How can you qualify for data science roles?

My take on the fundamentals. - Mathematics: Develop a strong foundation in linear algebra, calculus, statistics, and probability. -Computer Science: Acquire knowledge of data structures, algorithms, and programming languages such as Python or R. - Domain Knowledge: Understand the specific domain you're working in, whether it's finance, healthcare, marketing, or any other field. Knowing the context and nuances of the data will help you make informed decisions. - Learning Resources: Utilize online courses, books, podcasts, blogs, and tutorials to learn and enhance your skills. Experiment with practical projects and participate(GitHub) in data science competitions to gain hands-on experience(Kaggle).

My son will soon be completing a masters in statistics from a very well-respected masters program. I think he has all of these skills in spades, except maybe the domain. His domain target is the airline industry so I have given some advice on how to build that domain knowledge up and have had him connect to several connections of mine in the airline industry. Here is hoping he lands well.

After reading this article, I feel that becoming a data scientist is almost "mission impossible". I kind of disagree with most of what is written. Yes, all of these skills are needed (maybe) in the long term, as you progress, but to land your first role, you only need (a very small) subset of them. When I started, I focused on learning 1 programming language (R) and machine learning (linear and tree based models, svms). I got a job in finance, my domain knowledge was borderline zero. You learn on the job. No-one hires experts for entry level roles. In short, becoming a data scientists, like most roles, is a constant learning path. The more you know, the more you can progress in your career. But you don't have to know everything to start.

To qualify for data science roles: - Focus on building a strong foundation in the fundamentals of mathematical concepts like linear algebra, calculus, and statistics for data analysis. - Develop proficiency in programming languages like Python or R. - Gain domain knowledge relevant to the industry you want to work in, understanding its challenges and objectives. - Familiarize yourself with various data sources and formats and learn data preprocessing and cleaning, ensuring responsible data handling. - Embrace a mindset of continuous learning to stay updated with the latest trends and advancements. - Balance theoretical knowledge with hands-on practice to become a well-rounded data scientist ready for new opportunities.

The fundamentals of data science begin with statistics. You don't need to be a statistician to succeed in data science, but grasping the fundamental ideas is as important or maybe even more important than all the complex and fancy techniques (e.g. statistical significance, statistical power, law of large numbers, central limit theorem, multiple comparisons, etc). In addition, I find that semi-regularly refreshing your understanding of these fundamental concepts is equally as important.

I don’t really think any DS should master all the tools mentioned here. Most of the time your time will just be spent on a very small subset of those, depending on what part of data science you are anchored on. For me (analytics data scientist at LI) at least 80% of my work is done with a combo of SQL, Spark, Python, and Google sheet/slides/doc.

Agreed, I also think it is important to stay updated with the latest advancements in data science and technology. You could do it by attending webinars, conferences, and workshops to expand your knowledge and skills.

Think of data science tools as your trusty toolkit. You've got Python, SQL, Excel, Power BI, and more - they're your go-to gadgets. Online courses are like workshops, showing you the ropes step by step, turning you into a data wizard. Use Git and GitHub to keep your data routines fresh. Just like a comedian keeps their jokes current, version control keeps your data in check.

Most of the projects I've recruited for have had a big emphasis on tool skills. We like applicants who know Python, Tableau, SQL, and advanced Excel. It is also helpful for applicants to have knowledge of data engineering and automation tools.

I have no doubt that a data scientist should master some of the mentioned tools. However, I believe no one will use all of them in the same project or company. Along the years is perfect natural that a senior data scientist will have some familiarity with all of them. On the other side, for the ones at the beginning I would suggest to focus on a language programming like R or python. In special if the aspirant to data scientist has a low mathematical level I would suggest to start with R. The reason is that R is more similar to the standard mathematical notation and so it will help the student to understand the mathematics behind any machine learning method.

Documentation is essential for your portfolio. Clearly and concisely explain each project's problem statement, the data sources used, the methodology applied, the results obtained, and the insights derived. This narrative helps viewers understand the context and significance of your work, showcasing your ability to communicate complex findings effectively. GitHub is particularly valuable for sharing code and demonstrating your version control skills, while Kaggle provides exposure to a data science community and potential recruiters. Medium allows you to publish in-depth articles about your projects, explaining them in detail for a wider audience.

While sample data like the iris and Titanic datasets are great ways to learn and practice algorithms, I believe it's far more beneficial to build a portfolio with real-world data. Not just finding and analyzing the data but also explaining WHY it's important. Organizations aren't often terribly particularly interested in the idiosyncrasies of how the code works behind the scenes. They are, however, very interested in how data science models can bring value to their business (through benefits like increased revenue generation, improved time efficiency, and so on). Even if you don't have access to their proprietary data, there are a lot of fantastic public data sources to start building a portfolio with!

This one is a good piece of advice. Besides having something to show to your potential employer, having "done the work", by building a portfolio, gives you the practical experience that you need to answer interview questions.

Building a data science portfolio indeed showcases your skills, but it's crucial to stand out. Common projects can blend into the background. Challenge yourself with unique angles and hypotheses that haven't been widely explored. When selecting projects, always ask: "How can I approach this differently?" and "What unique value can my analysis bring?" This creative and value-driven mindset ensures your portfolio not only displays technical skills but also your ability to innovate and contribute meaningfully in data science. Remember, it's not just about the tools you use, but how you use them to solve problems and generate insights.

It is key 🔑 to build a portfolio using real-world datasets. This way you can showcase that you are able to solve real-world business problems. Portfolio projects should be selected based on: 🎯 target industry or domain, 💪 your personal motivation, 🌍 and (real-world) data availability to solve a (real-world) business problem.

Networking in the data science community is essential for professional development, learning, discovering new opportunities, and building a supportive network of peers and mentors. It not only enriches your data science journey but also opens doors to a broader and more fulfilling career path. Also, networking enables you to share your work and projects with peers and receive valuable feedback. Constructive criticism and validation from others can help you improve your skills and gain confidence in your abilities. Being active in the data science community and contributing valuable insights can enhance your reputation and visibility. This can lead to opportunities for speaking engagements, writing articles, or even potential job offers.

While it's relatively easy to meet people, it's much harder to build relationships with them. Networks aren't made up of robots, but actual people. Building a network is also a two-way interaction, not a one-way transaction. Networking is also much more difficult for individuals trying to land their first data science role. They might believe they don't have much to offer others who are more experienced in the field. Fortunately, this is far from true. The caveat, though, is to be thoughtful about how to make the first move. Making a cold request to submit an application for an open role is often poorly received. Instead, ask them for feedback on a project you're working on or how their own career path panned out.

Some people are natural networkers while others see this as a chore. Regardless of your perspective, networking is critical. In the real world, who you know is extremely important to finding jobs. In many cases, the personal introduction is the difference-maker in getting a job - companies struggle to distinguish the hundreds of resumes they receive so having an actual contact in a company is important. Choose your medium - you cannot be engaged actively in every single forum (LinkedIn, X, Quora, Reddit, Facebook...) and so you should find which one works best for you and serves your goals. Good luck!

Networking is the key in getting a job in any field. However, one must do conscious network. People do not respond when you just direct message a recruiter for a job referral in first message. Networking is all about building relationships and is a two way approach. This help the other end to believe/trust on the skills.

Data science is a field in constant evolution that is constantly a topic of writing (articles, posts, etc...). Contribute to this ecosystem yourself and reach out to others doing the same.

Continuous learning and improvement are core principles in the journey of a data science. In this dynamic field, staying updated with the latest technologies, tools, and methodologies is vital. Dedicate time to expand your skill set, explore emerging trends, and deepen your understanding. Consider advanced courses, certifications, or specialized training to sharpen your expertise. Embrace a growth mindset, as ongoing learning not only enhances your proficiency but also positions you as a top-tier data science professional who thrives on innovation and mastery. :)

Consistency is the key. Data Science is an evolving field and its important to keep learning. Some ideas to make your learn fun and productive: 1. Learn from peers: Whether you are a student or full time, have your friends to learn together 2. Join discord/online communities: There are several discord channels that provide several learning materials and assignments. 3. Start implementing or supporting open source communities, this will help you learn the different perspectives from other collaborators 4. Above all, stay connected with folks that are alongside your learning journey.

Never stop learning! If you plan to have a challenging data scientist role, it will probably require that you constantly acquire new skills and knowledge. Learning does not have to mean enrolling in a formal educational program. I think, for most of us, it will probably mean dedicating some minutes, every day, to learn something new. Use the resources readily available to you, for example, your peers previous work or 10 minutes reading Wikipedia, or watching a Youtube video.

For a field as diverse & ambiguous as Data Science, I'd say start with the fundamentals, learning 'what' & 'how' to learn. 🔣Start with the Maths-Linear Algebra, Basics of Differentials and Integrals (helps when working with discrete & continuous data). 💠Learn different techniques to visualise datasets-get innovative with creating dashboards. 🫧When it comes to real world data~ 70% of time is spent on cleaning, so familiarise with imputation, handling invalid entries, outlier detection, normalisation & scaling techniques etc. 🪴Read more about models and its inner workings- Start with ML models like Linear Regression, K-means, KNN etc then level up to CNNs, LSTM etc. ⚗️Experiment-Learning happens more on the ground than in books.

I can confirm that embracing continuous learning is truly fulfilling. I was always involved in updating my skills. So after 16 years of completing my engineering degree, I embarked on a master's journey driven by the desire to stay current with evolving technologies. Navigating through studies, which included challenges in mathematics, programming languages, and the dynamic field of data science, proved demanding. However, determination was the crucial factor that enabled me to persevere. The rewards obtained from this journey are substantial, reinforcing the valuable lesson that learning should never come to a halt.

Data science roles are incredibly diverse, making it very hard to prepare for them "in general". Learning to interpret (or "read between the lines") job ads is essential. This takes practice, usually in the form of applying for jobs and not getting them. However, the process will teach you a lot about what companies are looking for, how companies/roles differ, as well as differences by industry. Don't take rejections as a sign of failure, instead get as much feedback as you can, learn, and move on to the next interview. Your network, as discussed in the section above, is also an important resource to learn about companies, industries, and interviews to help you prepare.

- Review common interview questions and practice coding problems. - Refresh your technical skills in stats, machine learning, programming. - Research the company and role to ask informed questions. - Practice explaining technical concepts clearly for non-experts. - Prepare stories from your projects demonstrating your skills. - Anticipate behavioral questions by reflecting on past work experiences. - Cite quant examples judiciously unless specifically asked. - Request feedback to identify areas for improvement. - Practice interviews to boost confidence and reduce nervousness. Thorough preparation allows you to showcase your analytical abilities and land the role just.

Data science interviews are not just about showcasing your technical expertise; they're a chance to demonstrate how you apply your skills to real-world problems. Your preparation should include a deep dive into the company's projects and their industry's challenges, using this to tailor your approach. Be ready to articulate your problem-solving process, from data analysis to actionable insights, in a clear and concise manner. Also, update your portfolio with relevant projects and achievements, ensuring it aligns with the company's needs and the role's requirements. This tailored, in-depth preparation will make you stand out as a candidate who can add immediate value to their team.

In the crucial step of preparing for data science interviews, here are a few pointers to help you succeed: Master Technical Skills: Deepen your understanding of key technical concepts, algorithms, and tools commonly used in data science. Practice coding exercises and algorithms frequently encountered in interviews. Hone Communication Skills: Work on articulating complex ideas in a clear manner. Practice explaining your thought process during problem-solving, making it easy for interviewers to follow your approach. Simulate Mock Interviews: Conduct mock interviews with peers, mentors, or online platforms to simulate the interview experience. Receive constructive feedback to identify areas for improvement in both technical & soft skills.

Every role is different. Because of this your interview prep needs to be built in the following way. Imagine a pyramid, made up of three tiers: 1. The base: the fundamentals and non-negotiable skills required for data science. This can be prepared for through lists of generic data science interview questions. 2. Mid-tier: Broad knowledge of concepts mentioned in the job-description. If a job description specifically mentions clustering, you should know about it. 3. Top-tier: bring something that will make you stand out, are there new libraries or research articles you could mention for example? Could you build a quick project/demo? Finally, get 1% better every time. There will be plenty of rejections, this is expected so don't worry.

Data science needs understanding of business, math and technology. Role requires you to focus on all 3 aspects together. To get into data science, understand business problems is critical. You can learn that by solving business cases, building framework. Math part requires you to put the data in the best equation possible. Once you have enough confidence on these aspects learn the tech part . Focus on python as a coding tool. Pick up a real world problem and apply your learning’s. This will help you build enough confidence for data science roles!

To be successful in anything - one got to be consistent. Data Science is no joke at the same time its not impossible. Irrespective of background, if one can invest time and effort, they can be successful. From my experience, I see the more comfortable we are with technical tools (like Python/SQL for me), the more confident we become. Try to get the basics right. Solve simple math problems. Pick a Domain. Understand the business need. Data Science is not just about building complex models, its about utilising the right data to solve the business problem. Keep it simple, relevant and explainable.

Stay curious, adaptable, and resilient, as the data science field evolves rapidly. Combine technical expertise with a passion for data and a willingness to explore diverse industries to carve a successful path in the world of data science.

Curiosity and critical thinking are both essential skills to be a successful data scientist. Curiosity is crucial for exploring and understanding data, uncovering hidden structures and relationships, possible outliers and quality issues. Critical thinking helps interpret the results of analyses accurately, assess the reliability of the data, and identify potential biases or limitations in the findings.

Some candidates don’t make the final cut because they cannot find the right applications of their ML toolkit. A deep leaning model achieving SOTA performance in speech-to-text benchmarks is cool, but useless to a business on its own. You’ll need to be able to describe in plain terms how your system helps leaders meet their business goals. Too many candidates fail to realize that world-class skills won’t mean anything to a business leader if that leader has to be the one to tell you what to do with them. So in your interviews, ask questions like “what business goals is your org supporting?” Or “what is your current biz strategy for meeting these goals?”. That’ll give you an opportunity to see what ML and Experimentation can do for them.