How I'd Learn to be a Data Analyst in 2024
TLDRThis video discusses the evolving skills needed for data analysts in 2024 during the AI revolution. It covers the core analytical tools like SQL, Excel, Python, R, Tableau, and PowerBI, and how AI advancements like copilots and chatbots are changing these tools to assist analysts, not replace them. The host explores open source AI models as an alternative to closed models, believing AI improves productivity and job performance. He aims to provide tutorials on leveraging AI tools to learn programming languages and enhance data analysis skills.
Takeaways
- 😀 The video covers core data analyst skills and new AI tools to help streamline analytics
- 💡 SQL, Excel, Python and data visualization tools like Tableau remain critical skills
- 📈 New AI assistants like CoPilot and Duet AI can help generate code and speed up workflows
- 😎 Postgres and Python are gaining popularity over MySQL and R for advanced analytics
- 🤖 AI is being used to power calculations in Excel and enhance dashboards in tools like Power BI
- 🧠 OpenAI's ChatGPT dominates AI search tools, but competitors like Google's Gemini aim to catch up
- 🌟 Large language models like Anthropic's Claude power many AI assistants and are seeing major investments
- 🔒 MistAI offers an open-source alternative for using large language models locally for better security
- 📊 Studies show AI augments human capabilities, making analysts more productive and higher quality
- 👩💻 Business leaders see AI assistants expanding capabilities, not replacing human roles like data analysts
Q & A
What are the core skills needed to become a data analyst?
-The core skills needed are: SQL, Excel, coding languages like Python and R, and data visualization tools like Tableau and Power BI.
Why is SQL listed as the most important skill for data analysts?
-SQL is the programming language used to communicate with databases. Since working with data stored in databases is a key part of a data analyst's job, SQL is an essential skill listed in almost half of all data analyst job postings.
Why is Excel overhyped as an analytical tool?
-Companies often use Excel for purposes it wasn't designed for, like managing huge data sets. This overuse of Excel causes it to be overhyped even though it may not be the best tool for complex analysis.
How can Python and R be used to 'flex' on Excel users?
-Unlike Excel which has limitations, Python and R can perform advanced analytics and even machine learning. So data analysts skilled in these programming languages have more advanced capabilities to analyze data.
Why introduce Python functionality in Excel now?
-To make calculations easier in Excel by leveraging Python's capabilities, similar to how chatbots use Python for calculations behind the scenes.
What are the benefits of using AI coding assistants?
-AI coding assistants like GitHub Copilot can speed up workflow, help you learn programming languages more quickly, and autocomplete code as you type it.
Why does the narrator prefer Power BI over Tableau?
-He finds Power BI superior because of its tight integration with Power Query for data transformation and DAX for advanced data modeling.
What is the difference between closed source and open source AI models?
-Closed source models like GPT-3 are proprietary and you pay to access them. Open source models are free to download and modify.
Do business leaders see AI as replacing or assisting human jobs?
-Most see AI as assisting and empowering workers, not replacing them. Over half expect to expand headcounts in areas like data science due to AI.
What evidence shows AI improves human job performance?
-A Harvard study found consultants using AI were 12.2% more productive, completed tasks 25.1% faster, and produced 40% higher quality results than those not using AI.
Outlines
👨💻 Top data analyst tools and AI advances to know
Paragraph 1 discusses the top skills needed for data analysts - SQL, Excel, Python, R, Tableau and Power BI. It also mentions AI advances like GitHub Copilot that can help speed up workflows. The focus is on core data analyst skills plus understanding new AI tools for efficiency.
😎 Using AI to improve SQL and Excel workflows
Paragraph 2 talks about using AI like GitHub Copilot and new Microsoft 365 Copilot to improve workflows for SQL and Excel. It allows for faster SQL querying and analysis in Excel. There is also a new Python integration in Excel to support calculations.
🤖 Choosing between Python vs R and BI tools
Paragraph 3 compares Python and R programming languages, with Python being more versatile. It also discusses Power BI and Tableau BI tools and their new AI capabilities like natural language processing to analyze data more easily.
Mindmap
Keywords
💡data analyst
💡AI revolution
💡coding
💡Microsoft Excel
💡SQL
💡chatbots
💡job automation
💡data visualization
💡machine learning
💡natural language
Highlights
The introduction provides important context and motivation for the research.
The methods section explains the experimental design, materials, and procedures in detail.
Key findings from the results are presented, including statistical analyses and data visualizations.
Limitations of the study are acknowledged, such as sample size constraints.
Connections are drawn between the current findings and prior related research.
Theoretical implications of the results are discussed, such as supporting or challenging existing models.
Practical applications and future directions are suggested based on the conclusions.
The writing is clear, concise, and uses discipline-specific terminology appropriately.
Statistical analyses seem appropriate and are thoroughly explained.
Data visualizations effectively summarize patterns in the findings.
The literature review synthesizes previous work and identifies knowledge gaps.
The conclusion summarizes the main points and emphasizes the significance of the research.
Formatting adheres to disciplinary writing conventions in the field.
Citations are used appropriately to situate the work within the scholarly literature.
The presentation highlights the most significant aspects of the research concisely.
Transcripts
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