Companies are opening new positions (Data Visualization, Data Analyst, Data Scientist) every single day and the talent pool is really small. Salaries are ridiculously high for the role requirements and responsibilities.
A lot of people think these opportunities are only for people with high degrees, mathematicians or programmers and that’s not the case.
In this article, I’d like to explore all the opportunities there are for different people with non technical backgrounds to make a successful transition from let’s say finance or even marketing into data science.
Is data science for you ? Yes, chances are you would find a role that fits you.
I’ll discuss the different types of roles and some resources that will open your eyes letting you know if Data Science could be something for you to explore.
You will probably have to learn a few new and exciting skills, I will briefly describe the process of data science so you can identify which skills you would have to pick up and at the end will give you some resources that will help you get up to speed in those areas.
Salaries: According to Glassdoor, the Data Science professionals are getting an average salary of $150,000 per year. The shortage in talent availability is creating a gold rush for beginners who are willing to jump into this field.
The Data Science process as described in the book: Data Science Workflow for beginners follows some simple steps where different skill sets are required:
- Data Collection
- Data Processing
- EDA (exploratory data analysis)
- Insights Communication
In big companies the whole process might be splitted between different groups or even departments, so it’s important to develop an understanding of the whole process and at the same time the skills.
You might need to have some of the following skills (the ideal candidate should have a good balance):
Databases, Excel, SQL, Analysis, Understanding of Machine learning, Statistics, Basic Programming Skills (Python), Dashboard, Reporting, Graph Plotting, Verbal Communication skills.
You don’t need all of them, you might be non technical with the people, insights and communication skills necessary to see the big picture, to find meaning in the numbers and trends in the data.
Non technical skills: Data Processing, Data Cleaning, Reporting,Dashboards, Visuzalization.
On the contrary a technical person would be more inclined towards APIs, Python libraries, frameworks that would allow her or him to build models to help with the prediction or classification problems.
Technical skills: Algorithims, Models, Machine Learning,Databases.
- Data Scientist.
- Machine Learning Engineer.
- Data Architect.
- Big Data.
- Data Visualization.
- Data Manager.
So, if you are a total beginner I recommend you the following books so you can understand if this is an area you would feel comfortable working in:
I’m sure once you understand the process you will find some areas in your current role where you could start applying some of the techniques.
Then it’s advisable to start building some projects, the best way is to use resources like kaggle.com to help you solve challenges and at the same time practice with some real life datasets.
Start building a portfolio of simple projects, maybe you keep them in Kaggle , hosted on github or as a Jupyter notebook for example.
The projects will be a good way of showing people and even teaching other people how to solve some of the problems and get the right insights from the data.
If you come from a business background then it’s really easy to start thinking about questions you might be asking the data to get some interesting answers, it will also help to take a few programs like the one below:
And check other free resources like:
With all of the above you will have some of the steps already covered:
- Education & Certification
- Industry Experience
Industry Networking: you can use linkedin and also kaggle to interact with some communities which could recommend you or even tell you about some good opportunities.
Interviews: you need to start taking some interviews to understand the kind of questions and experience companies are looking for, It’s also an opportunity to showcase and present your projects to get general feedback and know what else you need to improve.
Hopefully some of these tips will help you in two ways:
Find out if data science is the right move for you.
Start taking the right steps to make the transition.