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Introduction

Coursera Learner working on a presentation with Coursera logo and

Data science is a rapidly growing field that has become essential for businesses and organizations of all sizes. It is the process of extracting insights and knowledge from data using statistical and computational techniques. The field encompasses a wide range of techniques, including machine learning, data visualization, and data mining.

If you’re new to data science and want to learn how to start from scratch, this guide will provide you with the tools, resources, and tips you need to become a data scientist.

Learn the basics of statistics and mathematics

Data science is built on a foundation of statistical and mathematical concepts. It’s essential to have a strong understanding of these concepts to be successful in the field. Start by learning the basics of statistics and probability, including mean, median, mode, and standard deviation. Then, move on to calculus, linear algebra, and optimization.

Learn a programming language

Data science is a field that heavily relies on programming, so it’s essential to have a strong foundation in at least one programming language. Python and R are the most popular languages for data science, but you can also use other languages such as Java, C++ or SQL.

Learn the basics of data visualization

Data visualization is an essential part of data science, and it’s essential to have a good understanding of how to visualize data. Start by learning the basics of data visualization using tools like matplotlib and seaborn in Python, or ggplot in R.

Learn the basics of machine learning

Machine learning is a subset of artificial intelligence that enables machines to learn from data without being explicitly programmed. Start by learning the basics of supervised and unsupervised learning, including supervised learning algorithms like linear and logistic regression and unsupervised learning algorithms like k-means clustering.

Practice with real-world datasets

Once you’ve learned the basics of data science, it’s important to practice with real-world datasets. Kaggle is an excellent platform that provides a wide range of datasets and challenges to work on

Get a certification

A certification can help you to stand out in the job market and demonstrate your knowledge of data science to potential employers. There are several data science certification programs available online, such as those offered by Coursera, edX, and DataCamp.

Join a community

Joining a community of data scientists can help you to learn from others and stay up-to-date with the latest trends and developments in the field. There are several online communities, such as Data Science Central, KDnuggets, and Kaggle, where you can connect with other data scientists.

In conclusion, learning data science from scratch requires time and effort, but it’s a rewarding process that can lead to a career in an exciting and in-demand field. By following the steps outlined in this guide, you can gain the knowledge and skills you need to become a data scientist.

Sign up today and start on the path to becoming a data scientist with interactive exercises where you can practice what you learned immediately.