With a hot job market, lucrative salaries, and promising career opportunities, it’s a great time to become a data scientist. But what if you’re starting from scratch? Luckily, there is a myriad of different learning paths. You can learn skills in the field in many different ways—from getting a college degree to attending bootcamps to teaching yourself. Not sure where to start?
Embarking on a career in data science can be an exciting journey, but it requires careful planning and the acquisition of relevant skills. This comprehensive guide outlines seven essential steps that will guide you towards securing a data science job from scratch. Whether you're a fresh graduate or a working professional looking to switch careers, following these steps will help you stand out in a competitive job market and build a successful data science career.
To become a successful data scientist, you need to develop both technical and soft skills. Start by learning statistics, mathematics, and programming languages such as SQL, Python, or R. This will give you a solid foundation in data analysis and manipulation. Additionally, focus on developing soft skills like effective communication, presentation abilities, and business acumen. These skills are crucial for collaborating with stakeholders and effectively conveying data insights.
Coursera: "Mathematics for Machine Learning" by Imperial College London
edX: "Introduction to Python for Data Science" by IBM
LinkedIn Learning: "Data Science and Analytics Career Paths and Certifications"
Data science revolves around leveraging the right tools and technologies. Familiarize yourself with popular tools like TensorFlow, PyTorch, Pandas, and NumPy. Additionally, mastering dashboarding tools such as PowerBI or Tableau will enable you to effectively visualize and present data insights.
TensorFlow: Official TensorFlow website and documentation
PyTorch: Official PyTorch website and documentation
DataCamp: "Pandas Foundations" course by Daniel Chen
Tableau: Tableau Public Gallery for inspiration and learning from existing visualizations
Theory alone won't be enough to land a data science job. Seek opportunities for hands-on experience, such as internships, volunteering, or personal projects. If you're a working professional, look for data-related projects within your organization. Real-world experience will showcase your practical skills and demonstrate your ability to apply data science concepts.
Kaggle: Platform for data science competitions and datasets for practice
GitHub: Explore data science projects shared by the community
Towards Data Science: Blog platform with articles on various data science topics and tutorials
For those seeking advanced knowledge and a competitive edge, pursuing a Data Science Master's degree can be beneficial. Alternatively, industry-recognized certifications from renowned companies like Microsoft, Amazon, or IBM can validate your skills and increase your marketability.
Coursera: "Applied Data Science with Python" specialization by the University of Michigan
Microsoft Certified: Azure Data Scientist Associate certification
IBM: Data Science Professional Certificate on Coursera
Creating a strong online presence is crucial in today's job market. Highlight your skills, projects, and experiences on platforms like LinkedIn. Craft a well-structured and visually appealing resume that showcases your data science expertise and is ATS (Applicant Tracking System) compliant. Tailor your resume to the specific job requirements.
Alphaa AI: "How to build Resume for Data Science Role?" by Citizen Data Scientist
Jobscan: Online tool to optimize your resume for ATS compatibility
Utilize job search platforms like LinkedIn, Glassdoor, or Indeed to find suitable data science positions. Prepare for interviews by practicing commonly asked questions on platforms like Interviewquery, Datalemur, Hackerrank, or LeetCode. Demonstrating your technical knowledge and problem-solving skills is crucial to succeed in interviews.
Cracking the Coding Interview by Gayle Laakmann McDowell
Interview Query: Database of real data science interview questions and solutions
Data Science Career Guide by Springboard
Data science is a rapidly evolving field, so continuous learning is essential. Subscribe to data science blogs, follow industry leaders on social media, and seek out opportunities for professional development. Attend conferences, join data science communities, and engage with fellow professionals to expand your network.
Medium: Follow data science publications like
Twitter: Follow influential data scientists and organizations in the field
Data Science Meetups: Meetup.com or local communities for networking and learning opportunities
Securing a data science job from scratch may seem daunting, but by following these 7 steps and utilizing the suggested learning resources, you'll be well-prepared for success. Remember to build a strong foundation, gain practical experience, continuously upskill yourself, and showcase your abilities. With determination and perseverance, you'll unlock a rewarding career in the exciting field of data science.
We at Alphaa AI are on a mission to tell #1billion #datastories with their unique perspective. We are the community that is creating Citizen Data Scientists, who bring in data first approach to their work, core specialisation, and the organisation.With Saurabh Moody and Preksha Kaparwan you can start your journey as a citizen data scientist.
