Courses for a career in data science without STEM training

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Data analysts and data scientists will be the most in-demand jobs by 2025, according to the World Economic Forum’s Future of Jobs report released in 2020. The COVID-19 pandemic has led to digital transformation, thus leading to an increased demand for data science skills. Even employers are willing to pay long-term incentives and other perks to attract and retain data scientists and data engineers.

Not many people know that data science skills are not limited to learners and professionals in STEM fields. It’s not uncommon for companies around the world to create data science teams with talent from a wider variety of fields, including social sciences. Some of the most prominent positions include a data scientist, data architect, data engineer, data analyst, business analyst, analyst, and business analyst.

According to Coursera’s Global Skills Report (GSR) 2021, learners can prepare for an entry-level data analyst position with only around 64 hours of e-learning sessions. If you’re looking for a career in data science, here are the top skills to learn, according to Coursera’s GSR 2021:

Python programming

– Programming for all (Introduction to Python) by the University of Michigan

– Python for data science, AI and development by IBM

Statistics

– Statistical inference by Johns Hopkins University

Machine learning

– Machine learning by Stanford University

Probability and statistics

– Probabilities and statistics: at p or not at p? by the University of London

– Introduction to probability and data with R by Duke University

Machine learning algorithms

– Machine Learning Algorithms: End-to-End Supervised Learning by the Alberta Machine Intelligence Institute, University of Alberta

– Models and limits of artificial intelligence algorithms by LearnQuest

Applied machine learning

– Introduction to Applied Machine Learning by the Alberta Machine Intelligence Institute, University of Alberta

– Applied AI with DeepLearning by IBM

– Applied Machine Learning in Python by the University of Michigan

Data management

– Data management and visualization by Wesleyan University

– Prepare data for exploration by Google

Econometrics

– Econometrics by HSE University

– Econometrics: methods and applications by Erasmus University Rotterdam

Deep learning

– Neural networks and deep learning by DeepLearning.AI

– Introduction to deep learning and neural networks with Keras by IBM

SQL

– SQL for Data Science by University of California, Davis

– Databases and SQL for Data Science with Python by IBM

– Introduction to Structured Query Language (SQL) by the University of Michigan

According to a recent survey by another education technology organization, Great Learning, up to 92% of hiring managers said they faced a huge gap between supply and demand for jobs in data science. The talent shortage exists at all levels, from entry to mid-level positions, the survey found.

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