10 Resources to Successfully navigate a career in Data Science!
- Here are 10 Data Science resources that answer the majority of career-related questions beginners have
- By no means is this list exhaustive. Feel free to share other resources in the comments below
The scope within the Data Science industry is diversifying and expanding with each second. The sheer volume of data produced is colossal, thus increasing the demand and work of the data scientists.
Furthermore, with each passing day, there are new algorithms to solve a problem, new tools, new libraries, etc which makes it only difficult to penetrate the data science industry. Hence, it only makes sense to begin your data science journey with the right resources.
In this article, we point you towards 10 resources that answer the most fundamental questions any beginner will have while beginning his data science journey!
Table of Contents
- Should I become a data scientist (or a business analyst)?
- How To Have a Career in Data Science (Business Analytics)?
- 14 Must-Have Skills to Become a Data Scientist (with Resources!)
- 12 Essential Tips for People starting a Career in Data Science
- Do you need a Certification to become a Data Scientist? 5 Things you Should Consider
- 5 Popular Data Science Languages – Which One Should you Choose for your Career?
- 10 Questions Every Data Science Beginner Asks (with Answers and Resources)
- How can you Master Data Science without a Degree in 2020?
- Want to Build a Career in Data Science? Learn from these 5 Data Science Videos!
- 8 Thoughts on How to Transition into Data Science from Different Backgrounds
- framework by Kunal
- Here are some of the questions to remove doubt
The data science buzz is really exciting and who doesn’t want to get the “Sexiest Job of the 21st century” but do you want to enter a field just for the sake of it? Let’s start off with the most basic question – Should you even become a data scientist?
Kunal Jain, the founder of Analytics Vidhya provides a structured framework on which you can rate yourself and see whether a career in data science and business analytics is for you or not.
Once you are sure that Data Science or business analytics is your career choice, it is time to work on your skillset but there must be questions like – Should I go for a degree course? How to land an internship in a data science job role?
In this article, Kunal draws upon his rich industry experience and provides a step-by-step guide on becoming a data scientist. He also provides very insightful tips on entering the data science industry.
Due to the colossal growth in the data science industry, there has been a jump in the number of jobs. However, there’s a caveat! There is a massive shortage of skilled professionals in the data science industry. So how can you break this trend and become an industry-ready professional?
This article consists of 14 hard and soft skills like fundamentals of data science, statistics, big data, model deployment, etc, and links to amazing resources to learn from!
Learning data science can be intimidating. Especially so, when you are just starting your journey. Which tool to learn – R or Python? What techniques to focus on? How many statistics to learn? Do I need to learn to code? These are some of the many questions you need to answer as part of your journey.
This guide is a rich resource filled with tips from an industry insider on how to navigate a career in data science from finding the right job role to resume building. You will find it all here.
I have come across many data science enthusiasts who are constantly confused about the pros and cons of getting a data science degree. If you have the same query then you are at the right place.
Data science certifications are ubiquitous – should you get one? If yes, which certification should you choose? What metrics do you need to look at before selecting a certification? In this article, we list down the different aspects of a data science certification you should consider before making your career choice.
I’m fairly certain all of you will have come across this eternal dilemma about choosing the “perfect” programming language to start your data science career. But there’s no one size fits all approach here. Each language has it’s own unique features and capabilities that make it work for certain data science professionals.
Some languages may be suitable for fast prototyping while others may be good at the enterprise level. So in this article, let us clear the confusion once and for all and see which is the best language that suits your data science career goals.
Data Science is relatively a new field and along with its benefits comes a lot of obstacles! You’ll notice how a few key questions constantly keep popping up – Where to start? What to learn and how to learn? How to find the right resources for data science?
In this article, we have discussed the 10 most asked questions by data science enthusiasts and beginners. These will help you figure out different aspects of your data science career, including your resume, interview process, and other best practices!
As I mentioned above, Data Science is relatively a new field, even the top-notch universities have started offering specialized courses only recently, which has created a sudden buzz and confusion in the industry.
In this article, we are going to break down the question and look at all its aspect. Some of the discussion points being –
- Can you get a job without a data science degree?
- Pros and cons of having a degree
- Alternatives to having a data science degree.
Navigating through the data science career is a tricky proposition and it can become a tough job without proper guidance but not everyone is privileged to get access to industry insiders.
Meetups are a great way to learn and connect but since Covid-19 that is not possible, Webinars are one of the best alternatives to learn and interact directly with industry experts. Some of the benefits of webinars are –
- Get to learn from industry insiders
- A great opportunity for networking
- Get career advice and data science knowledge from experts
One of the major queries asked by people is how they should transition into data science. People from all sorts of backgrounds – IT, Sales, Finance, HR, Healthcare, etc. – all want a piece of the data science pie. So let us clear the confusion once and for all.
The 8 backgrounds we cover in this career transition article include – software engineering, finance, UX, application development, and a non-technical fresher. The author has provided links to plenty of resources and learning paths to help you start your data science journey.
In this article, we discussed 10 articles that will help you get started on your data science journey. I hope it was fruitful and answered many of your questions.
Let us know in the comments if you have any queries.