We know that Nokia was one of the pioneers in the area of mobile phones, but with the arrival of the internet, other mobile companies began to understand how data, not voice was the future of communication. And Nokia was late to arrive in the smartphone category.
Much like the data being talked about here, the data in “data science” is the future of businesses. And you don’t want to be late like Nokia, right?
Then make sure to read this blog till the end because not only will we highlight the importance of data science but also the skills you need to pick up if you aspire to be a data scientist. This will help you fit into the job description of a data scientist but also make a career in data science.
- What is Data Science?
- What’s the hype?
- History of Data Science
- Skills you need to become a Data Scientist
- How to become a Data Scientist?
- A Data Scientist’s Career Path
- How are they making a difference?
- Is Data science under threat due to AI?
Quick Glance at a Data Scientist’s Skill set:
Image Source: Original
Let’s begin with - What is Data Science?
Data science is the study and extraction of data to arrive at meaningful conclusions on which business actions can be based. It analyzes huge sets of data using multidisciplinary approaches to identify certain patterns and trends that can be tapped into by businesses and derive valuable opportunities from them, that might otherwise seem insignificant, and thus go unnoticed.
They combine various practices, theories, and algorithms from the fields of statistics, mathematics, machine learning, artificial intelligence, and of course business analytics. In simple words, they help you understand the ‘why’ and ‘what’, predict the ‘what if’, and give solutions to the ‘what can be done’ aspects of business trends and analytical problems.
What is the hype around it and why is it important?
The competitive needs of modern businesses, to constantly have an upper hand, have brought data science to the forefront. And rightly so, because as the world goes online, especially after COVID-19, much of everything that a business is based upon has digitalized and been taken over by digital systems.
We can see this happening on a massive scale in payment systems, finance, video conferencing, storing information, and e-commerce. The penetration of technology and electronic devices has generated overwhelming amounts of data that can be either converted into a useful database or left to become redundant files that only add to digital pollution.
Image Source- LinkedIn Analytics
What is the history of Data Science?
You must’ve heard the term data science being interchangeably used with statistics as it was used during the 1960s as an alternative to statistics and data as we know it today didn’t exist.
It was only in 1974 that Peter Naur proposed the use of the term in relation to computer science in his “Concise Survey of Computer Methods”. And finally, it was in the ‘90s that data science gained recognition as a separate academic field that primarily dealt with the collection of data, its design, and analysis.
As the field matured and more research was conducted, it became one of the most lucrative professions of the 21st century.
Skills you need to become a Data Scientist
You might have a cursory idea about the skills needed to be a data scientist; if you had a “Quick Glance” at a Data Scientist’s Skill Set listed at the beginning.
Let’s take a deeper look into a few of these skills:
- TECHNICAL SKILLS:
- Quantitative Analysis- a technique that uses mathematical and statistical modeling, measurement, and research to understand finance and investment management
- Visualization Skills- recalling or forming mental images to make sense of data by using imagination
- Programming Skills- use of various programming languages to write commands, instructing a computer, application, or software program about the actions it must perform and how to perform them
- Computing Big Data- here you need to know how to mine big data and apply business analytics over large-scale structured, semi-structured, and raw unstructured data.
- Deep Learning- a type of machine learning based on artificial neural networks with multiple layers of processing that are used to extract a progressively significant level of features from data.
- Data wrangling- the process of removing errors and combining complex data sets to make them more accessible and easier to analyze through data discovery, structuring, cleaning, enriching, and validating data.
- Linear Algebra- is the most important math skill in machine learning and a must-have skill for a Data Scientist. As most machine learning models can be expressed in a matrix form; a dataset itself is often represented as a matrix where this branch of mathematics becomes extremely useful in data science.
- Multivariate Analysis- is a branch of statistics that is based on observation and analysis of more than one statistical outcome variable at a time. It’s the study of multiple variables in a data set with the objective to reduce and simplify data and identifying dependencies among variables
- NON-TECHNICAL SKILLS:
- Data Intuition- Intuition in data science is not about using your gut feeling. Here it refers to the intuitive understanding of concepts, in other words, how to apply the concepts. Do not make the mistake of thinking that to be a successful data scientist you only have to learn mathematical concepts.
- Creativity in Data science- Creativity will help you make innovative combinations of different tools and bridge the gap between the data you have and the data you want.
- Iterative Design- is a methodology that diagnoses errors and finds weak areas as you go about a project, regularly tweaking them rather than building it in one go.
- Business Acumen- this is a trait that every aspiring data scientist must try to inculcate as it becomes the game changer and a defining element that a data scientist has, but a traditional researcher/software developer may not.
You can refer to Think School by Ganesh Prasad- a YouTube channel that delivers in-depth business case studies.
- PERSONAL SKILLS:
- Teamwork- because data analysis is not supposed to be asynchronous. Help your teammates and let them help you.
- Intellectual Clarity- this helps you see what might otherwise seem unimportant
How can you become a Data Scientist?
The skills required for data scientists can be picked up in various ways:
- By earning a Bachelor’s degree- In fields like Statistics, Computer Science, and Data Science itself. Check more about colleges offering Data Science courses here: https://collegedunia.com/usa/data-science-and-analytics-colleges
- By learning Programming Languages- It is essential to learn relevant programming languages such as Python, R, SQL, and SAS while working with large datasets.
- By earning Certificates: Google Data Analytics Professional Certificate Course
- By learning Machine Learning: Machine Learning can be achieved through various algorithms such as Regressions, Naive Bayes, SVM, K Means Clustering, KNN, and Decision Tree algorithms, etc. You can start by learning them.
- By doing Internships in data-driven companies: This is where you will get hands-on learning experience.
- By working on open-source projects
After looking at how you can become a data scientist, it’s quite clear that a degree is not the only way you can become a data scientist. It is only one of the requirements and not the only one.
A person with basic knowledge of data science algorithms with a pinch of Machine learning models can become a professional data scientist in no time with just a bit of consistency.
Roles & Responsibilities of a Data Scientist
A data scientist’s role and day-to-day work may differ depending on the size and requirements of the business. In larger companies, a data scientist may be assisted by other analysts, engineers, machine learning experts, and statisticians to ensure efficient service delivery.
But, in smaller teams, a data scientist may have to play several and/or overlapping roles. In this case, their daily responsibilities might include engineering, analysis, and machine learning along with core data science methodologies.
The Data Science process involves the following applications:
- Descriptive analysis- examines data to gain insights into what happened or what is happening. It is primarily done by data visualizations using pie charts, graphs, tables, etc.
- Diagnostic analysis- examines data in a detailed manner to understand why something happened.
- Predictive analysis- uses historical data to make predictions about data patterns that may occur in the future.
- Prescriptive analysis- It gives an effective solution to the predicted issue. It can analyze the potential consequences of different choices and recommend the best course of action through simulations.
The Career path of a Data Scientist
Career path of a data scientist (insert the video here)
How are data scientists making a difference?
Data science is not only restricted to commercial enterprises. It can also help in areas of healthcare, medicine, sports, governance, and other socially impactful discourses. For example, Google has applied data science to identify breast cancer tumors that spread to nearby lymph nodes, which can be difficult for the human eye to detect.
During the COVID pandemic, data science helped us in mapping the spread of the infection in real-time by tracking location data, documenting its trends to understand its R0 (Reproduction Number), and detecting new variants in a timely manner.
Is Data science under threat due to AI?
Contrary to popular arguments put forward that AI will replace data scientists, Artificial Intelligence will only become a Data scientist’s smart assistant, which will get more work done even with much more complex data than was ever possible before. The total time spent on data collection can be reduced by a huge margin of more than 60% by automating the process through AI.
AI can not only help you detect the obsolescence of certain models but can also generate thousands of alternative models. So, are we still suspicious of this boon? Well, we shouldn’t be because it’s here only to make your work easier.
However, like any other field of science, data scientists need to keep adapting and upskilling their skill set every 12 to 18 months. Your Data science skills need to match the pace of the ever-evolving technology around you, so that you can bridge the gap and not fall behind.
Now that you know everything you need to know about the profession- the skills needed, courses you could take, the career path of a data scientist, and the job description; go be that dashing nerd you always knew was there inside you while earning the big bucks!