The-Almost-Astrophysicist
Priya, a senior data scientist, provides insights on learning data science from scratch and divides it into seven sectors: data processing, SQL, basic programming in Python, algorithms, modeling, data visualization, and math. She also shares her project experiences in the tech industry and offers a curated data science guide with cheat sheets. Priya advises learners to focus on pre-programming, working on projects, and understanding experimental design basics. She concludes that one should start as a jack of all trades, specialize on the job, and recommends free resources like textbooks, cheat sheets, and fun projects, and to not feel overwhelmed and take it step-by-step.
In this section, Priya, a senior data scientist at a startup under Uber, shares her own learning experience and segments different chunks of data science that she thinks one needs to know to become a data scientist. She gives concrete projects that she built in the tech industry and a curated data science guide with cheat sheets to help learners. She divides data science into seven buckets, which include data processing, SQL, basic programming in Python, algorithms, modelling, data visualization, and math. Priya emphasizes the importance of pre-programming, which involves data processing and SQL, and encourages learners to get their data together in a way that makes it viable to answer business questions. She also gives the tip of working on projects over just reading or watching videos about data.
In this section, the speaker discusses the different skills and knowledge required to become a data scientist. This includes knowledge of basic programming and algorithms, as well as competence in data visualization and modeling. They recommend practicing programming by working on problems and challenges from websites like LeetCode, and emphasize the importance of understanding different regression algorithms and translating model results into business value. The speaker also mentions the value of online learning platforms like Skillshare and provides tips for effective studying.
In this section, the speaker offers advice on how to tackle data science learning as a beginner. The speaker suggests picking one interesting concept that aligns with the data science framework and consistently studying it. When it comes to math, the speaker points out that you only need to know enough to understand data processing and algorithms, but not complex multivariate calculus. Additionally, as an entry-level data scientist, it is important to understand experimental design, particularly the use of sampling techniques, identifying data bias, and hypothesis testing. Finally, the speaker offers an example of a data science project they worked on that touches on all of the different aspects of the data science framework, including data processing, SQL, algorithms, and modeling. Overall, the speaker emphasizes that you don't need to be a specialist and just learning a little bit about each bucket is enough to get started.
In this section, the speaker emphasizes that one cannot specialize in everything from the beginning, and it's essential to be a jack of all trades and eventually learn to specialize on the job. They then provide a resource guide for those interested in learning data science. It includes free resources like textbooks, cheat sheets, and relevant Reddit posts. They recommend trying fun projects then getting a data set from Kaggle to create analysis or models. The speaker reminds everyone not to feel overwhelmed by the amount of work they have to do and to take it step-by-step.
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