You want to become a data scientist. What workshops will help you get there?

One thing I would like to add is that Python is a robust language, and while workshops cover basics, becoming an expert requires daily coding practice and patience. Mastery comes over time, with real-world projects and community engagement playing crucial roles in the learning journey. Consistency and commitment are key to achieving proficiency in Python and data science.

Workshops on programming languages like Python and also programming skills like OOP/data structures and algorithms are essential foundations. Workshops on statistics and machine learning builds the core competencies required for predictive modeling and analysis. Practical workshops on data visualization tools (Tableau/PowerBI), enhance the ability to communicate findings effectively. Workshops on database skills, such as SQL and Spark, contribute to proficiency in handling and querying big data. Workshops which target problem solving and story telling skills are equally essential. Further, staying updated with emerging technologies like deep learning or NLP techniques or Generative AI ensures relevance in this dynamic field of data science.

For all the aspiring data science enthusiasts out there, mastering the following concepts is pivotal on the path to become a proficient data scientist. 1. Acquire proficiency in Python Programming 2. Understand the principles of Object-Oriented Programming 3. Gain expertise in SQL for effective data manipulation. 4. Develop skills in utilizing Numpy, Pandas ad Matplotlib, scikit-learn for data analysis and visualization. 5. Understand statistical concepts to enhance data interpretation. 6. Explore and comprehend machine learning algorithms for advanced analytics.

I think there are many free resources on the internet to learn Python but it is difficult to choose a good one. In particular for learning the language from the basics, I think Corey Shafer's YouTube channel is a safe bet.

Contributing to data science articles, I emphasize proficiency in Python and machine learning, backed by hands-on experience with TensorFlow and scikit-learn. My skills extend to exploratory data analysis and visualization using Matplotlib and Seaborn. I bring a solid statistical foundation, ensuring meaningful insights from complex datasets. Familiarity with big data technologies like Apache Spark complements my ability to work with large-scale datasets. My articles showcase expertise in data cleaning, preprocessing, and database management (MySQL, PostgreSQL). Effective communication is a key asset, allowing me to convey intricate concepts clearly. I stay current with industry trends, reflecting a commitment to continuous learning.

Knowing SQL is basic for data science and data analysis in general. Below, I share some resources of interest to learn more about SQL: * Khan Academy - SQL Course. * Codecademy - Learn SQL. * W3Schools - SQL Tutorial. * Coursera - SQL for Data Science. * DataCamp - Introduction to SQL

SQL isn't just a workshop; it's a journey where you decode the language of databases and emerge as a data scientist, armed with the power to transform raw information into informed decisions Each SELECT statement becomes a spell, revealing patterns and secrets hidden within tables. JOINs transform into bridges connecting disparate data realms, crafting a narrative of relationships. GROUP BY and aggregate functions orchestrate symphonies of summary statistics. WHERE clauses become gatekeepers, allowing only the most relevant data to enter your realm of exploration. With every workshop keystroke, you evolve from a novice querying data to a seasoned sorcerer sculpting actionable insights.

SQL stands for Structured Query Language and used to manipulate data in relational databases. Attending SQL workshops and doing small projects can clear a lot of doubts. Your 50% work is done. For the next 50% you can jump on YouTube and learn by yourself. Here are some channels: 1. Alextheanalyst 2. Khan Academy 3. FreeCodeCamp 4. Programming with Mosh

To become a data visualisation specialist one should not be afraid of learning the new age AI tools that makes the entire outcome so augmented that a deep level insight builds becomes a cakewalk.

You can't be great data scientist if you can't explain your findings. And the best way to understand the data is visualisation. The most important tools for this are : Tableau and PowerBI. Attending Tableau or PowerBI workshops can be the best way to learn it. You really are going to enjoy making dashboards, playing with columns and finding relations between different parameters. Tableau provides a lot of tools and freedom while PowerBI provides integration with MS tools as Azure workspace.

I began my data visualization journey with Tableau, exploring various chart types to gain proficiency. Afterwards, I delved into popular Python libraries like Matplotlib, Seaborn, and Plotly, broadening my visualization skills beyond Tableau's scope. However, it's not just about starting with Tableau; Power BI holds significant importance in the realm of data visualization, offering a powerful suite of tools for analysis and presentation. Both Tableau and Power BI provide unique advantages and cater to different user preferences, making them essential tools in the field.

From my experience, to learn Tableua, I recommend the following sites: * Tableau official website. * Coursera - Data Visualization and Communication with Tableau" from Duke University. * Udemy. * Official Tableau YouTube Channel. *Tableau Forums on the official site. * Stack Overflow - Tableau. * Books:"Tableau Your Data!" by Dan Murray and Christian Chabot is a popular book for learning Tableau. * Events and conferences. * Social networks: Follow hashtags like #Tableau on Twitter and LinkedIn to stay up to date with the latest news, tips, and resources.

At the heart of Data Science is the art of transforming a business problem into a mathematical model, working hand-in-hand with stakeholders to refine your approach, and developing systems that effectively address these challenges. But remember, that's only half the journey. The other half? Bringing your AI system to life in the real world, making it a part of the daily fabric of your organization or customer experience. This is where your technical skills meet the art of communication - through data visualization and creative storytelling. To embark on this transformative path, invest in workshops that hone not only your analytical skills but also your ability to visualize and narrate data.

Tensorflow and Pytorch either might be learned. They both help us to create and train neural networks. In my opinion it is not a must requirement in every job, but may improve understanding in data science to know this

Initially, as a beginner, I began with Keras due to its higher level of abstraction compared to TensorFlow, which made it seem less intimidating. Later, I delved into PyTorch. In my experience, Keras felt more user-friendly than both TensorFlow and PyTorch. However, in terms of speed, the trade-off is between TensorFlow and PyTorch. I found PyTorch slightly easier to grasp than TensorFlow, but this might vary based on individual preferences and prior exposure to these frameworks.

Delving into machine learning with TensorFlow was a pivotal point in my data science journey. This workshop demystifies the complexities of machine learning, guiding through problem definition, model selection, and optimization. TensorFlow's versatility shines, empowering tasks from image processing to natural language tasks. Practical implementation of algorithms like linear regression and neural networks enhances hands-on skills. This workshop equips you to confidently construct and deploy machine learning models, unlocking doors to innovative applications in the dynamic realm of data science.

To learn TensorFlow, you can use a variety of online resources. Here are some options: * Official TensorFlow Documentation * TensorFlow - Online Courses: * Udacity - Intro to TensorFlow for Deep Learning. * YouTube - Official TensorFlow channel on YouTube. * Official TensorFlow repository on GitHub.

I prefer PyTorch, but as for now Keras 3.0 started to support both backends - TensorFlow / PyTorch (and others) - for models written the same way. The ability to formulate your own loss function is the key in understanding how to use neural nets effectively.

Data ethics and privacy are critical aspects in the field of technology and information. Ethics refers to the moral principles and values that guide data management, ensuring responsible decision-making and practices. On the other hand, data privacy focuses on protecting personal information, ensuring that the collection, storage and use of data is done securely and respectful of individual rights. These issues are essential in the digital age, since inadequate data management can have negative consequences for society and individuals, highlighting the importance of establishing solid ethical regulations and practices in the processing of information.

To excel as a data scientist, go beyond workshops and embrace a mindset of curiosity, problem-solving, and continuous learning. Cultivate critical thinking, effective communication, and attention to detail. Foster collaboration, persistence, and ethical awareness. Business acumen is very important to connect data insights to business goals. A data scientist with a strong understanding of the business context can provide more impactful and actionable recommendations, thus realising the best potential of data science.

One of the key skills that people underestimate is 'Communication'. It is one of the main reasons a lot of data science projects fail to get adopted in the industry. As a data scientist, you will be faced with situations where you have to communicate the technical aspects of your work to non-technical audiences. Story telling with data is a powerful skill anyone should master to be successful in the professional world.

Gain proficiency in cloud computing platforms like AWS, Azure, or Google Cloud. Many data science tasks are performed on the cloud, and familiarity with these platforms is valuable.

Para la aplicación de ciencia y análisis de datos dentro de una organización; la puesta en valor del análisis de datos es directamente proporcional a la colaboración y asociatividad de los actores que interactúan en el territorio y/o con los datos.

Cognitive services become your enchanted tools, unraveling the mysteries of AI. Vision APIs unveil the art of image recognition, while NLP models transform text into poetry. You orchestrate sentiment analysis symphonies and conduct emotion detection harmonies. Azure Cognitive Services, your companion, empowers you to sculpt applications that understand, interpret, and respond like sentient beings. In these workshops, you transcend from a data scientist to a cognitive sorcerer, weaving algorithms that comprehend the intricacies of human-like perception. It's not just a tutorial; it's a transformative journey where you become a data magician, wielding the magic of cognitive services to enchant the world of artificial intelligence.