How I Would Become a Data Analyst if I had to Start Over in 2024 | 6 Month Plan
TLDRThe video details how someone with no data analytics experience or knowledge could become a data analyst from scratch in 6 months. The first steps would be researching what a data analyst is, determining what type to become, and learning key skills like SQL, Excel, and Tableau via free YouTube videos or paid platforms. After 2 months of skills building, create projects to showcase abilities and build an impressive resume. Then, work with as many recruiters as possible to get interviews and opportunities. Prepare for technical and general interviews to nail them and land a job offer. While an optimistic timeline, with focus and persistence, a career change to data analytics is achievable.
Takeaways
- 😀 The first step is researching what a data analyst does and figuring out what type of data analyst you want to be.
- 📊 Focus on learning SQL, Excel and Tableau as those skills open up 40% of entry-level data analyst jobs.
- 🎥 YouTube and online courses are good options for learning data analytics skills if you don't have money to spend.
- 📈 After learning skills, build projects to showcase them and create a portfolio website.
- ✍️ Create a resume highlighting your new skills and projects.
- 👔 Work with recruiters to find job opportunities instead of blindly applying online.
- 🤝 Build relationships with multiple recruiters to increase your chances of finding a job.
- 📝 Prepare for interviews by researching the company and practicing technical questions.
- ⏱ With focus and persistence, it may be possible to switch careers in 6 months.
- 🌎 Consider learning cloud and AI skills to future-proof your career in the long run.
Q & A
What is the scenario that Alex describes in the video?
-Alex describes a scenario where he has amnesia and has forgotten everything except that he wants to become a data analyst. He then walks through the steps he would take to become a data analyst again from scratch.
What are the first things Alex would do in this scenario?
-The first things Alex would do are: 1) Research what a data analyst does and figure out what type of data analyst he wants to be. 2) Determine the key skills he needs to learn for that data analyst role.
What are the main skills Alex recommends focusing on first?
-Alex recommends focusing on learning SQL, Excel, and Tableau first. He says these 3 skills will allow you to apply for 40% of data analyst jobs.
Where does Alex suggest learning data analyst skills from?
-Alex suggests learning from free YouTube videos first if you have no money. If you do have some money, he recommends platforms like Udemy, DataCamp, and his own platform AnalystBuilder.
How can you supplement learning technical skills according to Alex?
-Alex suggests building real projects using the skills you learn to solidify them. These projects can then be showcased on your resume.
What methods does Alex recommend for finding job opportunities?
-Alex recommends working with recruiters instead of blindly applying online. He also suggests researching companies, networking on LinkedIn, and cold emailing.
What does Alex say are the two main parts of data analyst interviews?
-Alex states data analyst interviews typically involve a general interview focused on soft skills and a technical interview focused on SQL, Python, etc.
How can you prepare for the technical interview?
-Alex recommends practicing SQL and other technical questions, such as those provided on his platform AnalystBuilder.
What is Alex's optimistic timeframe for becoming a data analyst?
-Alex believes if you focus and work hard at learning, networking, and interview prep, you could become a data analyst within 6 months.
What additional skills does Alex recommend learning if you have more time?
-If you have more time, Alex suggests learning about cloud platforms like AWS and AI/ML tools.
Outlines
😀 How I Would Learn Key Skills and Build a Portfolio
The first step would be researching data analyst roles and determining which skills to focus on. For a generalist role, I would learn SQL, Excel, and Tableau. I could learn these skills in 2 months through free YouTube videos or paid courses. I would build projects to create a portfolio and demonstrate these skills. This would strengthen my resume before applying.
😃 Creating a Resume and Working with Recruiters
With my skills and portfolio, I could create a resume tailored for data analyst roles. I would highlight my projects over limited work experience. Additionally, I would proactively reach out to recruiters rather than blindly applying online. This increases visibility and connects me with insider opportunities.
😄 Preparing for Interviews to Land a Data Analyst Job
To get a job offer, I need to ace the interviews. I would practice telling my story and preparing for technical questions, especially in SQL. With focused preparation, I could potentially transition into a data analyst role within 6 months. In the long run, this career change is well worth the investment.
Mindmap
Keywords
💡Data analyst
💡Skills
💡Resume
💡Recruiter
💡Interview
💡YouTube
💡Udemy/Coursera
💡AnalystBuilder
💡Projects
💡Timeframe
Highlights
Proposed a new deep learning architecture called Transformer that achieved state-of-the-art results in machine translation
Transformer introduced the concept of attention mechanisms in deep learning, allowing models to focus on relevant parts of the input
Showed that attention could be used to replace recurrence and convolutions in neural networks
Demonstrated that Transformer models trained on large datasets achieve excellent results on multiple NLP tasks
Established Transformer as the dominant architecture for natural language processing tasks
Enabled development of large pretrained language models like BERT, GPT-3, T5 that have advanced the state-of-the-art in NLP
Attention and Transformer concepts extended beyond NLP to computer vision, speech, and other modalities
Showed advantages of Transformer over RNNs and CNNs for sequence modeling and transduction tasks
Demonstrated the scalability of attention mechanisms to model long-range dependencies in sequences
Enabled development of transformer models for multimodal tasks combining text, images, audio, etc.
Established deep self-attention as a general computational primitive for capturing global dependencies
Transformed sequence modeling and shifted focus of NLP research to self-supervised pretraining methods
Inspired innovations in model architectures, training techniques, and applications across domains
Fundamentally changed how we build and pretrain neural networks for natural language
Wide adoption of Transformer principles in industry and academia for natural language applications
Transcripts
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