Como você pode fazer a transição para uma carreira de ciência de dados com aprendizado de máquina?

Immerse into a passion outside work When you're passionate about something, whether it's cricket, sports, finance, marketing, martial arts, politics, or any other domain, you naturally develop a deeper understanding of its nuances, trends, and challenges. By immersing yourself in your interests, you not only deepen your understanding but also hone your ability to uncover insights that drive real impact. For ex, in cricket, decoding the relationship between strike rate and average helps pinpoint players tailored for the fast-paced T20 format. As you delve deeper into your passion, your statistics, mathematics & programming proficiency naturally evolves, enabling you to decipher results and effectively communicate them to stakeholders.

How can I become a mechanic especially without knowing much about cars? I have a sharp knife and a table, how can I become a surgeon? How you get into data science and machine learning is you do the hard work and graft of learning your trade. If you can program already, thats a great start but its far far from the only skill you will need. Linear Algebra is going to be useful and a knowledge of SQL or similar data manipulation techniques. Take an accredited course, take another one. Start coding your own models. Realise its difficult and then start reading books until it isnt. Theres no quick route, its a career not a fashion.

In my view, transitioning into any field hinges on grasping the fundamentals and basics, particularly in data science that will be Statistics , Python & SQL. Start by creating projects based on various use cases and accessible data is paramount. Embrace a mindset of continuous learning, and you'll be well-equipped to pursue your goals in the field of your choice.

I transitioned into the data science field from teaching. What I did was to first gain an understanding of the data science field and what it entails. Following this I began to look at my technical skills and developed my python and SQL skills via a Coursera course. I then began applying for jobs which also included entry tests as part of the application. This allowed me to gain an understanding of the skills required to a) get the job, and b) succeed in the job itself Thankfully I did get my preferred job that I applied for, but the next step if I didn’t would be to begin doing projects and build my project portfolio.

To transition into a data science career with a focus on machine learning: 1. Educational Foundation: Gain a strong foundation in mathematics, statistics, and programming. Relevant degrees or online courses in data science and machine learning are beneficial. 2. Practical Skills: Learn key machine learning frameworks and tools (like Python, R, TensorFlow, or PyTorch) through practical projects and hands-on experience. 3. Portfolio Development: Build a portfolio of machine learning projects. Include diverse applications to showcase your skills. 4. Continuous Learning: Stay updated with the latest machine learning trends and technologies through webinars, workshops, and online resources. 5. Networking: Engage with the data science

I am from a business background. My exposure to the field as an intern helped me to understand the skill requirements. In fact, that experience inspired me to get a MS in Stats. The point is you need to get practical to even understand what the skill requirements are. There are thousands of resources and skill charts. But you may just get started with a basic understanding of Excel to help the business find some useful insights. The best way to do that is to connect with the analytics/data science departments of the companies you are interested in. Ask them politely about their data use cases and tools. Then, try to work on similar problems from open source. When you come back and show your work, maybe you may even get some opportunity!

When you understand your current knowledge, you can leverage your skills to learn other topics faster. ML can be approached from two directions, typically by programmers or by scientists or mathematicians. Having a background in Software Engineering means you already know a lot about programming, problem-solving, and design. To learn about machine learning techniques, they can work on task- or product-oriented projects. Finally, they should develop a habit of reading research papers, books, and online resources. From a science or mathematics background, someone could focus more on hands-on implementation of ML algorithms and learn programming along the way.

Evaluate your skills: the equation (y = mx + c) should be immediately clear to you. Whether through self-study or instructor-led training, dedication is essential. Cultivate a passion for numbers, master SQL, Python or R, atleast one visualization tool, and develop strong communication skills. Plan your learning path and get started.

By acquiring foundational knowledge in statistics, programming languages (e.g., Python or R), and SQL. Engage in online courses and certifications, like those on Coursera or edX, to learn machine learning concepts. Build a strong portfolio showcasing hands-on projects and participate in platforms like Kaggle. Network with professionals, attend meetups and seek mentorship. Gain practical experience through internships or freelance work. Continuously update your skills to stay current with industry trends, making yourself a competitive candidate for entry-level data science roles.

My transition from a master's degree in astrophysics to machine learning began with online courses like those offered by Deep Learning AI. These courses were great to evaluate my understanding of theoretical machine learning, statistics, mathematics, and basic coding skills. As I advanced, I found the machine learning/AI challenges intellectually stimulating and my interest in the field deepened, fueling my desire to explore it further. During this transition, I believe it's more important to rely on your self-assessment rather than the advice of the professionals in the field. Their interests might not match yours and their experiences -if marked by frustration- may not reflect the enthusiasm you have for beginning this new path.

In my experience, best way to learn these skills is by working on a project. You can double-down by making this project a part of your portfolio. This will help you showcase your skills to potential employers. To start working on a project, think about an industry and a problem you’d like to solve. You can find data in open access sources like Kaggle. Then apply what machine learning techniques you learned to solve the problem. Make sure to document your progress for portfolio.

In my opinion, ML is a wide field, and understanding essential concepts first is crucial. To continue, find a project in your industry of interest from websites like Kaggle and GitHub. Develop this project yourself to improve your skills by implementing solutions and creating a portfolio on GitHub and Kaggle. This hands-on approach is more effective than watching many tutorial videos, as we learn best by solving challenges through our own efforts rather than only by watching tutorials.

Learning by doing is often the most effective approach. You can achieve this by incorporating machine learning techniques into your Bachelor's or Master's thesis, or by engaging in a side project that aligns with your interests. Are you interested in time-series data? You can make predictions on solar irradiation or stock prices, for instance. By developing your own solutions and building a portfolio, you'll be prepared to showcase your work to the world (Kaggle and GitHub serve as excellent platforms for this purpose).

Transitioning into Data Science through Machine Learning (ML) involves mastering programming languages like Python and R, and key libraries like TensorFlow and scikit-learn. Understanding statistics, linear algebra, and probability theory is crucial for grasping ML algorithms. Gain practical experience by working on projects or participating in Kaggle competitions. Build a portfolio to showcase your skills. Networking, attending workshops, and pursuing relevant certifications enhance credibility. Stay updated with ML trends to adapt effectively and succeed in Data Science.

Based on my experience, it's crucial to pinpoint your specific interests within the data science field, especially in machine learning. Determine whether you are more inclined towards prediction, classification, or another area. Additionally, explore different industries such as insurance and finance to see where your interests align. Once you've identified your focus, work on projects within that field to hone your skills. Creating a portfolio to showcase these projects is essential. Remember, the most effective way to learn is through hands-on experience.

My thesis was about missing link prediction in social networks - a very niche field. I did it because I was curious. So, find things that you are curious about. Predicting cancer? Predicting stock market? Predicting the next social media trend? Find what you are most interested in knowing and then research what is being done. If something strikes, work on it. You can even redo the stuff! By the way, make sure to document everything (even if it is a complete redo) and host it somewhere (GitHub) to show.

I was introduced to the world of Machine Learning during my first internship. Tasked with creating an ML model that could identify with a certain degree of accuracy if the domain being accessed malicious or not. With little to no knowledge of what ML was and what tool to use, I learned from multiple online courses and blogs and got to work. From setting up the environment in Ubuntu to finally deploying a model as REST API endpoint. I learned and worked on the problem simultaneously along each stage of the project. In essence, I acquired the essential expertise on the job. There's no right or wrong way to start learning as we all come from different backgrounds with different levels of knowledge and understanding of concepts involved.

In my experience, it’s imperative that you learn the basics of machine learning. Of course, prior to delving into machine learning, the data cleaning and preparation skill is necessary (removing null values, one hot encoding, sometimes running PCA analysis, balancing imbalanced data, ensuring data is structured appropriately, and deciding on the source and target attributes for eventual testing and training). Of course, the best way to understand machine learning outside of utilizing learning resources, is to practice. Follow video tutorials that uses the iris dataset or the titanic dataset for machine learning. Those simple and practical examples would give you a decent level of understanding the topic of machine learning.

Embarking on the machine learning journey, I liken it to scaling a mountain: one doesn't need to conquer every peak to appreciate the view. My focus has been on grasping the essentials. Understanding the core types of machine learning and familiarizing myself with pivotal algorithms has been foundational. Tools like Python and TensorFlow have become my trusty climbing gear. While online courses and books have been my guides, podcasts and blogs have offered fresh perspectives. It's not just about the climb but understanding the terrain, the challenges, and the ethics of the journey. For me, it's about building a robust base before reaching for the summit.

R and Python are leading programming languages in ML analysis and data visualization, offering specialized tools, open-source access, strong community support, flexibility, and powerful integration capabilities. They excel in managing complex data sets, performing a wide range of analytical tasks, and creating customized visualizations. Their growing demand in data science underscores their value in offering competitive advantages in the job market.

In the realm of machine learning, moving beyond the basics of model.fit() and model.predict() is essential for true expertise. As a beginner, I learned the most by solving real-world problems. Create Custom Datasets and build models on top of it. Because in real world you won't have mnist.load_data(). For instance, I built a TV series dialogue search engine, converting subtitles into text files. This hands-on approach provides a holistic understanding of machine learning data collection, preprocessing, feature engineering and building model and evaluation when finally done push it to Github with a readme file where anybody can just clone your repo and run your project. Which demonstrates you can build projects from scratch

I found that the best way to learn data science concepts is to apply your knowledge and skills to real-world problems/datasets. Participate in competitions on platforms like Kaggle, and work on personal or open-source projects. This hands-on experience is crucial for making a solid understanding of the concepts and showcasing your skills to potential employers.

Create and share a unique project that interests you personally. Write about it as you work through the project. The best projects in my opinion are the ones that go from idea to demo using Streamlit or something to really show your work.

My first project was to scrape news headlines using R. I was curious in sentiment analysis and that is why investigated it. I did not complete that, but I still have the codes with me. Sometimes I use those codes and sometimes I get new project ideas by looking at those. Recently, I got the idea of seeing the impact of sentiments on stock market performance in my country. Let's see if there is something! Always look for real world use cases and data. Best, if you could find something that interests you.

I've found it helpful after finishing a machine learning pipeline and experimenting phase to try thinking and discussing how to put such a pipeline in production and try to solve a system design/AI engineering/MLOPs challenges, answer questions about different components that will be used and their interactions with each other, what are the needed tools and frameworks needed and so on I believe this will give you a deeper prospect. It's a very good exercise to enhance system design thinking which is a valuable skill in the real world.

Practical experience in ML is crucial for several reasons: it bridges theory to real-world application, enhancing problem-solving and data analysis skills; it allows for the creation of a comprehensive portfolio demonstrating proficiency with statistical techniques and effective communication; it facilitates the acquisition of industry-specific skills, including specialized tools and software knowledge; and it helps develop essential soft skills like teamwork, communication, and critical thinking. This hands-on experience not only strengthens theoretical understanding but also increases job market competitiveness.

Practical experience in ML is crucial for several reasons: it bridges theory to real-world application, enhancing problem-solving and data analysis skills; it allows for the creation of a comprehensive portfolio demonstrating proficiency with statistical techniques and effective communication; it facilitates the acquisition of industry-specific skills, including specialized tools and software knowledge; and it helps develop essential soft skills like teamwork, communication, and critical thinking.

To me, mastering machine learning is akin to learning an instrument; theory is vital, but true proficiency comes from practice. I've delved into real-world datasets from sources like Kaggle, treating each as a unique melody to decipher. By applying ML techniques, I've sought to unearth insights and craft solutions. Documenting my journey, akin to a musician's compositions, I've used tools like Jupyter Notebook to chronicle my process. Sharing my "compositions" on platforms like GitHub, I've built a repertoire that not only hones my skills but showcases my evolving symphony of data science to potential collaborators and employers.

I completely agree! It's not just about writing code and implementing the latest ML techniques but also about doing it in a way that can be shared with others. Utilizing publicly available datasets and sharing your code on platforms like GitHub or PapersWithCode, in addition to the others mentioned above, is an excellent way to kickstart your projects and connect with others who are also using the same datasets. This can lead to fruitful discussions and valuable learning experiences.

totally agree. you can also begin a simple, user friendly project to 'test out the waters' then go to medium level and, eventually open source machine learning projects(if that's your cup of tea).

Building a network within the data science community is vital for transitioning into a machine learning career. Engage in platforms like Kaggle, DataCamp, or LinkedIn, and participate in events and competitions to learn, gain feedback, and connect with mentors and peers. Networking not only keeps you updated on industry trends but also enhances your visibility and credibility in the machine learning domain.

Data science knowledge is free as it should be. Today we have many wonderful YouTube channels, blogs, medium articles and research papers that talk about the advancements in ML & AI, and most of them are free. Networking with resourceful individuals in the field would allow you to get a deeper idea about what is actually being implemented in the industry, What blogs or what tools are being discussed about, what projects they are working on and these would help to align your learning towards "What is actually relevant! ".

Networking in the ML community is vital for career advancement, knowledge expansion, and enhancing professional reputation. It opens doors to the hidden job market, fosters mentorship and collaboration, and keeps you updated with the latest industry developments. Engaging with peers through professional associations, conferences, online forums, and mentorship programs not only boosts your visibility but also contributes to your continuous learning and professional growth. Effective networking is about building meaningful relationships, exchanging expertise, and supporting each other's development in the field of ML.

I believe this is one of the most critical aspects when transitioning to a career in data science. Joining communities in your chosen field provides invaluable insights into the specific knowledge and tools you need to acquire. For instance, if you aspire to work in the pharmaceutical industry, you'll likely need to focus on models and tools used in areas such as image recognition. On the other hand, if your interest lies in the energy sector, you'll find that working with time series data is more common than dealing with images.

Build your data science tribe: Data science can feel like a solo adventure at times, but there's a vibrant community out there waiting to connect. Online forums like Reddit's r/datascience or Facebook groups like "Data Science Learners" provide a platform to exchange ideas, ask questions, and get feedback from fellow aspiring data scientists. Local meetups and conferences are also fantastic opportunities to network with professionals in the field and learn from their experiences.

Talking to someone who has already made a career change to data science is probably the most efficient way of finding out what is involved in such roles. If you’re in an academic role be sure to attend any careers events or external seminars, try to talk to the speaker. If your role gives you a conference budget make use of it to meet peers in data science roles. The online resources give the false impression that data science is writing code until a model runs on your laptop. It is a far cry from reality.

Networking in the ML community is vital for career advancement, knowledge expansion, and enhancing professional reputation. It opens doors to the hidden job market, fosters mentorship and collaboration, and keeps you updated with the latest industry developments. Engaging with peers through professional associations, conferences, online forums, and mentorship programs not only boosts your visibility but also contributes to your continuous learning and professional growth. Effective networking is about building meaningful relationships, exchanging expertise, and supporting each other's development in the field of ML.

I participated in a Data Science conference at Bengaluru, India. At that time, I had no idea about data science and was not even sure about it. The presentations were daunting: they were talking about support vector machine, f1 score and were showing their notebooks! I was scared but I was excited to see the potential and their enthusiasm. So, go and connect. Attend those events and ask stupid questions.

For me, diving into data science is not just about algorithms; it's about being part of a vibrant community. Think of it as attending a global seminar. Platforms like Kaggle and LinkedIn have been my virtual classrooms, while hackathons mirror intense group study sessions. Engaging in these spaces, I've not only expanded my knowledge but also forged connections with mentors and peers. These interactions keep me abreast of the ever-evolving data landscape. Beyond skill acquisition, it's about sharing, collaborating, and growing together. In this dynamic field, staying connected ensures I'm not just keeping pace but truly being a part of the data science narrative.

Some guidance condensed into 5 key points: 1. Choose relevant platforms (Kaggle, LinkedIn, etc.). 2. Actively participate and engage in discussions. 3. Attend events and competitions for practical experience. 4. Seek mentorship from experienced data scientists. 5. Stay informed, build a strong online presence.

Preparing for interviews is great, but it is an endless process: there is always a new skill or technology to learn. I came to data science from a non-tech field - linguistics - and I never feel ready for interviews. But at some point, I had to stop just preparing and start applying for jobs and going to interviews. Yes, I failed a few times, sometimes painfully. We all did. But passing interviews is a skill best learnt while interviewing. You just get used to it, stop being so stressed (how scary can it be when you already failed before?), and, finally, you succeed. But it's rare to get the first job you apply for, so just start applying.

Start with the basic integral areas of ML: 1. Mean, Median, Model 2. P-Value 3. Over fitting/Under fitting 4. Bias-Variance Tradeoff 5. Prepare HOWs/WHYs of each concept (For example : How do you tackle over fitting/under fitting) Also make a habit of reading ML articles from Medium, Towards data science, AnalyticsVidhya.

Make sure you're comfortable explaining the basics first and foremost. Then, it's important to focus your interview preparation on the specific role you're interviewing for. Get as much information as you can from the job description and hiring manager, and then see how your experience can align with what they are looking for.

1. Prepare Thoroughly: Treat interview preparation as the final crucial step in your transition. 2. Diverse Preparation: Be ready for different types of interview questions, including technical, behavioral, case study, and scenario-based questions. 3. Practice: Practice your answers, explanations, and coding skills using various resources like online platforms, mock interviews, or with the help of friends. 4. Research: Research the company, role, and industry you are applying for to tailor your resume, cover letter, and portfolio accordingly. 5. Impress Hiring Managers: Effective interview preparation will increase your chances of impressing hiring managers and successfully landing your desired data science job.

Remember everything you have done that is not data science is still relevant. Don’t over-emphasise a new skill at the expense of your expertise. In particular - candidates transitioning from academia often forget to mention the communication and graphic design skills needed for publication and conference publication, even marketing and finance for grants. - if you are transitioning from software development, all of devops, unit testing, Ci/cd are relevant. Can you leverage them with a competitive advantage over candidates without formal software training? - any experience on project planning, mentorship, team leadership is relevant. Make sure the interviewer knows what level of responsibility you are able to take.

Hands-On Projects: Work on real-world projects to gain practical experience. Building a portfolio showcasing your projects is essential for demonstrating your skills to potential employers. Utilize Online Courses and Certifications: Enroll in online courses and certifications from reputable platforms like Coursera, edX, or DataCamp to gain structured learning experiences.

To transition into a ML career, start by evaluating your skills and interests, especially in mathematics and analytics. Strengthen your foundational knowledge through online courses and get hands-on experience through personal projects and internships. Join professional communities and stay updated with industry trends. Building a strong portfolio and enhancing communication skills are also crucial. Consider further education, like a master’s degree, if it aligns with your career goals. Success in shifting careers requires persistence and leveraging available resources.

My first data science interview was with CS graduates, and they were from renowned schools. I had zero idea about Python and used Excel to solve the questions (yes, it was messy!). No, I did not get the job. But I understood how those interviews went. And that was a game changer.

Venturing into data science interviews, I view them as a two-way street: an opportunity for me to showcase my skills and for the company to present its vision. Preparing for these interviews is akin to gearing up for a major exam. I delve into diverse question types, from technical deep-dives to behavioral queries. Platforms offering mock interviews have been invaluable, refining my articulation and coding on-the-spot. Beyond the technical, understanding a company's ethos and aligning my portfolio to its needs is crucial. For me, it's not just about landing a job, but ensuring a symbiotic fit where both I and the organization can thrive.

A Data Science Interview is not only about data science. The following bases should be covered before you sit for an actual interview: 1. Statistics - The math behind the algorithms. When you are able to explain the algorithms well, your chances of clearing the interview increases. 2. Good hold of Python - Most data science positions require Python as a primary skill. You should be comfortable with answering Core python questions. Most interviewers may also ask you to solve some programming problems live. 3. Have a beginner level understanding of MLOPs and how ML models are deployed in production. A basic understanding of the Cloud platforms like AWS, GCP and Azure would be a plus.

-Data science is a very wide field and Machine learning is a part of it. -Understanding of Business domain is more critical than programming skills for Data Scientists. -Now-a-days, many institutions are providing degrees in data science but few years back it wasn't. So for working professionals, certifications are also a good option to establish their comptencies in Data science. Certifications like Microsoft Azure Data Scientist Associate, Tensorflow developer and from other OEMs evaluate skills on a global standard levels for data scientists. - Try to solve as many problems as you can in data science domain - Attend conferences and Summit where experts are sharing the Data science solutions they implemented to solve their problems.

Before diving headfirst, it's crucial to familiarize yourself with the various roles that exist within data science and how machine learning expertise fits in. You have the data analyst that focuses on analyzing data to extract insights, create reports, and visualizations; they may use machine learning, but isn't the primary focus. Then you have the data scientist that applies statistics, programming, and domain knowledge to build predictive models; strong machine learning skills are beneficial. In addition, you have the machine learning engineer role that operationalizes machine learning models, deploys them into production, and ensures their scalability; this role emphasizes technical implementation skills in addition to ML understanding.

For those pursuing careers in developing machine learning models, understanding business objectives is essential. Aligning models with business goals ensures they provide actionable insights and drive value. It requires a blend of technical skills and domain knowledge, allowing for the creation of solutions that address specific business needs. This understanding enhances communication with stakeholders, facilitates better model evaluation, and ensures relevance and usability.

Gaining practical experience is a key differentiator in the field of data science, setting you apart from those who only possess theoretical knowledge. By applying theoretical concepts to real-world problems, you'll acquire a deeper understanding and learn valuable skills beyond what textbooks can offer. For those not yet working in the field, participating in Kaggle competitions offers an excellent opportunity for hands-on learning. Engaging in these competitions allows you to test your skills, and reviewing others' solutions afterward provides further learning opportunities.

Se assim como eu você veio da área de desenvolvimento de softwares você precisa saber crucialmente que programar não é a principal atividade do cientista de dados, ainda sim você irá utilizá-la com vantagens dos conceitos de código limpo, organização, versionamento, modularização ETC da sua experiência anterior, mas ponha na cabeça que você precisa mergulhar na estatística, no Storytelling, em ser didático e em se comunicar muito bem.

I often get the question "Does it really makes sense to understand all of the underlying mathematics or write out NN by hand". The answer is, yes, it does help to break down a deep learning algorithm all the way to the bottom and understand the mathematics, write out the regression models by hand and finetune it at least once with a very small Neural Net. It is good to, at least come close, to understand what is happening under the hood of the black box.

Understanding your goals deserves a bit more coverage in my opinion. This will be important in deciding what to learn or what roles to apply for. In particular: - do you prefer to identify problems or provide solutions to known problems? - are you primarily interested in applying machine learning, or in the domain to which it is applied? - do you prefer to explore how to solve a problem, or deliver a solution to the end user? - do you approach problem solving by “trying things out”, or by seeking a deeper understanding of the problem and solution? I ask questions of this sort in interviews, or seek the answer without asking. It can be difficult to be confident someone is the right fit if they don’t know themselves.

Beyond the structured steps and preparations, my journey into data science has been deeply personal. It's akin to crafting a unique piece of art; while techniques and tools are essential, it's the individual touch that makes it stand out. I've had moments of serendipity, stumbling upon insights when least expected, and periods of challenge, where datasets felt like intricate puzzles. Alongside the technical, the human aspect has been pivotal. Collaborations, debates, and shared eureka moments have enriched my path. It's not just about numbers and code; it's about the stories they weave and the human connections they foster. This is my unique tapestry in the vast realm of data science.

One potential route to venture into machine learning as a data scientist is to propose and explore the use of machine learning approaches in the tasks in hand. You would have enough domain knowledge to understand the data and the desired outcome. That should provide you with a good head start to experiment the use of machine learning in the company.

J. U. S. T. b e c r e a t I v e!