Data science is one of the most sought-after and lucrative careers in the 21st century. According to the U.S. The U.S. Bureau of Labor Statistics projects the demand for data scientists to increase 15% from 2019 to 2029, much faster than the average for all occupations. Data scientists analyze large and complex datasets, deriving insights from them and developing solutions in sectors that cut across business, health, education, social good.
So how exactly does one become a data scientist? What steps should you take to ensure success in this field? And what challenges and opportunities can you expect along the way? In this article, we will provide a comprehensive guide to becoming a data scientist in 2023, answering all of these questions and more.
What is Data Science?
Data science is an interdisciplinary field that combines mathematics, statistics, computer science, and domain knowledge together to gather process analyze and communicate data. The study of data science involves various methods and tools employed in the extraction of meaningful information from data such as patterns, trends, correlations, predictions, and recommendations.
Data science can be applied to various domains and problems, such as
Business
Data science can drive businesses to improve their process efficiencies, increase their revenues, cut down their costs, enlarge their customer satisfactions levels, and position them in a competitive manner. For example, data science can be used for business to segment their customer, personalize their product or service, forecast their sales or demand, detect fraud or anomalies and improve their decision making.
Health
Data science can be applied in health to facilitate health professionals and researchers in enhancing the quality and efficiency of care, preventing or treating diseases, discovering new drugs or therapies, as well as promoting public health. For instance, health scientists may use data science to diagnose diseases, monitor the condition of patients and predict outcomes or risks as well as recommend treatments or interventions and identify new biomarkers or genes.
Education
Data science can thus be applied by educator and learner in evaluating how students are doing and respond with remarks or advice, recommend what resources or strategies they need for learning purpose as well as developing adaptively or interactive curricular. Educators and learner can adopt data science to enhance quality of education and its efficiency in learning outcomes, personalization of learning experience, as well as lifelong learning.
What Skills Do You Need to Become a Data Scientist?
To become a data scientist, you need the perfect combination of technical skills and soft skills. Technical skills are certain abilities and knowledge that you require in order to accomplish tasks related to data science. Soft skills are the general qualities and abilities that you need to possess in order to be able to work well with other people and communicate your results.
Some of the most important technical skills for data scientists are
Programming
Programming is the art of coding that tells computers what to do. Programming is an essential skill for any data scientist as it enables one to work with data, implement algorithms, produce models and prototypes, automate functionality,and create applications. Some of the most popular programming languages for data scientists are Python, R, SQL, and Java.
Mathematics
Mathematics is the ability to use abstract signs and logical reasoning so as to represent problems and solve them. Data science requires mathematics because it gives them a theoretical groundwork and the tools with which they can work on data for computing, measuring or applying methods.
Statistics
Statistics is the science of collecting, organizing, presenting, analyzing and interpreting numerical data. Importance of statistics in a career in data science is that it helps the data scientists to describe the data, test hypotheses from known information, estimate parameters using the method of least squares, and predict.
Machine learning
Machine learning is the ability to learn from data and use algorithms for making predictions or decisions. Machine learning constitutes a very important part of data science because its foundation is based on machine learning which allows the data scientists in building models that are capable of performing complex tasks like classification, clustering, recommendation as well as generation.
Data Visualization
This involves a way of representing and communicating data with the use of graphical elements. This enables data scientists to study the data, observe any trends or abnormal values as well as present conclusions.
How to Become a Data Scientist in 2023?
To become a data scientist in 2023, you need to follow these steps
Step 1: Get your data science basics right. Begin with online courses, books, videos, and podcasts that teach you the fundamental theories and skills to become a data scientist. Some useful resources are
- Data Science Specialization from Johns Hopkins University on Coursera
- Introduction to Data Science in Python by the University of Michigan on Coursera
- Python for Data Analysis by Wes McKinney
- R for Data Science by Hadley Wickham and Garrett Grolemund
- Data Science from Scratch by Joel Grus
- DataCamp
- Kaggle Learn
- Dataquest
- edX
- Udemy
Step 2: Practice on real-world projects. Applying what you’ve learned to working on a real-world project with problems or challenges using data. Find datasets, competitions, or platforms where you get to work on things like
- Kaggle
- DrivenData
- Zindi
- DataKind
- UCI Machine Learning Repository
- Google Dataset Search
Step 3: Build your portfolio and network. Some places to showcase your work and achievements are
- GitHub
- Medium
Look for connections with other enthusiasts, experts or employers from the data science community on
- Meetup
- Stack Overflow
- Quora
Step 4: Prepare for interviews and get jobs. You can polish your resume, cover letter and online profile to highlight your skills, experience and projects. You also brush-up on technical or behavioural skills by taking mock interviews or doing coding challenges or case studies. Here are some resources like
- Cracking the Coding Interview, by Gayle Laakmann McDowell
- Interview Cake
- LeetCode
- HackerRank
You can find jobs and internships here
- Indeed
- Glassdoor
- Monster
- AngelList