills required. Dmitry provides a visual representation of the interconnectedness of different components within an analytic solution, from data sources and processing to storage, business intelligence, and machine learning. The mind map also highlights the diverse roles in the data industry, ranging from non-technical roles like data analysts and BI developers to technical roles like data engineers and data scientists. Additionally, The lesson emphasizes the importance of soft skills and domain knowledge, underscoring their crucial role in driving successful analytics initiatives. The mind map serves as a valuable resource for anyone seeking to understand the complex landscape of analytics and the various career paths available in this field.
Video lesson - Module 1.6 - Ultimate Mindmap
Module 1.7 - Data Analytics Career Roadmap. Top 20 Skills for Success.
This video shows a comprehensive guide for anyone aspiring to build a successful career in data analytics. The lesson outlines the essential skills and knowledge required for various data roles, including data analysts, analytics engineers, and data engineers. It also provides insights into less conventional but equally rewarding career paths like support engineers and sales engineers. The roadmap emphasizes the importance of both technical and non-technical skills, highlighting the need for a strong understanding of business domains, effective communication, and the ability to drive impactful business recommendations. The lesson serves as a valuable resource for individuals seeking to navigate the dynamic and ever-evolving field of data analytics.
Video lesson - Module 1.7 - Data Analytics Jobs Roadmap
Module 1.8 - Agile for Data Teams
The lesson dives into the practical application of Agile and Scrum methodologies within data teams. It goes beyond theory, offering insights into how these frameworks shape day-to-day operations and project execution. The lesson emphasizes the importance of understanding Agile and Scrum, especially for those entering the data field, as it impacts collaboration, task management, and overall project success. The instructor encourages viewers to reflect on their past experiences and consider how they’ve worked within Agile frameworks, highlighting its relevance for resumes and job descriptions.
Video lesson - Module 1.8 - Agile for Data Teams
Module 1.9 - Data Team Structures
The lesson takes a closer look at the critical topic of data team structures, exploring various models and their impact on efficiency and collaboration. Dmitry, emphasizes the importance of understanding team structures for both newcomers and experienced professionals in the data field. He shares insights into different organizational approaches, including centralized, centralized with distributed teams, and the unique structures found in large tech companies like Microsoft and Amazon. The lesson also highlights the significance of team size, drawing on the “two-pizza team” concept from Amazon, which advocates for smaller, more agile teams. The discussion extends to the roles and responsibilities within data teams, touching on data engineers, data analysts, analytics engineers, and other specialized roles. The lesson concludes with a practical exercise, encouraging viewers to analyze their own team structures and prepare for potential interview questions on this topic.
Video lesson - Module 1.9 - Data Team Structures
Module 1.10 - Common File Types
The lesson provides a comprehensive overview of various file types commonly encountered in the field of data analytics and engineering. Dmitry explains the purpose and characteristics of different file formats, including markdown files (.md), comma-separated values files (.csv), tab-separated values files (.tsv), text files (.txt), JavaScript Object Notation files (.json), Extensible Markup Language files (.xml), Parquet files (.parquet), Avro files (.avro), and Optimization Record Columnar files (.orc). The lesson also covers log files, configuration files (YAML), dot files (.env, .gitignore), Python environment files (Pipfile, poetry.toml, requirements.txt), and code files (Python, shell scripts, SQL, DBT models). Dmitry emphasizes the importance of understanding these file types for effective data handling and analysis, and provides practical examples and insights into their usage in real-world scenarios. The lesson concludes with a demonstration of using Git commands to add and commit changes to a repository, highlighting the importance of version control in data projects.
Video lesson - Module 1.10 - Common File Types GitHub Repository - Common File Types
Module 1.11 - Spreadsheet Analytics
The lesson includes a practical introduction to data analysis using spreadsheets. Dmitry guides viewers through the process of building an interactive dashboard in Excel, using a real-world dataset from Tableau’s Superstore. The lesson emphasizes the importance of understanding data structures, formulating business questions, and utilizing spreadsheet functions to extract meaningful insights. It also highlights the limitations of spreadsheets and the benefits of transitioning to dedicated BI tools for more advanced analytics. The lesson concludes with a discussion of career paths in data analytics and the value of automation and migration projects in showcasing skills to potential employers.
Video lesson - Module 1.11 - Spreadsheet Analytics GitHub Repositiry - Spreadsheets Analytics