Great, it shows that you are a very self-motivated person with a passion for data science and want to bring value to the company by solving complex problems. But you have no experience in data science and don't know how to get started. I know you well because I used to be the same. This article is specifically aimed at passionate and aspiring data scientists, answering the most common questions and challenges of entering the field.
I hope that by sharing my own experience, I will help you understand the career of entering the department for a career in data science and provide you with some guides to make your learning journey more enjoyable. Let's get started!
Data science talent gap
According to the International Data Corporation (IDC), global big data and business analytics revenue will exceed $210 billion in 2020.
According to LinkedIn's August 2018 U.S. Workforce Report, there was a surplus of data science talent in the U.S. in 2015. Three years later, this trend has Latest Mailing Database changed dramatically as more companies face a shortage of talent for data science skills. More and more companies are using big data to derive analytical insights and make decisions.
From an economic point of view, it all depends on supply and demand.
The good news is that the situation has changed. The bad news is this: As job opportunities in the data science field continue to increase, many aspiring data scientists struggle to find the jobs they want because their skills don't meet the needs of the market.
In the next section, you'll see how to improve your data science skills to stand out from the crowd of job seekers and ultimately reap the jobs of your dreams.
The ultimate guide
1. What skills are required and how do I master them?
To be honest, it's almost impossible to master all the skills in the field of data science because the scope is so wide. There are always some technologies that data scientists don't have mastered because different businesses require different skills. But there are some core skills that data scientists must master.
Technical competence, mathematical and statistical, programming and business knowledge. Although no matter what language is used, programming skills are a must. As data scientists, we should use our business communication skills to illustrate model results at the top of the business, based on mathematical and statistical support.
(1) Mathematics and statistics
For math and statistics, you can check out Randy Lao's related articles, which are very resource-rich.
When I first started studying data science, I read the book An Introduction to Statistical Learning — with Applications in R. I highly recommend this book to beginners because it focuses on the basic concepts of statistical modeling and machine learning and provides detailed and intuitive explanations. If you particularly like math, maybe you prefer this book: The Elements of Statistical Learning.
Regarding learning programming, especially for inexperienced beginners, I recommend focusing on learning a language, I personally prefer Python because Python is easier to learn. There has always been a debate about which language is better for Python or R, and I personally think the focus should be on how to help businesses solve problems, rather than which language to use.
(3) Business knowledge
Finally, I would like to emphasize that the understanding of business knowledge is also crucial.
(4) Soft skills
In fact, soft skills are more important than hard skills. We asked 2,000 business leaders at LinkedIn and we found that the soft skills they most wanted their employees to have in 2018 included: leadership, communication, collaboration, and time management. I think these soft skills play a vital role in the day-to-day work of data scientists.
2. How do I choose the right bootcamp and online course?
With the rise of artificial intelligence and data science, a large number of courses and training camps have sprung up, and there is no hope of missing out on opportunities.
So the question is, how do you choose the right learning resource for you?
My selection method is as follows:
No single course covers all the resources you need. Some courses overlap in some way, so it's not worth spending money on different but repetitive courses.
The first thing to know is what you need to learn. Don't blindly choose a course because of fancy and appealing headlines. By looking at job descriptions for data scientists on job search sites, you'll find some generic skills that companies need. Then search for the course by understanding the skills you lack.
Compare the quality courses offered by different platforms. Compare a few courses, and see other people's reviews (very important!). ）。 On the other hand, platforms such as Coursera, Udemy, Lynda, Codecademy, DataCamp, Dataquest, and others also offer many free courses.