What data communication skills do you need to succeed in Data Science?

When applying data storytelling it's tempting to shape the narrative around the technical details. Instead, try to provide an example centred around a user or customer journey. This allows you to illustrate what the data means, the challenges faced, and the implications and "so what?" for your business and the audience.

Data Scientists often work with cross-functional teams that include non-technical stakeholders such as business leaders, managers, and clients. Effective data storytelling helps bridge the gap between the technical and non-technical audiences. Data Storytelling transforms data and analysis into actionable insights that decision-makers can use to make informed choices.

Data Storytelling is like telling a story to your friends, but you use numbers instead of words. You look at lots of numbers and find out what they are trying to say. Then you show these numbers in a simple and clear way so that everyone can understand. The goal is to help people see what you found out and why it matters. You're like a detective who finds clues in numbers and then tells everyone what you've discovered. This helps people make good choices based on what the numbers are saying.

Data storytelling is like turning boring numbers into a captivating bedtime story for grown-ups, sprinkled with charts and graphs instead of fairy dust, to reveal the thrilling adventures hidden in your data jungle! 📊📚😄

Visualization is like drawing a picture but using numbers instead of colors. Think of Data Visualization as turning a puzzle of numbers into an easy-to-understand picture. Imagine you have a list of how many ice creams were sold every day for a month. It's hard to see any pattern just by looking at those numbers. But if you make a graph, it becomes clear which days had more sales and which days had fewer. You can even use colors and shapes to make it easier to understand, like using red for low sales and green for high sales. It's like giving people a map to help them quickly see the important information. Data Visualization is making data easy to look at, even those who don't like numbers.

Data visualization is an essential component of data science and plays a crucial role in helping data scientists succeed. It provides an effective means to quickly grasp the structure, patterns, and potential outliers in the data. Charts, graphs, and plots can reveal trends and relationships that might not be immediately apparent from raw data. Data visualization helps in formulating and testing hypotheses.

Data visualization is like crafting eye-catching, easy-to-read infographics that turn your data into the superhero comics of the business world. It's where numbers put on capes, bar graphs become skyscrapers, and pie charts turn into delicious slices of insight! 📊💥🦸♂️😄

As I see it, having a strong foundation in data visualization is essential for effectively communicating insights and knowledge to other teams within a company. It serves as a bridge between raw data and actionable insights, making complex information more accessible and understandable. Whether it's identifying market trends, analyzing customer behavior, or presenting performance metrics, the ability to harness data visualization tools and techniques empowers you to contribute meaningfully to your organization's decision-making processes.

Data Scientists work with various data sources, such as databases, APIs, and raw files. Data Literacy helps in comprehending the structure and format of these sources, making it easier to extract and manipulate data effectively. Data Literacy enables data scientists to identify inconsistencies, missing values, and outliers allowing them to prepare high-quality data for modeling. It also helps in generating meaningful visualizations and statistics which can provide valuable insights for modeling and allow them to understand the assumptions strengths and weaknesses of different models, enabling informed choices.

Data literacy is like learning the secret language of numbers, so you can have meaningful conversations with data, instead of awkwardly nodding along at the data party. It's the cool club where you decipher barcodes, not bar talk! 📊🔍🤓😄

I think collaboration is the key skill missing in many data science team, because unlike Software Engineering, Data Science projects often involve a lot of experimentation using Jupyter Notebooks, and collaboration in these experiments is limited. However, given the advancement in Data Science orchestration pipelines, there are many tools to manage and create Data Science repositories, manage model versions and collaborate on data which makes the code quality more robust and more efficient in managing production size projects.

Data and evidence alone will not lead to a successful persuasion. You must understand the KPIs, pain points, challenges and motivations of your target audience. You are unlikely to succeed unless your message resonates with these.

Data Presentation is crucial for several reasons, It helps convey complex insights in a way that is understandable to a broader audience.

Some of the important pillars for Data Science are: - Knowledge in statistics: In this way, you can evaluate and guarantee the quality of analyzes and AI models produced with scientific criteria. - Business knowledge: It is unlikely that we will have AI specialists who dominate all industries, although we can apply data solutions to all of them. In the beginning, it is important to ask and always seek help from people with deep knowledge of the field, although it is important to generalize to any subject, delving into the industries you work or intend to work in will be a differential. Thus, it manages to independently produce increasingly relevant insights for the area.

Some nuanced communication skills are missing from the document: - Reports and Documentation: Documenting data sources, methodology, and results for transparency and reproducibility. - Active Listening: Listen actively to colleagues or clients to tailor your communication to their concerns. - Domain Knowledge: Speak the language of your industry or sector to bridge the gap between data analysis and practical applications. - Simplicity: Use analogies and real-world examples to make data-related ideas more accessible. - Adaptability: Different audiences may require different approaches, whether you're speaking with executives, data analysts, or end-users.

The points above are crucial but two points are not listed: business acumen and mathematics. You cannot get rid of math from data science and you need at least basic business know-how depending on what industry or what business you're working in. Perhaps a simple example would help clarify: let's say you're building a predictive model in geology and you several features at your hand ready to dump into training. You need to understand them in a level that perhaps you will find another one and import it to your environment. Some industries may seem simpler like e-commerce because millions of people are using it but still as you master one industry you get to figure out much detailed know-how to your advantage

To conquer the data science realm, you need the skills of a data wizard: Data Taming Magic: Like a digital Dr. Dolittle, you talk to data in Python or R. Math Mastery: Math isn't just for school; it's your spellbook for statistics and algorithms. Storytelling Charisma: You turn data into tales that captivate both tech wizards and muggles. Coding Sorcery: You write spells in SQL, Python, and maybe even a bit of Java. Domain Wizardry: You understand the secret languages of various industries. Machine Learning Alchemy: You can turn data into predictions like a digital Nostradamus. Curiosity Elixir: Your potion of choice is curiosity; you're always eager to explore new data realms With these skills, you'll be the Merlin of data science!