Specialize in specific areas after learning the basics of data science to set yourself apart.
Utilize Twitter and relevant hashtags like #rstats or #datascience to connect with data science communities and find learning opportunities.
Assess your starting point by considering your coding experience, comfort with mathematics and statistics, and past experience presenting reports based on data.
Deep dives
The Path to Becoming a Data Scientist
Becoming a data scientist involves figuring out where you are in terms of skills and knowledge, and where you want to go in terms of your career. It is important to learn the basics of data science and then specialize in specific areas that set you apart. Communication skills are also crucial as data scientists are required to convey statistical results in business terms.
Learning Resources for Aspiring Data Scientists
There are various learning resources available for aspiring data scientists, such as DataCamp, DataQuest, and Khan Academy for basics. Books from O'Reilly and other publishers are also recommended. However, it's important to find resources that match your specific needs and learning style. Seeking recommendations from other data science learners and leveraging hashtags on Twitter, such as #rstats or #pydata, can help you connect with communities dedicated to learning data science.
Using Twitter for Data Science Learning
Twitter is a valuable resource for aspiring data scientists. By searching relevant hashtags like #rstats or #datascience, you can tap into communities, follow leaders in the field, and find learning opportunities. Twitter communities often have Slack channels that provide further discussion and support. Connecting with experts on Twitter can help you stay up-to-date, learn the language of data science, and ask questions when needed.
Assessing your starting point for data science learning
When embarking on a journey to learn data science, it is important to assess your starting point. Ask yourself questions such as: Have you coded before? What language have you used? How comfortable are you with mathematics and statistics? Have you presented reports based on data? These questions help determine your current knowledge and skills. Additionally, understanding the specific domain you want to work in and familiarizing yourself with its lingo and career paths are crucial.
Different paths to a data science career
In the field of data science, there are different career paths that individuals can pursue. These include the roles of an analyst, engineer, and researcher. An analyst works closely with end users, converting business questions into data questions and presenting the findings. An engineer focuses on the back-end work, such as coding, working with databases, and building data pipelines. A researcher focuses on developing cutting-edge algorithms and tools. Most data scientists end up specializing in one or more of these roles. It is important to choose a path that aligns with your skills and interests.
Hugo speaks with Renee Teate about the many paths to becoming a data scientist. Renee is a Data Scientist at higher ed analytics start-up HelioCampus, and creator and host of the Becoming a Data Scientist Podcast. In addition to discussing the many possible ways to become becoming a data scientist, they will discuss the common data scientist profiles and how to figure out which ones may be a fit for you. They’ll also dive into the fact that you need to figure out both where you are in terms of skills and knowledge and where you want to go in terms of your career. Renee has a bunch of great suggestions for aspiring data scientists and also flags several important pitfalls and warnings. On top of this, they'll dive into how much statistics, linear algebra and calculus you need to know in order to become an effective data scientist and/or data analyst.
