When you think of data science and its applications, you may think of a world where artificial intelligence and data meet to solve real-world problems. This view is somewhat accurate, but it only represents a small fraction of data science’s many purposes and use cases. Data science is an interdisciplinary field that combines computer science, statistics and mathematics to analyze data sets and find insights across a variety of software platforms, databases, businesses, industries and levels of complexity.
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Data scientists focus on collecting and cleaning data, analyzing data sets using different statistical methods, visualizing the data to identify patterns and using insights to solve problems.
With their broad range of in-demand skills, the expected job growth for data scientists is incredibly promising. According to the U.S. Bureau of Labor Statistics, data scientist employment opportunities are expected to increase 36% between 2021 and 2031, which is well above the 5% average for all professions.
If you’re new to or simply curious about the data science field, many top tech organizations and universities offer data science training to help you get started. However, selecting the right course for your experience level and goals can be daunting. To guide you in your data science learning and development, we’ve put together a list of some of the best data science courses on Coursera, covering a range of specialties and skill levels.
Coursera’s best 2022 data science courses
Google Data Analytics Professional Certificate
The Google Data Analytics Professional Certificate course on Coursera is a data analytics training program that includes eight modules and hands-on projects. The course has no prerequisites and is a highly-rated starting point for new data scientists who want to build their skills.
Participants will learn how to explore large data sets and extract insights with the help of industry-standard tools such as spreadsheets, SQL, Tableau and R programming. They’ll also gain data cleansing, problem-solving, critical thinking, data ethics and data visualization skills that are transferable across industries.
With over one million enrollments, Google offers a seven day free trial, but learners who want to continue working on the course after this period will need to pay $14/month after that timeframe. Learners are eligible to receive an official certificate of completion from Coursera upon course completion.
IBM Data Science Professional Certificate
IBM is considered one of the foremost world leaders in data and AI. In its IBM Data Science Professional Certificate course, you will learn how to analyze and visualize data and build machine-learning models using Python, SQL, and open-source tools and libraries. This curriculum includes nine lessons and one capstone project with frequent hands-on practice in IBM’s cloud environment.
This data science course examines machine learning methods for analyzing data and extracting value. You will learn how to work with large volumes of data, explore and visualize different types of relationships in data sets, build models using various techniques and evaluate the accuracy of models.
You’ll also be introduced to emerging technologies like deep learning, which are transforming many aspects of data science. In addition, you will gain an understanding of big data systems that process massive amounts of unstructured data, as well as programming languages such as Python and R that are used for manipulating and modeling data.
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This course has received a 4.6 out of 5 star rating based on over 57,000 ratings. There is no need for a degree or previous experience to take this popular online data science course with Coursera.
Students will earn a professional certificate from Coursera and a digital badge from IBM upon completion. Learners can enroll today at no charge and enjoy a seven day day free trial before committing to $39/month after the trial period.
Learn SQL Basics for Data Science Specialization
Learn SQL Basics for Data Science Specialization, a Coursera program offered by UC Davis, teaches you the basics of SQL, an important programming language and data management concept for data analysts. In this course, you’ll learn the basic constructs of SQL, how to create data analysis data sets, how to query and filter data, how to conduct feature engineering and the various functions you can use within queries.
By the end of this specialization course, students will have a solid understanding of what SQL is and how it can be leveraged for analytics purposes. Learners will also gain experience with SQL by working on four increasingly complex SQL projects that focus on SQL basics, data wrangling, SQL analysis and A/B testing. The ultimate goal is for students to learn how to use SQL creatively to analyze and explore data.
This course is available in English with subtitles in Arabic, French, Portuguese, Italian, Vietnamese, German, Russian and Spanish. The course has a 4.5 rating out of 5 stars, based on 7,452 ratings. A seven day free trial is available; if desired, learners can sign up for $39/month to continue learning after the trial ends.
Data Analysis and Presentation Skills: the PwC Approach Specialization
Data Analysis and Presentation Skills: the PwC Approach Specialization is a Coursera course from PwC, one of the top professional services firms in the world, that allows you to take a deep dive into data analysis, data cleansing, business dashboards, big data, pivot tables and data virtualization from their brand’s perspective.
The importance of organizational data analytics and business intelligence is the focus of this course. While becoming familiar with tools and techniques used in data analytics, you will also gain knowledge of data visualization tools and techniques and how to make data visualization presentations.
With a 4.7 rating out of 5 stars after more than 9,000 reviews, this specialization course should be a go-to option for mastering how to create effective business intelligence presentations. No prerequisites are required, making the course suitable for all learners. In addition, the course provider offers a seven day free trial before charging $49/month.
Data Science: Foundations using R Specialization
R is a programming language that is frequently used for statistical analysis and data visualization. In the Data Science: Foundations using R Specialization course offered by Johns Hopkins University, users can learn how to use R as a tool for exploratory data analysis to identify meaningful patterns in raw data, create simulations and visualize data.
The goal is to give students an understanding of the fundamental statistical techniques used in modern data analysis, which will help them prepare to solve problems with real-world data sets. Students in this course learn concepts such as:
- Exploratory data analysis
- Machine learning
- R programming
- Data analysis
- Data manipulation
- Regular expression
- Data cleansing
- Cluster analysis
This course has no prerequisites and a 4.6 out of 5 rating after more than 5,000 ratings. You will have access to the course materials and resources for seven days, but if you wish to continue your learning after your seven day free trial ends, you must purchase a subscription of $49/month.
Applied Data Science with Python Specialization
Applied Data Science with Python Specialization, a Coursera program offered by the University of Michigan, teaches students how to solve data science problems using Python. More specifically, you’ll learn how to gather, explore and analyze data in Python. This is an intermediate-level specialization, so learners must be familiar with programming in general or Python in particular before taking this course.
This specialization in Applied Data Science with Python will teach you how to use Python for:
- Text mining
- Data cleansing
- Data virtualization
- Data visualization
- Machine learning algorithms
- Natural language toolkit
This course has a 4.5 out of 5 star rating from over 24,000 reviews on Coursera. This course provider offers a seven day free trial, while continually learning past the trial period costs $49/month.
Foundations of Data Science: K-Means Clustering in Python
Foundations of Data Science: K-Means Clustering in Python is a specialty data science course on Coursera that is offered by Goldsmiths, University of London. This course teaches the fundamentals of data science, focusing on clustering and classification algorithms.
K-Means Clustering is a popular clustering algorithm in computer vision, image processing, machine learning and bioinformatics. After completing this course, you will be able to understand how these algorithms work and apply them to real-world problems.
You can take this course for free, but you won’t be issued a certificate. To receive certification upon completion of the course, you’ll need to pay a one-time fee of $49.
Microsoft Azure Data Scientist Associate (DP-100) Professional Certificate
The Microsoft Azure Data Scientist Associate (DP-100) Professional Certificate course teaches students how to process, store and analyze data using Microsoft Azure. The course is designed for those with experience in training machine learning models with Python and open-source frameworks like Scikit-Learn, PyTorch and TensorFlow. It takes around seven months to complete this course at a pace of four hours per week.
With this Professional Certificate, you can apply your existing Python and machine learning knowledge to data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.
This course provider offers a seven day free trial. For continued learning beyond that point, the cost is $49/month.
Choosing the right data science course for you
Coursera offers a wide variety of data science courses for data scientists of all backgrounds and experience levels to choose from. The best way to figure out which course is right for you is to go through each course description and find the one that suits your interests and skill level.
Another factor in choosing a data science course is whether or not it aligns with your career goals. TechRepublic recommends considering how much time you want to spend studying, the course length, whether or not the material focuses on your preferred industry and the cost of the course before making a decision.
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