Summary of I started my data analyst career taking these beginner courses

Main Ideas and Concepts

• Getting Started in Data Analysis

  The video is aimed at beginners interested in pursuing a career in data analysis. It emphasizes the importance of selecting tools that suit individual learning styles rather than following trends.

• Core Competencies in Data Analysis
  • Data Querying: Understanding how to interact with databases.
  • Data Cleaning: Preparing data for analysis.
  • Data Analysis: Using statistical and non-statistical methods to analyze data.
  • Data Visualization: Creating visual representations of data.
• Essential Tools
  • Microsoft Excel:
    • Recommended as a foundational tool due to its prevalence in corporate environments.
    • Key features to learn: sorting, filtering, VLOOKUPs, pivot tables, formulas, and basic data cleaning methods.
  • SQL:
    • Important for querying databases.
    • Essential concepts: relational databases, primary/foreign keys, SQL functions (SELECT, FROM, WHERE, etc.).
  • Tableau and Power BI:
    • Tools for data visualization that allow importing data from various sources.
  • Python:
    • Used for data manipulation and advanced statistical analysis.
    • Learning Python is optional; many data analysts successfully use Excel and SQL.
• Recommended Courses
  • Excel:
    • "Introduction to Data Analysis using Excel" (EDX)
    • "Analyzing and Visualizing Data with Excel" (EDX)
  • SQL:
    • "SQL for Data Science" (EDX)
    • "Intro to SQL: Querying and Managing Data" (Khan Academy)
  • Power BI:
    • "Analyzing and Visualizing Data with Power BI" (EDX)
• Statistical Concepts

  Basic statistics knowledge is beneficial: sampling, frequency distribution, mean, median, mode, standard deviation, etc. Practical application of statistics is emphasized; not all data analysis requires advanced statistics.

• Practical Experience

  Importance of applying knowledge through projects to reinforce learning. Suggested resource: Kaggle.com for datasets to practice with.

Methodology/Instructions

• Learning Path:
  • Start with Microsoft Excel to build foundational skills.
  • Progress to SQL for database querying.
  • Explore data visualization tools like Tableau or Power BI.
  • Optionally learn Python for advanced data manipulation and statistical analysis.
• Practice:
  • Work on real datasets from Kaggle to apply learned concepts and improve confidence.

Speakers/Sources Featured

• Wale (host of the channel)

Notable Quotes

— 06:29 — « I do not want people to feel they have to learn python or r to become data analyst trust me I know data analysts that have worked solely on excel and they are fine. »

— 07:13 — « The bottom line is you can be a data analyst with the simple tools at your disposal. »

— 07:44 — « You must find ways to apply your knowledge in specific projects to deepen your experience and showcase your skills. »

Category

Educational

Video