What do you do if you want to master data analysis through self-directed learning?

I suggest taking a class on basic statistical reasoning first. Understanding basic concepts such as categorical variables, continuous variables, t-tests, or simple linear regression is mandatory before moving on to more complex statistical procedures. Once you have acquired a basic understanding of those core statistical concepts, you can create a more concrete and tailored path to master data analysis through self-directed learning.

To master data analysis, it is always important to apply the concepts in practical through projects rather than learning the theoretical concepts. Start with Excel and try to perform all required data analysis on some real time data. Next learn Python and try to apply the concepts using Pandas as it requires in the project.Find teams within your organization who works on data analysis, and display your interest in contributing to their work after your regular project work is complete. In this way, you get the opportunity to work on real time data within your organization and start developing the required skills that is needed to master. Finally, its quite important that practice, practice and practice on daily basis to master data analysis.

"Prioritize practice over code/command memorizing" To master data analysis through self-directed learning, one has to engage in hands-on practical analyses. When it comes to data analysis, depending solely on theory can be challenging; we tend to forget what we don't practice. I recommend watching tutorial videos, obtaining a sample dataset, and diving into it from the outset. Instead of attempting to memorize codes, apply them directly to real data. Through practice, we naturally remember the necessary codes. Learning data analysis is an ongoing process, and the more you practice, the more engaging it becomes.

Start with a tool that is accessible to you and widely used, such as Excel or Power BI. Work with free or existing datasets on a topic that interests you and think of ways you would like to visualise or draw insights from that data yourself, it should make deriving logic easier for someone improving their proficiency in said tool. For instance, if you are interested in fashion, download free dummy datasets for a clothing store and derive insights for inventory, merchandise, turnover and admin expenses - all the important elements to run a store smoothly.

Data analysis is independent of any BI tools. Yes, it is highly significant to be aware about the basic Dashboarding tools and scripting languages such as SQL or Python ,however it is equally important to understand the business impact of the data that is being provided to the stakeholders in order to analyse by applying top-down or bottom-up approaches to the use case and provide useful recommendations andactionable insights. A data without insights is like a fish without water!

Após dominar as ferramentas básicas de análise de dados, é crucial explorar técnicas mais avan?adas e aprofundar seu conhecimento em diferentes tipos de dados e algoritmos de aprendizado de máquina. Além dos dados estruturados (como tabelas em bancos de dados), explore também os dados n?o estruturados (como texto, áudio, vídeo). Aprenda como coletar, limpar e analisar esses tipos de dados.

Once you have a handle on basic tools, delve into more complex data analysis techniques. Learn about different types of data, such as structured and unstructured data, and how to handle them. Explore machine learning algorithms that can provide predictive insights from data. Start with simple models and gradually work towards more complex ones. Online courses and tutorials can be particularly helpful during this stage, as they often provide practical examples and projects for you to work on.

Apply your theoretical knowledge to real-world problems through practical projects and case studies. Seek out datasets from sources like Kaggle, UCI Machine Learning Repository, or government data portals, and use them to explore different aspects of data analysis. Engage in end-to-end projects where you collect, clean, analyze, and visualize data to derive actionable insights or build predictive models. Participate in competitions or contribute to open-source projects to collaborate with others and gain exposure to diverse problem domains.

Just start a project. That's the most effective and efficient way to self-learn data analysis. Of course, before you begin, you need to learn the basics such as language, programming, and the process. But once you start preparing for the next step, you need to know what to do next. Starting your own project or using already-done projects by others can provide all the information you need. This way, you can explore everything you need to know, the skills you need to learn, and become familiar with the analysis process.

One effective strategy I’ve frequently used is researching the data analysis methods employed by thriving large corporations. By delving into their success stories, you can glean invaluable insights into their data analysis techniques and how they derive meaningful insights. This not only boosts your analytical prowess but also provides practical knowledge. Leveraging AI tools can significantly streamline your search process, enhancing both the efficiency and accuracy of your quest for these success stories.

By actively engaging with leading data scientists on social media, tuning into industry podcasts, newsletters, and attending professional events, you can stay abreast of new trends and advancements in the dynamic field of data analysis. This approach not only enhances your knowledge but also facilitates networking opportunities with experts and peers, fostering a collaborative and learning-rich environment. Keep exploring, learning, and connecting to thrive in the fast-paced world of data analysis.

Define specific objectives for mastering data analysis through self-directed learning like learning new programming languages or improving statistical analysis skills. Create a Structured Plan: Develop a detailed study plan with milestones and timelines to track progress effectively. Utilize Resources: Explore a variety of resources such as online courses, books, and tutorials to enhance your knowledge. Engage in Practical Projects: Apply your learning through hands-on projects or challenges to reinforce concepts and gain practical experience. Seek Feedback and Support: Share your progress with peers, mentors, or online communities to receive feedback and guidance on your learning journey.

Work in the subject matter you want to analyze. As Steve Jobs points out you can analyze data about a banana but until you actually grow one and taste one you can’t understand bananas. One needs to learn nuances that only come from experience not just the numbers.

I think it's very important to firstly know what you want to learn - focus. This is because there is so so much resource out there and when you are not clear on what you want to achieve, its easy to drown in the resources. Once you do that, start to practice because practice makes improvement! And in your practice, also try to find masters at the 'game' and work through with them.