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You want to become a data scientist. How can you get started?

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Data science is one of the most in-demand and rewarding fields in the modern world. It combines skills from mathematics, statistics, programming, and domain knowledge to extract insights from data and solve real-world problems. But how can you get started on this exciting and challenging career path? Here are some tips to help you learn the basics, build your portfolio, and land your first data science job.

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1 Learn the fundamentals

Before you dive into the complex and specialized tools and techniques of data science, you need to master the fundamentals. This means learning the basics of programming, preferably in Python or R, which are the most popular languages for data analysis. You also need to understand the concepts and methods of statistics, linear algebra, calculus, and probability, which are essential for data manipulation, visualization, and modeling. You can find many online courses, books, and tutorials that cover these topics, and practice your skills on platforms like Kaggle or DataCamp.

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Collins Kgooho

Data Scientist||Data Analytics||Certified RPA Developer||Bcom Accounting Graduate
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Start by learning basics of Data Science tools Learn Basics of Python Learn Basics of SQL Understand Power BI Learn basics of Machine Learning both supervised and Unsupervised.

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2 Choose your domain

Data science is a broad and diverse field that can be applied to many different domains, such as finance, health, education, social media, e-commerce, and more. Each domain has its own data sources, problems, and solutions, and requires specific knowledge and skills. To become a successful data scientist, you need to choose a domain that interests you, suits your background, and matches your career goals. You can explore different domains by reading blogs, podcasts, books, and papers, and by following experts and influencers on social media.

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3 Build your portfolio

One of the best ways to showcase your data science skills and impress potential employers is to build a portfolio of projects. A portfolio is a collection of data science work that demonstrates your ability to collect, clean, analyze, visualize, and model data, and communicate your findings and recommendations. You can create your own projects based on your domain of interest, or use existing datasets and challenges from platforms like Kaggle or DataCamp. You should document your projects on platforms like GitHub or Medium, and use tools like Jupyter Notebook or R Markdown to create interactive and reproducible code.

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Enrique Companioni

Data Scientist | Data Analysis | AI | Machine Learning
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One of the ways to create a data science project portfolio is to start with competitions on DataCamp and Kaggle. On these platforms, there are competitions ranging from beginners to experts. Additionally, some professional data scientists, such as Nicholas Renotte, dedicate themselves to sharing their projects. These can serve as a foundation for more complex projects. Something important to mention is that a portfolio should be structured logically (by the domain of knowledge, complexity, etc.). Additionally, each project should be thoroughly documented. An idea would be to add an Executive Summary at the beginning, depending on the project.

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4 Learn the tools

Data science involves using a variety of tools and frameworks to perform different tasks and stages of the data analysis process. SQL is a language for querying and manipulating data from relational databases, while pandas is a Python library for data manipulation and analysis. NumPy is another Python library that specializes in scientific computing and linear algebra, while matplotlib is for data visualization and plotting. Additionally, scikit-learn is a Python library for machine learning and data modeling, and TensorFlow and PyTorch are frameworks for deep learning and neural networks. In order to be successful with data science, you should learn how to use these tools as well as others relevant to your domain and projects, and practice your skills on real-world data.

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5 Network and apply

The final step to becoming a data scientist is to network and apply for data science jobs. Networking is a great way to learn from others, get feedback, find mentors, and discover opportunities. You can network by attending events, meetups, workshops, and conferences, by joining online communities, forums, and groups, and by reaching out to people who work in data science or your domain of interest. Applying for data science jobs is a challenging and competitive process, but you can increase your chances by preparing your resume, portfolio, and online presence, by researching the company and the role, and by practicing your technical and behavioral skills for interviews and assessments.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Paul Savchuck

Python Engineer | Clguba Ratvarre
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Apply, apply, apply, apply. Find companies, ask their DS, how they got their job, train interview, since it's still crucial to path that step. Don't give up, always continue to move forward. You really should think about this as about a game, till you get the job, where work will start. Why as a game? Less stress.

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You want to become a data scientist. How can you get started?

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3

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1 Learn the fundamentals

2 Choose your domain

3 Build your portfolio

4 Learn the tools

5 Network and apply

6 Here’s what else to consider

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