Tips to become a good product analyst #analytics #interviewtips #podcast #productanalytics

This video, titled "Tips to become a good product analyst," offers succinct yet crucial guidance for individuals embarking on a career in product analytics. The speaker from "Tech Podcast" identifies two foundational skill sets that are absolutely essential for aspiring analysts to master. The primary objective of the discussion is to equip new entrants with a clear roadmap for developing the core competencies required to effectively analyze product data and contribute meaningfully to product strategy.

The first and foremost skill highlighted is a strong command of statistics. The speaker underscores the necessity of understanding fundamental statistical concepts, providing a list of specific examples that include Bayes theorem, the bell curve (normal distribution), standard deviation, probability, conditional probability, probabilistic models, and the interpretation of P-values. This statistical literacy is presented as indispensable for accurately interpreting data, identifying trends, and making data-driven decisions. To facilitate learning, the video points to various accessible resources such as LinkedIn Learning, YouTube, and an IT Metros course, specifically noting a structured "three-step course statistics 1, 2, and 3" available on LinkedIn Learning.

Following the emphasis on statistics, the second critical skill discussed is basic proficiency in SQL (Structured Query Language). The rationale for SQL's importance is explicitly stated: it enables analysts to independently extract and manipulate data. The speaker explains that in the vast majority of contemporary organizations, data is stored within Relational Database Management Systems (RDBMS), making SQL the primary tool for querying and retrieving the necessary information for any analytical task. This logical progression of skills—first understanding the meaning behind data (statistics), then knowing how to access that data (SQL)—forms a practical and effective framework for building a robust analytical foundation.

Key Takeaways:

• Foundational Skills for Product Analysts: The video identifies a strong grasp of statistics and basic SQL proficiency as the two non-negotiable, bare minimum skills for anyone starting a career as a product analyst. These form the essential bedrock for effective data analysis and informed decision-making.
• Mastery of Statistics is Paramount: Aspiring analysts must cultivate a deep understanding of core statistical concepts. This includes critical principles such as Bayes theorem, the bell curve (normal distribution), standard deviation, probability, conditional probability, probabilistic models, and the correct interpretation of P-values, all of which are vital for accurate data interpretation.
• Recommended Resources for Statistical Learning: The speaker suggests leveraging widely available educational platforms for acquiring statistical knowledge. Specific recommendations include LinkedIn Learning, YouTube, and an "IT Metros course on statistics," with a particular mention of a structured "three-step course statistics 1, 2, and 3" available on LinkedIn Learning.
• SQL Proficiency for Data Extraction: Basic SQL knowledge is highlighted as the second indispensable skill. Its primary value lies in empowering analysts to independently pull and query data from databases, a fundamental and recurring task in any data-centric role.
• Understanding Data Storage Architectures: The video points out that most modern organizations store their data in Relational Database Management Systems (RDBMS). Consequently, SQL becomes the essential language for interacting with these systems to retrieve, filter, and prepare datasets for analysis.
• Practical, Hands-On Approach: The emphasis on both statistical theory and practical SQL application underscores a balanced approach to product analytics, where understanding the 'what' (statistics) and the 'how' (SQL) of data are equally crucial for deriving actionable insights.
• Empowerment Through Data Autonomy: By mastering SQL, analysts gain the autonomy to access and manipulate their own data, which streamlines the analytical process, reduces dependencies on other teams, and accelerates the time-to-insight.
• Relevance to IntuitionLabs.ai's Service Offerings: The skills advocated in the video—statistics, SQL, and data extraction from RDBMS—are directly pertinent to the core competencies required for IntuitionLabs.ai's services. These foundational skills are crucial for professionals involved in Data Engineering & Business Intelligence, custom software development, and the development of AI & LLM solutions, as these services inherently rely on robust data handling, analysis, and interpretation.

Tools/Resources Mentioned:

• LinkedIn Learning
• YouTube
• IT Metros course
• RDBMS (Relational Database Management Systems)

Key Concepts:

• Product Analyst: A professional who uses data to understand user behavior, product performance, and market trends to inform product development and strategy.
• Statistics: The scientific discipline concerned with collecting, analyzing, interpreting, presenting, and organizing data.
  • Bayes Theorem: A mathematical formula used to calculate conditional probabilities, showing how to update beliefs based on new evidence.
  • Bell Curve (Normal Distribution): A common probability distribution that is symmetrical around its mean, representing data where values near the mean are more frequent than values far from the mean.
  • Standard Deviation: A measure that quantifies the amount of variation or dispersion of a set of data values.
  • Probability: The likelihood or chance of an event occurring.
  • Conditional Probability: The probability of an event occurring given that another event has already occurred.
  • Probabilistic Models: Mathematical models that incorporate randomness and uncertainty, used to describe the probability of various outcomes.
  • P-value: In hypothesis testing, the probability of obtaining an observed result (or a more extreme result) if the null hypothesis were true. A smaller P-value typically indicates stronger evidence against the null hypothesis.
• SQL (Structured Query Language): A standard language for storing, manipulating, and retrieving data in relational databases.
• RDBMS (Relational Database Management System): A type of database management system that stores data in a structured format, typically in tables, and allows users to access or recombine data in many different ways without reorganizing the database tables.