Data science is a fascinating and rewarding field that combines skills in programming, statistics, and problem-solving. You have a passion for data science and want to pursue a career in it, but you may wonder how to get started and what steps to take. In this article, we will share some tips and advice on how to turn your passion into a career in data science.
The first step to becoming a data scientist is to learn the basic concepts and tools of the field. You need to have a solid foundation in mathematics, especially linear algebra, calculus, and probability. You also need to master at least one programming language, such as Python, R, or SQL, and be familiar with common data science libraries and frameworks, such as NumPy, pandas, scikit-learn, TensorFlow, or PyTorch. You can find many online courses, books, and tutorials that can help you learn the basics of data science.
Here is a very simple guide to become data scientist: 1. Python 2. Probability and statistics 3. SQL 4. Excel 5. Machine learning Hands on experience by making projects related to machine learning, SQL and deep learning As you are just beginning your career that's why you should focus on the concepts and practice more and more by making so many projects. During this journey start reading articles on medium regarding data science and connect with people, see what type of projects they are doing. Happy learning ??
Counter to the popular answer which says start with Python: an aspiring Data Scientist should start with understanding the Scientific Method, fundamental logic, and critical thinking! Then probability and statistics. Learning how to program is (still, arguably) necessary for the role, and Python is currently the best choice - but I'd put understanding the concepts of inductive and deductive inference, and how to apply science to a problem, way before programming skills.
There are two essential dimensions that one must focus to be a good data scientist. 1) Ability to understand the statistical concepts, I would recommend this book - "An Introduction to Statistical Learning" by Trevor Hastie & Gareth James 2) Good programming abilities - teach yourself SQL and or python. Once you have the above two skills, its essential to partner with good subject matter experts from the business side to solve problem that will create business impact. Also lead with "why", "what problems you are trying to solve?" and "how to make easy for users to consume it" Having a good mix of technical with business acumen will take you a long way in your data science career.
While I'm not currently involved in teaching, I'm happy to share my approach to learning ML and Data Science. First and foremost, it's crucial to identify your passion. Ask yourself if you genuinely enjoy diving into data science, i.e. learning all the tools, statistics, and model building. It requires dedication, but once you find motivation, the next step is to get data science roadmap. Start with Mathematics and Statistics since understanding programming languages like Python and SQL for ML and DL Algorithms frameworks becomes more manageable with solid statistical foundation. The next step is to work on real projects using Kaggle datasets or by scraping data. Explore different domains like Computer Vision, NLP, or Gen AI.
The first step to be a data scientist is first learn the basic concepts of mathematics , statistics, linear algebra and calculus , A programming language Python is widely used , SQL , then jump into the data manipulation , visualization libraries pandas, numpy. and then ML,DL.there are various resourses online you can follow Youtube , Coursera , Udemy Practice on Kaggle and continue learning
The next step to becoming a data scientist is to build a portfolio of projects that showcase your skills and interests. A portfolio is a great way to demonstrate your ability to apply data science techniques to real-world problems and communicate your findings effectively. You can choose projects from various domains, such as health, finance, education, or social media, and use different methods, such as data visualization, machine learning, natural language processing, or deep learning. You can also participate in online competitions, such as Kaggle, or contribute to open-source projects, to gain more experience and feedback.
To turn your passion for data science into a career, start by acquiring relevant skills through online courses, certifications, or formal education. Build a strong portfolio showcasing your projects and practical applications of data science. Network with professionals in the field, attend industry events, and consider internships or freelance opportunities to gain hands-on experience. Finally, tailor your resume to highlight your skills and projects, and actively apply for entry-level positions or roles that align with your passion for data science.
Top two names are Kaggle and Github. Whichever you do, make sure that you give your best shot. If you are a beginner, I'd prefer Kaggle. With Kaggle, you will find it much easier to improve your work since it's easy to find advanced notebooks on the same task. Apart from that, it can give you a richer experience of data science than Github.
A well-curated portfolio provides tangible evidence of your capabilities, allowing potential employers or clients to assess your proficiency and suitability for data-related roles or projects. Your portfolio should include a diverse range of projects that demonstrate your ability to solve real-world problems using data science techniques and methodologies. Each project should be accompanied by a brief description outlining the problem statement, your approach, the tools and techniques employed, and the outcomes achieved. By showcasing your projects in a portfolio, you not only highlight your technical skills but also your creativity, critical thinking, and problem-solving abilities.
Building a data science portfolio involves selecting diverse projects that showcase your skills, from data cleaning and exploration to model building and evaluation. By collecting and cleaning data from various sources, conducting exploratory analysis, and engineering meaningful features, you demonstrate your ability to work with real-world datasets. Building and evaluating machine learning models further highlights your problem-solving skills. Visualizing and interpreting results effectively communicates your findings, while documenting your process and creating GitHub repositories ensure transparency and accessibility.
A portfolio can also allow you to utilize your target companies' data to address their problem statements, leaving a lasting impression in the hiring managers' minds.
The third step to becoming a data scientist is to network and connect with other data science enthusiasts and professionals. Networking can help you learn from others, find mentors, discover opportunities, and get referrals. You can network and connect by joining online communities, such as forums, blogs, podcasts, or newsletters, or attending local events, such as meetups, workshops, or conferences. You can also reach out to people who work in data science roles and ask them for advice or insights.
Networking is not just about expanding your list of contacts. It's a strategic approach to forge meaningful relationships within the data science community. By engaging with other enthusiasts and professionals, you're immersing yourself in a community that values knowledge sharing and collaboration. This proactive involvement is key to building a robust network that supports your professional growth and opens up new avenues in your data science journey. I have personally progressed by starting as a Data Scientist more than ten years ago, always active in the local and international community, towards being the president of MACHINERY - our local organisation of professionals in Data Science, ML, and AI.
Networking and connecting with others in the field of data science is essential for professional growth and advancement. By actively engaging with fellow data professionals, attending industry events, and participating in online forums and communities, you can expand your network, exchange ideas, and stay updated on the latest trends and developments in the field. Networking also opens doors to new opportunities, whether it's collaborating on projects, seeking mentorship, or exploring potential career paths. By fostering meaningful connections and building relationships within the data science community, you can enhance your visibility, credibility, and ultimately, your success in the dynamic and fast-paced world of data science.
Networking is crucial in building a career in data science for several reasons. Firstly, it allows you to connect with professionals already established in the field, providing opportunities to learn from their experiences, gain insights into industry trends, and access valuable resources such as job openings and training programs. Networking also helps you build a reputation and establish credibility within the data science community, which can lead to collaborations, mentorship opportunities, and recommendations for future roles. Additionally, networking allows you to expand your professional circle, opening doors to potential partnerships, projects, and even entrepreneurial ventures.
Think of every mentor as a teacher who can guide you on ambiguous matters. Enhance your depth and width by connecting with technical experts and functional experts.
Networking is essential part of career switch, understand you may find opportunities in your network that may not be a data scientist to start or may not have the title, you could find an analyst role or an operations role that could teach you essential skills to gain deeper business knowledge which essential to be a good data scientist. Don't let a title or job description limit you, prepare a solid pitch when you identify opportunities that would drive value to your organization and find a mentor within your organization or outside you can guide you and point you to the resources who could help you land the data science job.
The fourth step to becoming a data scientist is to keep learning and improving your skills and knowledge. Data science is a fast-changing and dynamic field that requires constant learning and adaptation. You need to stay updated on the latest trends, tools, and best practices in data science and explore new areas and challenges. You can keep learning and improving by taking advanced courses, reading research papers, following industry experts, or taking on new projects.
I switched from a Project management career to Business Intelligence/ Data science and the most important aspect of doing that was to build necessary skills. As a data science professional there are many facets to becoming a successful professional that involves multiple skills including data manipulation, architecture, understanding databases, the tools and software used to do the above and visualization tools along with skills like coming up with strategic insights or conveying the results from your data with non technical users. Leaning should be a continuous journey and learning where the industry is pivoting towards is also crucial.
Stay updated on the latest developments and advancements in data science by continuously learning new tools, techniques, and methodologies. Enroll in advanced courses, workshops, or certifications to deepen your expertise. Experiment with new technologies and tools to broaden your skillset. For example, explore specialized topics like deep learning or natural language processing.
Data science is a dynamic field that evolves rapidly. Stay updated on industry trends, new tools, and emerging technologies through continuous learning. Enrol in online courses, attend workshops, and obtain certifications to enhance your skill set and stay competitive.
The fifth step to becoming a data scientist is to apply for jobs that match your skills and goals. You need to prepare a resume that highlights your education, experience, projects, and achievements in data science. You also need to prepare for the interview process, which may involve technical questions, coding challenges, case studies, or behavioral questions. You can practice your interview skills by doing mock interviews, reviewing common questions, or using online platforms, such as Interview Query or Pramp.
Tailor your resume and cover letter to highlight your skills and showcase your portfolio. Apply for entry-level data science positions, emphasizing your commitment to ongoing improvement and your passion for the field.
The sixth step to becoming a data scientist is to grow your career and advance in the field. You need to set realistic and achievable goals for your career development and seek feedback and guidance from your managers, peers, or mentors. You also need to expand your network and build relationships with your colleagues, clients, or stakeholders. You can grow your career by taking on more responsibilities, leading projects, mentoring others, or pursuing certifications or degrees.
Once you land a job in data science, focus on continuous growth and development. Seek out opportunities for mentorship, training, and advancement within your organization. Consider pursuing advanced degrees or certifications to further specialize in areas of interest and advance your career.
To apply for data science jobs, tailor your resume and craft a compelling cover letter that highlights your technical skills and passion for the field. Utilize job boards, company websites, and networking connections to find relevant openings. Research the company to customize your application, and prepare thoroughly for interviews by practicing coding exercises and data analysis problems. Submit your application online, follow up when appropriate, and leverage your network to explore additional opportunities.
Talk to users and get their feedback. Acknowledge issues on accuracy, explainability & compatibility and partner with IT to develop more holistic solutions. This will allow you to not only develop as a professional but also help you make contacts who can vouch for you.
As you gain experience, focus on continuous growth. Seek opportunities for professional development, take on challenging projects, and consider advanced roles within the data science domain.
What most don’t consider is command of subject matter in the area of analytics one aspires to work in. Ex. if one wants to be a data scientist in healthcare, knowing human physiology, psychology and research methods in those areas sets you up for success. It also makes you stand out as an applicant for a position.
Ensure you have a solid foundation in mathematics, statistics, and computer science, which are the core areas of data science. Pursue relevant courses, degrees, or certifications in data science, machine learning, and related fields. Work on projects that demonstrate your skills and expertise in data science. Look for internships, part-time roles, or freelance opportunities to gain hands-on experience in data science. Build a professional network within the data science community. Attend conferences, workshops, and meetups related to data science. Find mentors who can provide guidance, support, and advice throughout your career journey in data science. Data science is a rapidly evolving field, so it's essential to stay updated.
1. Find a mentor: You need someone to mentor you. 2. Mentor others: Give back to the community. 3. Don't get distracted till you achieve a milestone: Once you are in the middle of data science community, you will feel bombarded by content. One day you decided to learn SQL. Other day you see 10 job posts requiring Mongodb. You need to be vary of it. Complete what you had decided first. 4. Have interest in maths and statistics: An intuition of maths and statistics is important. A lot of people have not so good memories with these subjects. But overcome those fears.
Start by mastering foundational technical skills like programming languages (Python, R), statistical analysis, and machine learning algorithms. Supplement this with specialized knowledge in your niche areas such predictive analysis and data modelling. Showcase your expertise through real-world projects, creating a diverse portfolio that reflects your unique approach. Network with professionals in the field, attend industry events, and seek mentorship to deepen your understanding and stay current with advancements Balance technical skills with creativity and critical thinking, as these soft skills are essential for problem-solving and innovation in data science
As a Senior Data Scientist, to transform your passion for data science into a career, start by solidifying your foundation in mathematics, statistics, and programming. Engage in continuous learning through online courses, bootcamps, or a degree program. Build a portfolio of projects to showcase your skills and knowledge. Network with professionals in the field through social media, conferences, and data science meetups. Seek internships or entry-level positions to gain practical experience. Stay updated with industry trends and technologies. Finally, actively contribute to data science communities and projects to gain visibility and establish your expertise.
Explore contributions from fellow community members who have shared their journeys and insights. Learning from their experiences can provide valuable perspectives on navigating a successful career in data science. Ready to contribute your own experience? Start by sharing your insights and join the vibrant community of data science enthusiasts!