With the best Coursera data science courses, you can gain valuable skills that will help you advance your career in this fast growing, in-demand field.
We live in a world ruled by data. Data science has established importance in every field, including medicine, business, engineering, architecture, design, government, academia, and more.
Whether you’re a student looking to pursue a career in data science or a professional who aspires to grow your skill set, the good news is you can take world class data science courses online that will help you reach your career goals.
In particular, Coursera offers data science courses from some of the world’s top universities and companies, and you can take all of these classes from the comfort of your home or wherever you want.
Some courses are stand-alone courses while others are Specializations (a collection of courses designed to let you dive even deeper into the subject).
While you can audit Coursera’s courses at no cost and get access to many of the materials for a given class, if you want to get full access to all materials (graded assignments and the like) and receive a Certificate for completing the course (incredibly valuable for your resume), you’ll need to pay enrollment fees which can vary by course.
Learn in-demand skills from the data analytics experts at Google, and be ready to launch your new career in as little as 6 months.
Six months, at 10 hours a week
In this collection of courses presented by Google, you will learn data science and analysis skills and get to practice them with hands-on projects. You will be provided with feedback based on your performance in quizzes and assignments. This Professional Certificate doesn’t require any past experience or education and will take you from beginner-level to job-ready.
You’ll learn a variety of valuable information, including Data Cleaning, Problem Solving, Critical Thinking, Data Ethics, Data Manipulation, and Visualization. These skills will be taught on various platforms including presentations, spreadsheets, SQL, Tableau, and R.
As a whole, these 8 data science courses will help you stimulate real-world data analytics scenarios that will help you in your workplace since they have been designed by instructors with decades of experience.
- Discover a wide range of concepts that will be relevant to the role of a junior data analyst
- Conduct an analytical thinking self-assessment
- Gain an understanding of decisions backed by data and how analysts use data to present their findings
- Understand why spreadsheets are important to data analysts
- Learn about different types of data, data formats, structured and unstructured data
- Link spreadsheets and SQL with databases and data sets, and extract data from them
- Clean a data set that is dirty
- Use functions and formulae in spreadsheet and calculations using SQL to understand data
- Visualize data on Tableau dashboards and narrate a convincing story
- Use RStudio and its packages to apply understanding to your analysis
“For anyone starting as a data analyst. The course is a great intro and is inspiring. It is well-paced and easily accessible” – Rachel L.
Build foundational data science skills in this popular series of courses from the pros at IBM.
This series of 10 courses takes 11 months to complete, at a pace of 4 hours a week
Offered by IBM, a leader in business transformation and having clients in 170 countries, this Professional Certificate series of courses has been developed by a team of well-experienced professionals led by Rav Ahuja. He has taught up to 1M students and offered 25+ courses.
The specialization requires no prior knowledge of computer science or any programming languages. The 10-course Professional Certificate prepares you for the job market. It uses open-source tools and libraries, Python, SQL, and databases to teach you data visualization, data analysis, statistical analysis, and predictive modeling.
You can then practice through real data science tools and real-world datasets in IBM Cloud.
In all except for the first course, there will be a series of labs to incorporate applied learning and prepare you for jobs.
You will be learning tools like JupyterLab, R Studio, Watson Studio, and GitHub. For practice, you will be using libraries including Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, ScipPy, and more.
You will be making models to predict housing prices, successful rocket landings, and many more interesting predictions.
- Understand what exactly data science is and what a data scientist’s regular day at work is like. To learn the activities they do and the thinking process they go through.
- Develop practically applied learning using tools, languages, and libraries that are used by professional data scientists.
- Import data sets, clean them to analyze and visualize data.
- Build various models pertaining to machine learning
- Pipelines using Python
- Apply various skills, tools, and techniques to build a model, complete a project and develop a report on it.
After completion, you will be earning a Professional Certificate from Coursera, a digital badge from IBM and will be able to share a Certificate on your LinkedIn.
“Terrific introduction to the Data Science course. Was extremely excited with the quality of content, speakers and a very honest attempt to making this course interesting.” –KK
Takes around 5 months to complete, at a speed of 8 hours a week
The Specialization is offered by DeepLearning.AI with a team of 3 well-experienced professionals, each having at least 1M students and 20 courses on Coursera. The company globally trains people to be a part of the AI community.
There are 5 courses in the Specialization, each building on the knowledge taught in the others. However, the Specialization is for intermediates, teaching intermediate Python skills, basic programming, loops, if/else statements, and data structures.
Each course focuses on different skill sets.
The first course familiarizes you with the trends in technology. Deep neural networks are the most important concept in this course. You learn how to build, train and apply fully connected.
In the next course, you will be familiar with hyperparameter tuning, initialization, batch normalization, and L2 and dropout regularization gradient checking.
In the third course, you will be able to understand complex machine learning settings and compare them to human-level performance.
The fourth course helps you build a convolutional neural network, and apply it to detection and recognition tasks.
The last course helps you build and train Recurrent Neural Networks and apply them to Character Level Language Modeling.
You Will Learn
- Artificial Neural Network
- Convolutional Neural Network
- Recurrent Neural Network
- Deep Learning
- Python Programming
- Neural Network Architecture
- Hyperparameter Tuning
- Mathematical Optimization
- Inductive Transfer and more
Once you complete the Specialization, you will receive a Certificate from Coursera which can be attached to your resume, your LinkedIn and can be showcased on your portfolio
“Amazing course for anyone wanting to jump in the field of deep learning. Andrew explains the details very well. The assignments were structured very good that provided detailed instructions. Thank you” – SZ
“Amazing course for anyone wanting to jump in the field of deep learning. Andrew explains the details very well. The assignments were structured very good that provided detailed instructions. Thank you” –AS
The course takes around 61 hours to complete
The course is being offered by Stanford, a highly acclaimed university. It’s taught by Andrew Ng, a leading authority on AI and Machine Learning who is considered one of the world’s most famous and influential computer scientists.
Machine learning is all around us. It’s helped shape the world of self-driving cars, speech recognition, effective web search, and more. This course helps you understand the most effective machine learning techniques and how they can be used in real world applications.
This course gives you the theoretical underpinning of learning and also teaches you how to apply these techniques to newer sets of problems.
You Will Learn
- Supervised learning including parametric/non-parametric algorithms, support vector machines, kernel, and neural networks
- Unsupervised Learning includes clustering, dimensionality reduction, recommender systems, and deep learning
- The best practices in machine learning include bias/variance theory, innovation processes in machine learning and in AI.
There are multiple case studies and applications that help you apply learning algorithms to build smart robots, understand texts, computer vision, database mining, and other various fields
The course has flexible deadlines and a Certificate that you can put in your resume and your LinkedIn.
“The teaching style is great! Presentations are lucid and assignments are at the right level of difficulty for beginners to get an understanding without getting bogged with superfluous details!” – AA.
Looking to build a career as a data analyst? Learn from the experts at IBM and build valuable skills to launch your career.
The course takes 11 months to complete at a pace of 3 hours a week, however, can be completed earlier too.
The Specialization is offered by IBM, the global leader in AI with clients in 170+ countries all over the world.
A team of instructors led by Raj Ahuja has developed a Specialization of 9 courses for beginner level Data Analysis. You’ll get job-ready skills that help you launch your career in data analytics, a field that has seen tremendous growth over the last decade.
No degree or previous experience is required for enrolling in this course.
You Will Learn
- The different roles of Data Analyst, Data Scientist, Data Engineer, and their responsibilities
- The Big Data platforms like Hadoop, Hive, Spart, etc
- Data Cleaning using spreadsheets with the help of filtering, sorting, pivot tables, and so on.
- How to tell a story using your data and spreadsheet software like Excel via charts and graphs.
- Beginner level Python programming including data structures, fundamentals, and analysis
- Relational databases using SQL by intriguing you to the world of SQL language
- How to import, clean, and manipulate data to summarize them and present them into machine learning Regression models using Python.
- Present the data in layman terms using data visualization libraries including Matplotlib, Seaborn, Folium, and so on
“This is an insightful and educative pathway for me to become a data analyst as it stands. Great thanks to both IBM and our promising Coursera for this opportunity. I see this course as excellent” -J
Takes around 4 months to complete, at a pace of 5 hours a week
The Specialization is aimed at beginners with zero SQL experience and looking to develop fluency.
The courses in this series increase with difficulty, having multiple projects teaching SQL basics, data wrangling, SQL analysis, A/B testing, distributed computing using Apache Spark, Delta Lake, and more.
Offered by UC Davis, by a team of 5 instructors, the Specialization includes 4 courses.
- Interact with and think critically about the data that has been already collected.
- Write simple and complex queries to select data from tables, including different types of data like strings, integers, and so on.
- Create tables and move data into them, by combining data
- Perform data/time calculations
- Clean data by depending, running quality checks, backfilling, and handling nulls.
- Segment and analyze data by conditional logics
- Work with large data sets and combine data with advanced analytics
- Understand the Spark architecture, queries within Spark, optimize Spark SQL, and building reliable data pipelines
“This course has really helped with optimizing queries that I work with everyday, enhancing my understanding of RDBMS, joins, analyzing and structuring exactly what you need and yielding those results.” – JP
The courses take around 4 months to complete at a pace of 8 hours a week.
These 4 hands-on courses teach you an intermediate level of natural language processing, an advancement that allows algorithms to interpret and manipulate human language.
After completing the courses in this Specialization, you’ll be able to build your own NLP applications, such as your own chatbot.
It is offered by Deeplearning.AI, designed by two professionals in natural language processing, machine learning, and deep learning. One of them is an instructor at Stanford and the other is a scientist at Google Brain.
- Use Logistic Regression, Naive Bayes, and word vectors to conduct sentiment analysis, translate words and use locality sensitive hashing to nearest neighbors
- Use dynamic programming, hidden Markov models, and word embeddings to autocorrect misspelled words, automatically complete partial sentences, and identify speech tags for words
- Use dense and recurrent neural networks, Siamese Networks in TensorFlow and Trax to conduct an advanced level of sentiment analysis, text generation and to identify duplicate questions
- Use encoded-decoder, causal, and self-attention to perform advanced machine translation of complete sentences, text summarization, question answering, and building of chatbots.
- Write simple English to French translation using precomputed word embeddings
- Write your own Word2Vec model that use a neural network to compute word embeddings
- Translate complete English sentences to german using an encoder-decoder attention model
“One of the best introductions to the fundamentals of NLP. It’s not just deep learning, fundamentals are really important to know how things evolved over time. Literally the best NLP introduction ever.” – SK
Get hands-on epxerience with Jupyter, Python, and SQL so you can start building the skills you need for a successful data science career.
The Specialization takes around 6 months to complete at a pace of 4 hours per week.
Designed by a team of experienced professionals at IBM, the Specialization teaches you beginner-level Python and SQL across 5 in-depth, hands-on courses.
This series of courses requires no knowledge of any other programming languages, and it provides a clear pathway to building fundamental skills for a successful career in data science.
You Will Learn
- Knowledge of Data Science tools such as Jupyter, R Studio, GitHub, Watson Studio
- Basics of Python including data structures, invoking APIs, and libraries such as Pandas and Numpy, working with files, and more
- Statistical Analysis techniques include descriptive statistics, data manipulation and visualization, probability distribution, hypothesis testing, and regression.
- Learn ANOVA, regression, and correlation analysis
- Develop Relational Database fundamentals including SQL query language, select statements, sorting & filtering them, using database functions, and accessing multiple tables
- Present findings in layman language to non-statisticians who require an understanding of the results
“Absolutely Loved this course!! Challenging at times to keep up with all the terms and processes. The course provided great insight into Data Science. Would highly recommend it as your first course.” – AJ
The 5 courses take around 6 months to complete, at a speed of 2 hours a week.
Offered by Wharton Online and UPenn, this Specialization was developed by a team of 13 instructors experienced in Operations, Marketing, Management Sciences, and more.
Throughout this series of classes, you’ll gain the knowledge and skills you need to make smart, data-driven business decisions to solve real world problems in the workplace.
You’ll learn how data science can be applied in all aspects of business, including marketing, finance, human resources, and other areas.
- Explain how data is used for recruiting and to evaluate performances
- Model demand and supply for various business scenarios
- Solve business problems with data-driven decision-making
- Understand the tools used to predict customer behavior
- Explain key ideas about consumer analytics and how business decisions revolve around them
- Understand people’s analytics such as leadership, hiring and promotion, and so on.
- Understand how data drives financial performance and forecast future financial scenarios
“This course includes a comprehensive overview of the all the basic models that are used to analyze data concerning customer behavior. The real-life examples made it easier to relate to those theories.” – MA
Takes around 5 months to complete, at a speed of 7 hours a week.
Offered by a team of 4 instructors at the University of Michigan, the 5 courses in this Specialization teach text mining, python programming, data cleaning, data visualization, and machine learning.
You’ll learn popular Python toolkits including Panda, Matplotlib, Scikit-learn, NLTK, Networks, and more
- Conduct a statistical analysis
- How to use functions such as Groupby. Merge, Pivot Tables, and so on
- Analyze data visualizations
- Visualize data by creating basic charts and realize decisions based on the design in frameworks
- Use applied machine learning to improve data analysis
- Understand process related issues to data generalizability, building ensembles, and limitations of predictive models
- Analyze the connectivity of a social network
- Understand the importance of nodes in a network, the concept of connectivity, and network robustness.
- Understand natural language processing methods to text and group them by similarity.
“The course had helped in understanding the concepts of NumPy and pandas. The assignments were so helpful to apply these concepts which provide an in-depth understanding of the Numpy as well as pandans.” – PK
A Final Word on Coursera Data Science Courses
While there is no single best Coursera data science class, the options above are some of the most popular and highest-rated courses available today.
Coursera has a massive catalog of classes not just in data science but also in tons of other categories, and they’re all presented by some of the world’s most recognized universities and instructors.
Remember, you can always check out Coursera’s course materials for free to “audit the class” before signing up and paying to fully participate and receive your Certificate for completion.
Have any questions about these Coursera data science classes? Comment below and we’ll help you out.