Quelles sont les meilleures plateformes pour apprendre l’analyse de données ?

After starting my learning journey with DataCamp, I have always been adequately challenged by their learning material that ranges from video tutorials, code along sessions, guided projects, daily practice options, and well informed by their blog posts. Further to this, I expanded my online learning journey with Maven Analytics, where I have now spent a lot of time working on courses and building a portfolio. I have found Maven Analytics courses incredibly well structured, with a user-friendly interface, and quality course content and supporting resources. Maven Analytics keeps their finger on the pulse in the data analytics industry by contributing new relevant courses and promote inclusive learning through their various initiatives.

I've found that while online courses are a fantastic way to dive into data analytics, there's nothing like getting your hands dirty with actual projects and real-world tasks. I've gained so much by diving into practical exercises, connecting with fellow data enthusiasts, and experimenting on my own projects. It's these experiences that have given me insights I just couldn't get from courses alone. It truly deepens your understanding and skills in the world of data analytics. And remember, this hands-on approach doesn't always have to be about work; sometimes, just exploring and experimenting for the sheer joy of learning can be incredibly fulfilling. 🙂

Two platforms I find really useful are Udemy and Datacamp.. The reason I like Udemy is that there are multiple creators fighting it out to be the top of their field whether it be Excel, Python, SQL or Power Bi. Reviews don’t lie. Datacamp on the other hand gathers credentialed professionals in the field along with their own educators to go in deep on a topic. They also provide comprehensive learning paths to cover all angles of a tool. However the most useful aspect is the virtual machines which allows you to get hands on experience with a tool without having to get a license.

In my experience, learning via platforms such as Coursera, Pluralsight, DataCamp, Udemy etc. are a great way build context around the vast array of subjects within technolody, data and analytics, however, they should (where possible) be used alongside practical application. The more you can tangibly apply your learning, the better.

I’m biased because I taught for them but I think General Assembly is good! Their short term classes are a good intro and then the longer term boot camps are more comprehensive.

I highly recommend 'Practical Statistics for Data Scientists: 50 Essential Concepts'. It has become my go-to reference book. This book effectively combines theory and practice, deepening your understanding of classical statistical concepts with ease and simplicity.

Take notes!! Whenever you read anything with the purpose of improving your learning about a particular topic, be sure to make notes to reinforce your learning. I will often read a chapter whilst making notes, and then revise my notes from that chapter before moving on. A few reccomendations for those interested in Data & Analytis: Data Science for Business, Storytelling with Data, and Better Data Visualizations have helped me immensely.

Books are a valuable resource for delving deep into the foundations and advanced concepts of data analytics. They provide comprehensive knowledge and can serve as essential references for professionals in the field. My top recommendations for data analytics books are: 1. "Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking" by Foster Provost and Tom Fawcett 2. "The Art of Data Science" by Roger Peng and Elizabeth Matsui

Following are the best books to learn Data Analytics. "The Data Warehouse Toolkit" by Ralph Kimball and Margy Ross: A classic resource for understanding data warehousing concepts and design. "Python for Data Analysis" by Wes McKinney: This book is a practical guide for using Python and its libraries for data analysis, especially with pandas. "Storytelling with Data" by Cole Nussbaumer Knaflic: Focuses on the art of data visualization and effective communication of insights.

Similar to reading books, if you are listening to a technical podcast or watching something education then take notes. Youtube is a free university, full of insight and learning; but be sure to fact-check along the way.

Eu assisto vídeos de análises de dados quase que todos os dias pelo YouTube, e não só vídeos que apresentam códigos e simulações de projetos, mas também os que abordam sobre dicas de carreiras, rotinas de um analista ou de qualquer conteúdo paralelo!

As an avid walker and data enthusiast, I enjoy learning via podcasts. My favourite data podcast is "The Data Scientist Show" by Daliana Liu. As someone who's new to the field, I find it to be a good balance of digestible and challenging content. Would highly recommend!

Joining communities and forums is the best way to learn and help other in Data Science or even in any field. However, following forums are the best in data analysis field. Stack Overflow: An invaluable resource for getting help with coding and data-related questions. Active participation in the community can aid learning. Towards Data Science (on Medium): A publication on Medium that features articles on various data science and analytics topics.

I prefer live instructor-led training for data analytics because it offers hands-on experience with real-life dynamics. Interacting with an instructor and engaging in practical exercises helps bridge the gap between theory and application, preparing you for real-world challenges in the field...

Incorporating microlearning through phone apps like Mimo is a valuable and flexible approach, especially for busy schedules. They provide the flexibility to learn anytime, anywhere. Take advantage of short breaks, commuting time, or any free moment to engage in quick, focused lessons on data and analytics. Apps like Mimo break down complex topics into bite-sized lessons, making it easier to grasp one concept at a time. This approach supports better retention and understanding.

Eu sempre reforço que o único caminho possível é o estudo e a prática. Sim, praticar sempre! Aprender sobre dados não é um caminho tão linear e na verdade, muitas vezes parece até uma montanha-russa... Mas a disciplina, a insistência e a prática constante é o caminho para um aprendizado consistente. É importante entender também que cada um tem a sua forma de aprender melhor e quando você estiver seguindo com os seus estudos, precisa aprender a sua forma de aprendizado para suas horas de estudo serem mais produtivas.