You’re considering a career in data science. What are some common misconceptions you should be aware of?

Many people misunderstand what data science is really about. It's not just coding! Data scientists need a variety of skills, including statistics and explaining things clearly. They don't have to be IT wizards, but they should work well with IT people and focus on specific areas of expertise within data science. Cleaning data is important, but it's just one part of the job. Data scientists also analyze data and build models. Even though data models can make predictions, they aren't perfect. Data isn't always perfect either, so aiming for a flawless model isn't realistic. Data science is a never-ending process where you constantly improve and refine your work.

Well my dear Data Enthusiasts, Data Science isn't all about coding. While coding is an essential aspect because of machine learning algorithms and other stuff (don't drag me, I'm a run away Data Scientist ??)....that enables you understand your data patterns better and make more accurate predictions, it also requires you to be an Analyst. One of the reasons I decided to learn Data Analysis proper, was to become a better Data Scientist. Before the code, you must understand the data, its features, and how to handle them without "Bias". Data Analysis is most times "low to no code" Analysis. Without proper analysis of your data, you won't be able to solve any problem, which leaves your codes baseless.

This misconception highlights the need for a holistic approach to data science. Balance your technical skills with a strong understanding of the problems you're solving. Diving deep into the domain you're working in can significantly enhance the value you bring as a data scientist. Remember, the goal is to derive insights that inform decisions, not just to code. Cultivate a curiosity about your data's story and the impact of your analysis. By doing so, you ensure that your work is not only technically sound but also meaningful and actionable for stakeholders.

On se dit les choses, savoir coder est nécessaire pour tout data scientist. Cependant, il faut d'abord se concentrer sur les langages de programmation spécialisés en data science, notamment Python et R. Il est primordial de ma?triser les bases avant de se consacrer aux bibliothèques d'analyse de données telles que pandas, matplotlib, scikit-learn, TensorFlow, entre autres, pour Python. Cependant, le codage n'est pas la seule compétence requise. Il est essentiel de développer ses compétences en résolution de problèmes, ce qui permet d'aborder tout problème business et d'apporter des solutions adaptées. Cela se développera grace à des projets personnels intéressants qui nécessiteront l'application de toutes ces compétences.

Coding could be an important part of Data Science, but it is not the only thing that you have to do. There are a lot of other things to consider. Basically, to become successful in this career, you need to be good enough in multiple things. For example, you need to be good at understanding and working with data, solving complex problems, understanding the business you’re in (MOST IMPORTANT), and explaining your findings to others in a clear way. You also need to keep learning new things and be responsible when using people’s data. These are all important for a successful career as a Data Scientist.

This article highlights a common misconception in the perception of data science, emphasizing that machine learning is just one part of this vast and multifaceted field. When tackling data science projects, it's crucial to have a holistic understanding that includes exploratory data analysis, data cleaning, and result interpretation, in addition to applying machine learning algorithms. Always assess the problem as a whole and consider all available tools and methods before deciding on an approach. This will not only enrich your data science practice but also enable you to deliver more robust and meaningful solutions.

J'ai généralement constaté que les profils Juniors viennent à la Data Science par la porte du Machine Leanring (ML). C'est vrai que le ML fait partie des techniques les plus utilisées mais la data science est beaucoup plus vaste que cela. Pour moi, cette discipline englobe aussi quelques mathématiques de base comme l'algèbre linéaire ou l'analyse, l'optimisation, etc. Par ailleurs, une grande partie du travail de Data Scientist consiste à formuler les questions avant même de les résoudre. D'autres compétences non techniques sont nécessaires pour réussir : communication et l'esprit de synthèse par exemple.

When people talk about data science, they often lump it in with machine learning. But here's the thing: machine learning is just one piece of the data science puzzle. Sure, it's cool – making algorithms that learn from data to predict stuff – but data science goes way beyond that. It's about wrangling data, cleaning it up, exploring it, visualizing it, and figuring out what it all means. Sometimes, simpler methods work just fine without needing fancy machine-learning tricks.

In my experience, the misconception that data science is solely machine learning (ML) could pose significant challenges for aspiring data scientists. This misconception might obscure the fact that data science fundamentally revolves around data itself. In reality, a substantial portion of a data scientist's time is devoted to data collection, preprocessing, and management, tasks that demand strong reasoning skills and programming proficiency. Mastering these foundational aspects is essential for data scientists to be effective. Once these foundational skills are honed, delving into the realm of ML becomes a joyful task for the data scientist.

I would respectfully disagree with the statement that "Data science is the same as machine learning." While machine learning is an important subset of data science, data science is a broader field that encompasses various disciplines, including data collection, data cleaning, exploratory data analysis, statistical modeling, machine learning, and more. Data science involves the entire process of deriving insights and value from data, which includes understanding the problem domain, formulating hypotheses, designing experiments, and communicating findings. While machine learning is a powerful tool within data science for building predictive models and making data-driven decisions, it is just one component of the overall data science workflow.

De nombreuses écoles existent sur le domaine, surtout que trouver un data scientist était très compliqué y'a 5 ans par exemple. Ces formations sont parfois très courtes (quelques mois de cours suivis d'un stage). Cependant attention ! Elles peuvent vous permettre d'avoir les bases de la data science et d'être data analyst mais data scientist demande des connaissances supplémentaires qui prennent du temps à acquérir. Aujourd'hui le marché est inondé de "data scientists" auto proclamés et sortir du lot nécessite un gros travail à c?té de la formation... Par contre pour data analyst, ces formations courtes sont très bien !

??It is not the degree that is required; rather, it is the degree of commitment and passion towards the field. ?? Solving real world Data Science problems requires diverse backgrounds and experience. ? I landed in this field 19 years ago with a background in linguistics and oriental subjects. It took continuous learning and support from mentors to reach where I am today. ??Always try to learn new topics and concepts emerging in this area. Never compromise on learning and practicing.

This misconception highlights a barrier many feel when entering data science, emphasizing that practical skills often outweigh formal education in this field. From my experience, a diverse background can bring unique perspectives to data science projects. It's crucial to focus on building a strong portfolio and continually learning new skills through available resources. Employers value problem-solving abilities, creativity, and the capacity to translate data insights into actionable recommendations. Don't let the lack of a Ph.D. deter you; your passion and dedication to the field are what truly matter.

Data science goes beyond technical knowledge. From my experience, the main benefit of pursuing postgraduate studies was refining problem-solving skills and overall autonomy. However, I believe that dedication, a solid understanding of mathematical concepts behind algorithms (assumptions), and the practical application of technologies to real-world problems are the key factors for success in data science.

False as it can be, as long as you have a drive to learn, and good set of basics in stats, modelling, and coding; you don't need a PhD (I'm a B.Sc. and yet have thrived in the field, and know of many more cases like me).

This misconception sheds light on the less glamorous but equally critical aspects of data science. The truth is much of data science is about the groundwork - cleaning data, understanding its structure, and experimenting with different approaches. While it might not always be thrilling, these steps are fundamental to building solid, impactful solutions. My advice? Embrace the process. The satisfaction of solving a complex problem or making data "speak" after meticulous preparation is unmatched. Remember, the journey, with all its ups and downs, is what truly enriches your skills and experience in data science.

Nobody snaps their fingers and has a perfect model on their hands. Great data scientists spend a lot of time grinding. Well fit models come from iteration. Most of a data scientist's time is spent on data collection, data preparation and feature engineering, because that's what can have the largest impact on taking a model from good to great. It is often not glamourous, but the results are quite rewarding.

Verdades difíceis de absorver: Você passará grande parte do seu tempo, limpando e validando dados. Acostume-se com isso, fique muito bom nisso e dessa forma poderá fazer mais rápido e ter mais tempo para fazer análises.

While data science can be exciting and glamorous at times, it is not always the case. The field of data science involves a lot of hard work, including data collection, cleaning, analysis, and interpretation, which can be time-consuming and challenging. Additionally, not all data science projects result in groundbreaking insights or glamorous outcomes. Many projects involve routine tasks or incremental improvements that may not be particularly glamorous. However, the potential to make significant discoveries, solve complex problems, and contribute to cutting-edge research can make data science an exciting and rewarding field for those who are passionate about it.

The type and quality of work done at an organization comes down to it's vision, mission and values. If it is not sufficiently data driven, without seats at the table for data leaders, there is bound to be a poor understanding of what work a data scientist should be doing. Such an approach may lead to over or under hiring, focusing on lower priority tasks due to poor project management, and lack of agility in project implementation. This leads to a non-ideal workplace, where the skillsets of data scientists are not put to their best use. While data science is challenging- involving much number crunching, it can be a rewarding career especially in an organization that values that effort.

This misconception overlooks the inherently collaborative nature of data science. Emphasizing teamwork, communication, and the merging of diverse expertise is crucial for success in this field. From my experience, the synergy between data scientists, business stakeholders, and technical teams often leads to the most innovative solutions and impactful insights. Nurturing a collaborative environment not only enhances project outcomes but also fosters a learning culture where everyone can grow. Remember, the ability to work well with others and communicate complex ideas clearly is as valuable as technical proficiency in data science.

Data science is not inherently a solo activity. It often involves collaboration among professionals from various disciplines, such as computer science, statistics, mathematics, and domain expertise. Teams of data scientists, data engineers, domain experts, and business analysts often work together on projects, each contributing their expertise to different aspects of the project. Collaboration is essential for successful data science projects, as it allows for diverse perspectives, skill sets, and knowledge to be combined, leading to more comprehensive analyses and better insights. While individual data scientists may work on specific tasks independently, the overall process of data science is typically collaborative and involves teamwork.

Data science is inherently collaborative. Successful data science projects typically involve teams consisting of data scientists, data analysts, data engineers, and business stakeholders. Collaboration is essential for gathering domain knowledge, understanding the context of the data, defining project goals, and interpreting the results. Moreover, different perspectives and expertise contribute to more robust analyses and innovative solutions. Effective communication and teamwork are integral to the success of data science projects, as they ensure that insights are accurately interpreted, actionable recommendations are made, and the value of data-driven insights is realized across the organization.

La science de données est bien une activité collaborative qui nécessite d'avoir une meilleure vue pour explorer la plupart des paramètres pouvant aider à résoudre un problème business ou pour mieux conna?tre sa clientèle. Il convient de travailler en équipe sur les projets pour de meilleurs résultats

Sin dudas que este campo requiere de colaboración en equipo. Mi profesión es médico especialista en ortopedia y traumatología, y para poder investigar, trabajar y ejecutar algún proyecto de ciencia de datos he requerido de especialistas en tecnología para poder cumplir los objetivos de mi investigación.

Data science isn't a magical fix for every problem under the sun. You can't just toss any old data into the mix and expect miracles. It takes serious brainpower to sift through the noise, ask the right questions, and come up with meaningful insights. Data science isn't a one-size-fits-all deal; it's more like a versatile toolbox.

Believing that data science is a one-size-fits-all solution is a bit like assuming one style of shoe fits everyone perfectly. Each problem is different and needs its own special treatment. Data science is more like a versatile toolbox. It's about picking the right tool for a job, just like choosing the right shoe for the occasion.

Data Science isn't just coding; it involves business understanding, statistics, visualization, and storytelling. Data scientists often focus on data cleaning and exploration, not just complex machine learning. Data Science differs from Data Analytics; the former uses statistical analysis, machine learning, and processing for predictions, while the latter focuses on immediate insights. Mastering every tool isn't necessary; proficiency in key tools like Python, R, SQL, and statistics is more valuable.

La science de données accompagne la définition de stratégies et la mise en place d'actions marketing plus performantes et mieux ciblées pour plus d'efficacité et d'efficience. Elle permet de penser différemment pour une meilleure atteinte des objectifs

A ciência de dados é uma ferramenta poderosa para extrair conhecimento e insights a partir de dados. No entanto, para resolver problemas de negócios de forma abrangente, é essencial combinar conhecimentos de várias áreas, incluindo o entendimento do domínio, habilidades técnicas além da ciência de dados, comunica??o eficaz, ética e responsabilidade, e o uso de ferramentas de inteligência de negócios (BI). Integrar esses elementos é fundamental para obter resultados significativos.

When considering a data science career, understand common misconceptions: It's more than coding, involving statistics, business insight, and communication. Advanced degrees are helpful but not mandatory; practical skills matter. The role isn't just about complex algorithms but also about data cleaning and preparation. Data scientists often collaborate across teams, debunking the myth of solitary work. Projects typically begin with uncertain questions, requiring iterative analysis, not immediate clear answers. Recognizing these points helps align your career expectations and approach in data science.

Entrar en el área de la ciencia de los datos en ocasiones puede ser abrumante, además, presenta muchas barreras a partir de falsas creencias que se han hecho alrededor de la misma. Creo que al iniciar en la ciencia de los datos se debe hacer desde conocer que es la Big Data y las posibilidades que existen para su análisis y usos, y de ahí crear un camino que nos lleve sus diferentes áreas, como es la programación, bases de datos, visualización, negocios, entre otras. Al final todas estpan conectadas y se deben conocer, pero también podemos especializarnos en unas al inicio y luego en trayecto ir creciendo en lo que mejor nos resulte.

Machine Learning and Data Science are different verticals. Data Science is mostly about bridging the gap between Business and Data using Tech. Machine Learning is more mathematical in nature. And it is a complex technique. It is a different career option and has a different path.

A common misconception: All models are either ML based, Deep Learning based or Generative AI based. Sometimes as a Data Scientist one must go back to 1997, reminding oneself of when an "Expert System" or Rule based AI, called IBM Deep Blue beat Gary Kasparov a GrandMaster at Chess. Yes, sometimes an old-school Rule-Based system is the way to go. I am speaking from personal experience, where I built a Rule-Based AI system that now saves our clients over $500,000 a year. And yes, no ML/DL technique was possible because of many factors (details too long to explain here). Hence my suggestion is to use First Principles thinking and use every tool in the toolbox even if it's using a 100 year old approach.

Here are other misconceptions to consider. - Data Scientists just need technical skills. It is true that technical skills are important in Data Science, but success in our field requires domain knowledges as well. Understand the domain to which you're applying technical skills helps in framing the right questions, interpreting data correctly, and delivering insights that are actionable and relevant. No matter how many technical certifications you hold, they have no value if you cannot apply them to solve problems within a specific domain effectively. - More (Bigger) Data always means better models. Allow me here to keep it short. Garbage in. Garbage out. Quality over Quantity.