Overview
- Here there is 10 data science resources that answer most career-related questions available to beginners.
- This list is by no means exhaustive.. Feel free to share other resources in the comments below..
Introduction
The scope within the data science industry is diversifying and expanding with every second. The sheer volume of data produced is colossal, which increases the demand and the work of data scientists.
At the same time, with each passing day, there are new algorithms to solve an obstacle, new tools, new libraries, etc., which only hinders penetration into the data science industry. Therefore, it only makes sense to start your data science journey with the right resources.
In this post, we indicate 10 resources that answer the most fundamental questions any beginner will have as they begin their data science journey.
Table of Contents
- Should I become a data scientist (the business analyst)?
- How to have a career in data science (Business analysis)?
- 14 must-have skills to become a data scientist (With resources!)
- 12 essential tips for people starting a data science career
- Do you need a certification to become a data scientist? 5 things you should consider
- 5 popular data science languages: Which one should you select 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?
- Do you want to develop a career in data science? Learn from these 5 data science videos!
- 8 reflections on how to transition to data science from different backgrounds
- Marco de Kunal
- These are some of the questions to clear doubts.
The data science buzz is truly exciting and who doesn't want to get the “sexiest job of the 21st century” but you want to enter a field just for the sake of doing it? Let's start with the most basic question: Should you become a data scientist?
Kunal Jain, the founder of DataPeaker, provides a structured framework in which you can qualify yourself and see if 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, time to work on your skills, but there must be questions like: Should I go to a degree course? How to Get an Internship in a Data Science Job Position?
In this post, Kunal draws on his rich industry experience and provides a step-by-step guide to becoming a data scientist. It also provides very interesting tips on how to enter the data science industry..
Due to the colossal growth in the data science industry, there has been an increase in the number of jobs. Despite this, There is a warning! There is a huge shortage of trained professionals in the data science industry. Then, How can you break this trend and become an industry-ready professional??
This post consists of 14 hard and soft skills, like data science arguments, statistics, big data, model implementation, etc., And links to amazing resources to learn from!!
Learning data science can be intimidating. Especially when you are just starting your journey. What tool to learn: R o 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 must answer as part of your journey.
This guide is a rich resource filled with advice from an industry expert on navigating a career in data science from finding the right job to resuming construction.. You will find it all here.
I have come across many data science enthusiasts who are constantly confused regarding the pros and cons of obtaining a data science degree.. If you have the same query, then you are in the right place.
Data science certifications are ubiquitous, Should i get a? If so, Which certification should i select? What metrics should you pay attention to before choosing a certification? In this post, we list the different aspects of a data science certification that you should consider before selecting your career.
I'm pretty sure all of you have encountered this perennial dilemma regarding programming language selection. “perfect” to start your career in data science. But there is no one-size-fits-all approach here. Each language has its own unique characteristics and capabilities that make it work for certain data science professionals..
Some languages may be suitable for rapid prototyping, while others may be good at the business level. Then, in this post, let's clear up the confusion once and for all and see which language is best suited to your data science career goals.
Data science is a relatively new field and, along with its benefits, many obstacles arise. You will notice how some key questions keep popping up constantly: Where to start? What to learn and how to learn? How to find the right resources for data science?
In this post, we have analyzed the 10 Frequently Asked Questions from Data Science Enthusiasts and Beginners. These will help you discover different aspects of your career in data science!, including your resume, the interview procedure and other best practices!
As I mentioned previously, data science is a relatively new field, even top-tier universities have only recently begun offering specialized courses, which has created a sudden buzz and confusion in the industry.
In this post, we are going to break down the question and analyze all its aspects. Some of the talking points are:
- 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 a data science career is a complicated endeavor and can turn into a difficult job without proper guidance., but not everyone has the privilege of having access to industry inside information.
Meetings are a great way to learn and connect, but since Covid-19 that is not feasible, webinars are one of the best ways to learn and interact directly with industry experts. Some of the benefits of webinars are:
- Learn from industry experts
- A great possibility for networking
- Get professional advice and data science insights from experts
One of the biggest questions people ask is how they should transition to data science. People of all kinds of backgrounds (TI, sales, finance, human Resources, medical care, etc.) they want a piece of the data science pie. So let's clear up the confusion once and for all.
The 8 Background information we cover in this career transition post includes: Software Engineering, finance, UX, app development and a non-technical update. The author has provided links to a host of resources and learning paths to help you start your data science journey..
Final notes
In this post, we discussed 10 posts to help you start your data science journey. I hope it has been fruitful and has answered many of your questions..
Let us know in the comments if you have any questions..
