In our last article, we discussed the evolving future of Artificial Intelligence and Data Science; and that the fields are expanding across all the industries be it pharmaceuticals, agriculture, banking, manufacturing, logistics and so on. In this article, we will talk about the various job roles in data science and how to kick start a career in it.
The article covers the following points:
Data Science Project Lifecycle
Roles involved in a Data Science/AI project
Kickstarting a career in Data Science and AI
Data Science Project Lifecycle
A data science project, like any software development projects, follows an end to end planning and execution cycle. Data Science projects usually follow one of these lifecycles: CRISP-DM or Knowledge Discovery in Databases (KDD) or Team Data Science Process (TDSP). More or less, all these lifecycle frameworks are based on the following steps:
Data understanding & Preparation
A typical Team Data Science Process lifecycle to structure a Data Science project looks like this:
Various Roles involved in a Data Science project:
To sum up, the Skills required to crack a Data Scientist Interview
The above discussion gives a sense of the process and various roles & responsibilities involved in a DS project, along with the required skill sets. Essentially, to become successful in any of the roles in Data Science, one should have the following skills:
Be comfortable at applying statistical and mathematical techniques – To be able to choose which test & algorithm to apply to a given business problem, it is important to grasp the mathematics and logic behind the algorithms. For instance, a data scientist must know why linear regression should not be applied to a binary classification problem (Left as an open question, think about it and share your thoughts in the comments section). Don’t worry if you don’t feel so comfortable, follow this course by Edvancer to learn enough Maths and Stats for a data scientist job.
Good programming skills in Python and/or R as both these languages are the most sought after skills in a data scientist. One need not learn both Python and R.
Machine Learning Algorithms – supervised and unsupervised.
Fluency in writing SQL and/or Hive queries to retrieve data from databases.
Data Visualisation using packages available in R and Python or tools like Tableau.
Business domain understanding
Action Plan to launch/transition into a career in Data Science and AI
Figure out a role that suits your interests, skill sets and aspirations.
If understanding the business and creating stunning reports based on insights from data catches your attention, then Data Analyst is the way for you.
If the concept of big data and parallel computing fascinates you and you are curious to deep dive into the data flow and design pipeline, being a Data Engineer might be the happiness for you.
If you are a full-on techie and mathematician and only want to code and design new software, machine learning engineer fits you best.
Last but not least, if you can imagine yourself fulfilling all these tasks, go for a Data Scientist
Find a course that best aligns with your career goals.
Professionals and freshers from Statistics or Mathematics or Engineering background can easily follow the path to become a data scientist by combining and brushing up their skills academic and programming skills.
Having said so, people from other backgrounds can also become data scientist by determination and hard work. Edvancer offers courses which are a mix of theoretical concepts and live projects for hands-on experience. These courses are designed to cater to the needs of everybody – whether you are a math wizard or stats pro or a newbie.
Edvancer has a suite of courses that you can take up according to your career interests. Edvancer also offers counselling support to guide you through your queries.
Keep improving your skills and knowledge
Participate in online hackathons and competitions to better your programming skills and machine learning concepts.
Find good datasets to practice on. There are many open datasets available on Kaggle, UCI Machine Learning Repository and scikit-learn which anyone can use to work on. Create a GitHub profile and keep posting your work there.
Read Data Science and AI related blogs and write blogs yourself to create an online presence in the Data Science community.
Networking helps a lot to get into a job. Be a part of data science and AI groups on LinkedIn and your local area, and attend webinars and conferences on Data science
5. Optimise your job search
Customise your resume according to the job description and company.
Highlight your recent Data science projects and publications.
Include the relevant skills and courses/certifications.
This article lays down a step by step approach to begin your career in data science and AI. Your journey in this amazing field has just begun. Don’t feel overwhelmed because you don’t have to do it all in one day. Learn and practice every day with determination and passion.
Good luck and support to your way from Edvancer team.
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