How I'd Learn Data Analytics in 2024 (If I Had to Start Over)

CareerFoundry
12 Aug 202214:08
EducationalLearning
32 Likes 10 Comments

TLDRIn this insightful video, Tom, a senior data scientist at CareerFoundry, shares his personal journey and offers advice for anyone starting a career in data analytics in 2022. He emphasizes the importance of learning on the fly, considering the rapid evolution of the industry, and suggests online schools as a flexible and cost-effective alternative to traditional education. Tom also highlights the value of building a project portfolio and networking, while providing a lean roadmap for beginners that includes working with data, learning tools like Excel and Python, understanding statistics, visualizing results, and finding a passion in the field. He encourages viewers to take advantage of free online resources and to be confident in their learning journey.

Takeaways
  • 🌟 There's no one-size-fits-all approach to learning data analytics; everyone has their own style and path to success.
  • 📚 The data analytics industry is rapidly evolving with more online content and professionals than ever before.
  • 🌐 Networking opportunities have increased due to the growing number of people working in data analytics.
  • 🛠️ Various sectors are now utilizing data analytics techniques, opening up diverse job opportunities.
  • ☁️ Cloud resources and online tools have made it easier to manage data analytics infrastructure without needing to install software locally.
  • 🤖 Machine learning techniques, while not essential, can enhance a data analyst's capabilities and are increasingly accessible online.
  • 🎓 Pursuing a Master's in computer science or a related field can provide a solid foundation for a career in data analytics.
  • 💡 Self-teaching offers the freedom to learn at one's own pace and stay updated with the latest trends, but requires high motivation.
  • 🏫 Online schools offer a flexible and often cost-effective alternative to traditional education, with varying levels of human interaction.
  • 📈 Building a portfolio of projects is crucial for job applications, showcasing practical experience and skills in data analytics.
  • 🚀 Gaining actual work experience through internships or entry-level positions is highly valuable and recommended over extended formal education.
Q & A

  • What is Tom's current position in the data industry?

    -Tom is currently working as a senior data scientist at CareerFoundry.

  • What does Tom suggest as the best way to learn data analytics in 2022?

    -Tom suggests enrolling in an online school as the most effective way to learn data analytics in 2022, due to their flexibility, varied pricing, and the opportunity to quickly gain practical skills.

  • What are some advantages of the data analytics industry evolving rapidly?

    -The rapid evolution of the data analytics industry has led to an increase in online content, more job opportunities, a wider range of sectors utilizing data analytics techniques, and the availability of cloud resources and free machine learning tools.

  • How does Tom recommend building a portfolio for job applications?

    -Tom recommends starting with a core project based on a passion area, and then adding a few side projects related to that theme or exploring other topics of interest. He suggests that a portfolio should contain between one to three projects.

  • What are the five components of Tom's lean roadmap for becoming a data analyst?

    -The five components are: 1) Working with data, 2) Learning a tool to work with data, 3) Understanding statistics, specifically descriptive statistics, 4) Visualizing results and telling a story, and 5) Finding an interesting area to perform data analytics on.

  • How long does it take to become comfortable with the skills in Tom's roadmap?

    -It can take anywhere from a few months to up to one year to become comfortable with all the skills in Tom's roadmap.

  • What is Tom's advice on how much math is needed for data analytics?

    -Tom advises that one needs just enough math to get by in data analytics, and it's more about problem-solving and having an interest in math rather than being an expert in algebra or formulas.

  • What tools does Tom recommend for learning and practicing data analytics?

    -Tom recommends starting with Excel, moving on to SQL and Python for handling data, and using visualization tools like Looker, Tableau, Metabase, or Power BI to explain the data. He also suggests taking advantage of free online resources and sandbox environments.

  • How does Tom suggest networking in the data analytics field?

    -Tom encourages reaching out to respected professionals on LinkedIn, joining online communities like subreddits and Discord, and attending hackathons and meetups to build a strong network.

  • What resources does Tom recommend for further learning in data analytics?

    -Tom recommends Medium.com for articles, kaggle.com for real-world datasets, HackerRank for coding challenges, and YouTube for a variety of content. He also mentions CareerFoundry's own free short course on data analytics.

  • What is the estimated time to find a junior data analyst role with dedication and hard work?

    -With dedication and hard work, Tom estimates that it could take around 6 to 12 months to find a junior data analyst role after going through the learning process he outlined.

Outlines
00:00
🚀 Embracing the Data Analyst Journey in 2022

Tom, a senior data scientist at CareerFoundry, shares personal insights on becoming a data analyst in 2022. He emphasizes that there's no one-size-fits-all approach to learning data analytics, and everyone has their own style and path to success. He highlights the rapid changes in the industry, such as the abundance of online content, increased number of professionals in the field, and the expansion of data analytics across various sectors. Tom also mentions the availability of cloud resources and machine learning techniques to aid in data analysis. He concludes by teasing the sharing of free resources useful for beginners in data analytics.

05:00
📚 Learning Pathways and Essential Skills for Aspiring Data Analysts

Tom discusses the three main pathways to become a data analyst: university education, self-teaching, and online schools. He weighs the pros and cons of each method, including time commitment, cost, and exposure to current trends. He advocates for online schools as his preferred approach for starting data analytics in 2022, due to their flexibility, varied course offerings, and career support. Tom also stresses the importance of gaining work experience, building a project portfolio, and understanding the evolving skills needed in data analytics. He shares his lean roadmap for beginners, which includes working with data, learning a tool, understanding statistics, visualizing results, and finding a passion in the field.

10:04
🌟 Maximizing Potential and Overcoming Challenges in Data Analytics

Tom provides advice on how to excel in data analytics, emphasizing the importance of hands-on experience, building a strong portfolio, and learning the necessary skills such as working with data, using tools like Excel and SQL, understanding statistics, and storytelling through visualizations. He encourages setting personal learning goals and taking advantage of free online resources and tools. Tom also suggests studying others' work, networking with professionals, and participating in online communities and events. He concludes by offering a list of resources for further learning and practice, including Medium, Kaggle, HackerRank, and YouTube, and encourages viewers to subscribe for more content.

Mindmap
Keywords
💡Data Analyst
A data analyst is a professional who collects, processes, and interprets data to help organizations make decisions. In the video, the speaker shares insights on how to become a data analyst, emphasizing the evolving landscape of the field and the importance of continuous learning.
💡Data Science
Data science is a field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. In the context of the video, the speaker's background in data science includes the study of data analytics, which is a crucial component of his expertise.
💡Online Learning
Online learning refers to the acquisition of knowledge and skills through the internet, often through interactive courses and resources. The video highlights the abundance of online content and the opportunity to learn data analytics through various online platforms.
💡Cloud Resources
Cloud resources are services made available from remote servers over the internet, including databases and visualization tools. These resources enable users to manage and analyze data without the need for local software installations. In the video, the speaker mentions the convenience of cloud resources for data analysts.
💡Machine Learning
Machine learning is a subset of artificial intelligence that involves the use of statistical models and algorithms to enable systems to learn from and make predictions or decisions based on data. The video discusses the relevance of machine learning techniques in enhancing the work of data analysts.
💡Online Community
An online community is a group of people who interact and communicate through the internet, often around shared interests or activities. In the context of the video, the speaker emphasizes the value of engaging with like-minded individuals in the field of data analytics through online platforms.
💡Portfolio
A portfolio is a collection of projects or works that showcase an individual's skills and accomplishments. In the field of data analytics, a portfolio is crucial for job applications as it demonstrates practical experience and expertise.
💡Career Development
Career development refers to the process of advancing one's professional skills and knowledge to progress in their career. The video discusses the importance of career development for data analysts, including the support provided by career specialist teams at universities and online schools.
💡Industry Evolution
Industry evolution refers to the changes and advancements that occur within a particular field over time. The video highlights the rapid evolution of the data analytics industry, with new tools, techniques, and sectors adopting data analytics practices.
💡Learning Path
A learning path is a structured plan or sequence of educational experiences designed to help an individual acquire specific skills or knowledge. In the video, the speaker shares his personal learning path and suggests ways to find or create a personalized learning path in data analytics.
Highlights

Tom shares his journey from being a newcomer to becoming a senior data scientist, emphasizing that everyone has their own unique path to success in data analytics.

The data analytics industry is rapidly changing and evolving, with more online content and professionals than ever before.

There are more job opportunities and sectors utilizing data analytics techniques, which means a wider range of career options for aspiring data analysts.

Cloud resources and free machine learning techniques are now widely available, enhancing the toolset for data analysts.

Tom's personal journey involved getting a Master's in computer science and working on diverse data projects to become an expert.

For someone starting in 2022, Tom recommends less upfront learning and more on-the-fly learning, suggesting flexibility and adaptability.

Three main pathways to become a data analyst in 2022 are university, self-teaching, or attending an online school.

Online schools offer a balance between structured learning and flexibility, with varying costs and levels of human interaction.

Career specialist teams at online schools and universities can help prepare for job applications, unlike self-taught individuals who must navigate this on their own.

Tom believes that actual work experience is invaluable and suggests prioritizing internships or job opportunities.

A portfolio of projects is essential for job applications, with one to three projects being a good number to showcase.

Tom proposes a lean roadmap for becoming a data analyst, emphasizing practical skills over comprehensive domain knowledge.

The lean roadmap includes working with data, learning a tool, understanding statistics, visualizing results, and finding a passionate area to focus on.

Acquiring the skills in the roadmap can take a few months to a year, depending on the individual's pace and dedication.

Tom encourages setting personal goals and working backward to identify the skills needed to achieve those objectives.

Confidence in oneself and a willingness to make mistakes are important for success in the data analytics field.

Math skills in data analytics should be focused on problem-solving and understanding rather than rote memorization of formulas.

Becoming comfortable with tools like Excel, SQL, and Python is crucial for junior data analysts, as well as learning to communicate findings effectively.

Tom advises taking advantage of free online tools and resources, such as sandbox environments and freemium versions of software.

Studying others' work, networking, and engaging with the online community are recommended ways to enhance one's learning and career prospects.

Tom provides a list of resources for further learning, including Medium, Kaggle, HackerRank, and YouTube, as well as CareerFoundry's own free short course.

Transcripts

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