Data Science has become one of the most in-demand skills right now. According to Indeed, data science job posting continues to grow every year while the supply of skilled applicants in the domain has grown at a slower pace. Due to this imbalance, there are a number of ways for students and professionals to upskill themselves in the field of data science.
Data science: what is it, and who can learn
Data science is an interdisciplinary field of scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. A data scientist is skilled in organising and analysing massive amounts of data, and turning that data into useful intelligence for an organisation.
A data science career is also lucrative with a median salary of €65,000 in Europe. However, to become a data scientist or achieve a professional career in data science, you don’t need to spend a lot of money. Whether you are a newcomer or a professional, there is now a course in data science tailored for you.
Engineers, marketing professionals, software, and IT professionals are increasingly taking up a programme in data science. With a course in data science, you will be armed with the insights necessary to make sense of the data collected by companies from different touchpoints. Platforms like Coursera offer a number of data science courses and here is a look at some of the most in-demand courses right now.
With over 1 million students and a rating of 4.6 stars, IBM Data Science Professional Certificate is the most popular course on Coursera. This is a beginner course designed to help people kick start their career in data science and ML. The course helps learn about data science, the various activities associated with a data scientist’s job, and methodology to think and work like a data scientist.
Those joining the course will also learn Python & SQL, analyse and visualise data, and build ML models. The best part of this professional certificate course is that it does not require any degree or prior experience. With instructors who are either data scientists at IBM or part of IBM Skills Network, this course is designed by those who know data science better than most.
Google Data Analytics is a professional certificate offered by Google Career Certificates on Coursera. The course is designed as a path to a career in data analytics. In this course, the applicants learn in-demand skills necessary to turn you into a job-ready candidate in less than six months.
The course does not require a degree or a certificate to get started and those who complete the course will get a certificate from the institution providing the course content. Those who learn this course will gain skills such as data cleansing, data analysis, data visualisation, SQL, metadata, data collection, data ethics, and more. This is a beginner level course that takes approximately six months to complete at 10 hours a week.
Data Science Specialisation, as the name clearly implies, is a specialisation course designed to master a specific topic. This course offered by Johns Hopkins University is a top draw among data science courses on Coursera with 1.2 million learners. In order to participate in this course, you will need beginner level experience in Python and familiarity with regression is recommended.
Those joining this course led by associate professors from Bloomberg School of Public Health will gain skills such as Github, Machine Learning, R Programming, Regression Analysis, Data Manipulation, and more. This course is also offered in a number of languages and at a suggested pace of 7 hours per week, the course will be completed in approximately 11 months.
Applied Data Science with Python Specialisation is offered by University of Michigan and is an intermediate level course requiring some related experience in the field of data science. This course is designed to help learners gain new insights into data and learn to apply data science methods and techniques.
The course by assistant professors from School of Information at the University of Michigan can be completed in approximately 5 months. Those joining this course will gain skills such as text mining, Python programming, data cleansing, data visualisation, data virtualisation, Scikit-Learn, ML, and more.
Introduction to Data Science Specialisation is another course that is a specialisation course designed to launch your career in data science. This course offered by IBM has seen 12 per cent start a new career after completing this specialisation. There is no prior experience required for this course and can be completed in approximately four months.
The learners of this course will gain foundational data science skills to prepare for a career or further advanced learning in the field. Apart from data science, the skills gained through this course includes Relational Database Management System (RDBMS, Python Programming, SQL, Deep Learning, Machine Learning, Big Data, Data Mining, and more).
Data Science Fundamentals with Python and SQL Specialisation is offered by IBM and is designed to help the learners build their foundation for a data science career. The course offers hands-on experience with Jupyter, Python and SQL. The learners will also gain experience to perform statistical analysis on real data sets.
This is a beginner level course offering basic computer literacy and the Coursera page notes that “willingness to self-learn online” is necessary. The course can be completed in approximately six months at a suggested pace of 4 hours per week. The learners can walk away not only with a certificate but also skills in data science, Github, Python programming, Jupyter notebooks, probability and statistics, regression analysis, and more.
IBM Data Analyst Professional Certificate is another course where 31 per cent have started a new career after completing this specialisation. The course is designed to help the learners unlock their potential in data analytics and build job-ready skills for an in-demand career as a data analyst.
There is no degree or prior experience required for this course and the estimated completion time is 11 months. The learners get acquainted with skills such as Python programming, data analysis, Microsoft Excel, data visualisation, pivot table, Pandas, and IBM Cognos Analytics.
Advanced Data Science with IBM Specialisation is a special course led by IBM’s Chief Data Scientist Romeo Kienzler. It is designed to turn learners into experts in data science, machine learning, and AI. Those who join this course will become an “IBM-approved expert in Data Science, Machine Learning and Artificial Intelligence.”
This course is designed for those who are already in the data science industry and can be completed with a shareable certificate in approximately four months. The skills offered by this course include data science, IoT, deep learning, Apache Spark, Statistics, ML, and Long Short-Term Memory (LSTM).
This specialisation course is offered by CFA Institute and is designed for the needs of investment professionals. The course is termed as beginner level but requires knowledge of investment products and firms. The course offered in English can be completed in 5 months at a suggested pace of 3 hours per week.
The course will help learners harness the power of Data and Machine Learning. Those who join this course will learn to become the “translator” between an investment management team and data scientists to communicate “complex data science concepts clearly to clients.”
Practical Data Science Specialisation is offered by Deeplearning.ai and it brings together the interdisciplinary fields of mathematics, statistics, data visualisation, and programming skills using purpose-built ML tools in the AWS cloud. The course, at an advanced level, will help learners understand the difference between development environment and production environment.
To master this course, the learners must have working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses. The course can be completed in approximately 3 months and you will gains skills like Natural Language Processing with BERT, ML Pipelines and ML Operations (MLOps), A/B Testing and Model Deployment, Data Labelling at Scale, Automated Machine Learning (AutoML), Statistical Data Bias Detection, and more.