Data efficiency and new ways of growing revenue, make this list of best data science courses with certifications very important for any student or career person who is vying relevance and paying jobs.
Previously, we published a guide on the Best Jobs for Fresh Graduates in USA [Salary Review]. In this review, we are including essential guides that can also benefit business owners who need data insights to more professional efficiency and other vital needs in their businesses.
As we look at the best data science courses in the world, no stone is meant to be left unturned when it comes to breaking down every step of the journey and we kick off by really understanding what data science is all about, vital tips to shorten your research time as you go about the schools and available courses here.
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What is Data Science?
Following a well-defined explanation by IBM, data science combines math and statistics, specialized programming, advanced analytic, artificial intelligence (AI), and machine learning with specific subject matter expertise to dig out actionable insights hidden in an organization’s data. These insights can be used to guide decision making and strategic planning.
Best Data Science Courses
Depending on the degree in sight ( master’s, bachelor’s degree or PhD), we are gathering more elementary and deeper specialized data science courses that anyone could earn from any of these institutions outlines here.
These top data science courses include:
- Data Science Specialization
- Introduction to Data Science
- Applied Data Science with Python Specialization
- Data Science MicroMasters
- Dataquest
- Statistics and Data Science MicroMasters
- CS109 Data Science (Mostly Harvard)
- Python for Data Science and Machine Learning Bootcamp
Note: Different institutions may have slightly different titles for these courses or a combination of one or more for a more robust degree title. However, they remain the same as the elementary courses (maths, AI, machine learning, advanced analytic etc. may not change).
[To check out the list of institutions offering these courses as outlined above, simply copy any of the titles and paste on your favourite browser. A list of online schools will popup with their links]
To further guide you through acquiring the data science knowledge and desired certifications, below is a number of best schools online that offer certificates on each or some of the courses.
Core Areas in Data Science
Advancement in the field of data science has given birth to more important area in such a way that to get deeper into problem-solving with data science, students need to understand a couple of other modules that we consider core.
In other words, it has gone beyond just getting to learn about the types of data available. Other things one needs to learn as part of data science course modules include:
- Data Engineering
- Big data engineering
- Data mining
- Database management
- Predictive analytics
- Data Analytics
- Machine learning or cognitive computing ( to mention a few)
Data Science Courses with Certification Online Best Online Schools
Professional Certificate in Data Science from Harvard University (edX)
This Data Science Professional Certificate Program is offered by Harvard University through leading e-learning platform edX. It prepares you with key data science skills like R programming, machine learning, and others using real-world case studies to give you a jump-start in the roles of a data scientist.
Upon completion, students receive a Professional Certificate that they can highlight to their potential employers.
edX provides you the opportunities to learn these best data science courses free, however, there may not be any certificate issuance. In all, it will always be worthy of your time as you can as well pay for the certification.
Key Highlights
- Learn Statistical concepts such as probability, Statistical tools such as inference and modelling and how to apply them in practice.
- Learn how to use R to implement linear regression.
- Become familiar with essential productivity tools for practising data scientists such as Unix/Linux.
- Implement machine learning algorithms.
- Learn fundamental data science concepts through motivating real-world case studies.
2. IBM Data Science Professional Certificate (Coursera)
This certification comprises of 9 courses that cover following data science topics in detail – fundamentals of data science, open-source tools, and libraries, data science methodology, Python programming, working knowledge of databases and SQL, data analysis and visualization with Python, basics of machine learning followed by applied data science capstone project to help you consolidate your learning and apply skills learned to a real-life project.
Key Highlights
• Build databases, collect and analyze data from them using Python
• Use Python libraries to generate data visualizations
• Well designed content and all the topics are covered elaborately
• Graded Assignments with Peer Feedback
3. MicroMasters Program in Statistics and Data Science from MIT (edX)
The goal of this program is to master the foundations of data science, statistics, and machine learning. This MIT Micromasters certification comprises of 4 intensive online courses followed by a virtually proctored online exam to earn a certificate.
These graduate-level courses include Probability, Data Analysis in Social Science, Fundamentals of Statistics, Machine Learning with Python, Capstone Exam in Data Science and Statistics. The Probability course offered in this program is essentially the same as the introduction to probability course taught on MIT campus and refined for 50 years.
Key Highlights
• Learn Data analysis techniques, machine learning algorithms and apply them to real-world data sets
• An in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects
• Learn to analyze big data and make data-driven predictions through probabilistic modelling and statistical inference and apply appropriate methodologies for the extraction of meaningful information to aid decision making.
Develop and Build machine learning algorithms to make sense of the unstructured data and gain relevant insights into it.
4. Data Science MicroMasters Certification by the University of California, San Diego (edX)
It is a very immersive program that can help to gain critical skills needed to advance as a data scientist. This data science course aims to develop an in-depth understanding of mathematical and computational tools.
Again, it forms the basis of data science and the usage of those tools to make data-driven business decisions Probability and Statistics in Data Science using Python, Machine Learning Fundamentals and Big Data Analytics using Spark. Learners are introduced to a collection of powerful, open-source, tools needed to analyze data and to conduct data science.
Key Highlights
• Learn to load and clean real-world data.
• Learn to analyze big data using popular open-source software to perform large-scale data analysis and present your findings in a convincing, visual way.
• Learn to make reliable statistical inferences from noisy data.
• Use machine learning to learn models for data.
• Visualize complex data using tools covered in the lectures.
5. Applied Data Science with Python Specialization by University of Michigan (Coursera)
It is expected that learners have a basic working knowledge of Python or at least another programming background. This program focuses on the application of statistical analysis, machine learning, information visualization, text analysis, and social network analysis.
Key Highlights
• Analyze the connectivity of a social network
• Conduct an inferential statistical analysis
• Learn Visualization basics with a focus on reporting and charting
• Learn Applied data mining such as clustering and classification
• Learn to take tabular data, clean it, manipulate it, and run basic inferential statistical analysis on it.
6. Deep Learning Specialization (Coursera)
The course curriculum has been very carefully designed with neatly timed videos and has a well-regulated pace. You need to have a basic knowledge of mathematics and machine learning and some programming background to take the course.
Some experience in Python is an added advantage as the course is delivered using Python language.
Deep Learning and Machine Learning skills are highly in demand.
Key Highlights
• understanding of how neural networks work, along with How and Why We Make Them Deep
• Learn to Be able to build, train and apply fully connected deep neural networks
• Work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing
• Interviews and Personal stories of heroes and top leaders in Deep Learning.
7. Machine Learning Certification by Stanford University (Coursera)
You not only learn the theory of machine learning and statistical pattern recognition but also gain the practical knowledge to quickly and powerfully apply these techniques to solve new problems.
This course is recognized as one of the best data science courses available online.
This Machine Learning Certification Course has been developed by world-renowned AI expert Andrew Ng and provides details into most effective machine learning techniques and their implementation in the real world.
Key Highlights
• Work with large datasets from various fields and in different formats
• Understand parametric and non-parametric algorithms, clustering (k-Means algorithm), dimensionality reduction, anomaly detection among other important topics
• Programming assignments designed to help understand how to implement the learning algorithms in practice.
8. Microsoft Professional Program in Data Science (edX)
The Microsoft Professional Program in Data Science has been developed by Microsoft in collaboration with leading universities and employers and is available on online learning platform edX. In this program, you will learn data science fundamentals, key tools, and programming languages from industry experts.
Key Highlights
- Use Microsoft Excel to explore data
- Use Transact-SQL to query a relational database
- Create data models and visualize data using Excel or Power BI
- Apply statistical methods to data
- Use R or Python to explore and transform data
- Follow a data science methodology
- Create and validate machine learning models with Azure Machine Learning.
9. Data Science Specialization from Johns Hopkins University (Coursera)
This Data Science Specialization is a 10-course introduction to concepts and tools that you’ll need throughout the data science pipeline and is taught by renowned professors of Johns Hopkins University on the Coursera platform.
It aims to develop the capability of learners to ask the right kind of questions, manipulate data sets, make inferences, and create visualizations to publish results. Data Analysis techniques for summarizing data, Reproducible Research, Statistical Inference, Regression Models, Machine Learning, Developing Data Products.
Key Highlights
• Use R to clean, analyze, and visualize data
• Navigate the entire data science pipeline from data acquisition to publication
• Perform regression analysis, least squares and inference using regression models
• Balance both the theory and practice of applied mathematics to analyze and handle large-scale data sets
• Create models using formal techniques and methodologies of abstraction that can be automated to solve real-world problems
10. The Data Science Course: Complete Data Science Bootcamp (Udemy)
This data science course is one of the most effective, time-efficient, and structured data science training available online.
It covers the following topics in detail Basics of Data science, Mathematics (Calculus and Linear Algebra), Statistics, Python programming with NumPy, Pandas, Matplotlib, and Seaborn, Tableau, Advanced Statistical Analysis, and Machine Learning with stats models and sci-kit-learn, Deep learning with TensorFlow. It includes a wide variety of animations, quizzes, exercises, and bonus materials.
Key Highlights
- Understand the mathematics behind Machine Learning.
- Perform linear and logistic regressions in Python.
- Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop while coding and solving tasks with big data.
- Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross-validation, testing, and how hyperparameters could improve performance.
- Learn how to pre-process data.
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11. Data Science A-Z: Real-Life Data Science (Udemy)
Regardless of your prior experience with data science, it will help you realize your potential to become a data scientist.
This course is taught by Kirill Eremenko who has created 63 courses on Udemy and has taught over 900,000 students and is certainly one of the best tutors in the business.
This Data Science Course is very comprehensive and teaches data science step-by-step through real-world analytics. A very good balance of theory, practice, real-world business problems, take away templates and homework exercises make it one of the best courses on data science available online
Key Highlights
• Develop a good understanding of data science tools – SQL, SSIS, Tableau, and Gretl
• Create Simple Linear Regression, Multiple Linear Regression, Logistic Regression
• Use Backward Elimination, Forward Selection, and Bidirectional Elimination methods to create statistical models
Operate with False Positives and False Negatives and know the difference
12. Data Scientist Career Path for Beginners (Codecademy)
You will learn to analyze data with SQL and Python and build machine learning algorithms. You will also learn NumPy, pandas, sci-kit learn, and more.
No prior experience in data science is needed to take up this course. This Data Science program from Codecademy helps you master the skills you need to become a data scientist.
Key Highlights
• Learn SQL to talk to databases and manipulate tables
• Learn key statistics and analysis techniques
• Use Python for statistical analysis and create data visualizations to see the big picture
• Discover how to use supervised learning techniques, in which algorithms learn from many examples of past outcomes
• Learn how to perform learning on a dataset when we don’t have any of the answers, to begin with.
Frequently Asked Questions
Data science courses are designed with different modules, by different schools, based on the peculiarities of the course. Same way learners are supposed to identify the key needs in their career before proceeding with enrolling for any of the courses.
Because learners are asking important questions, we believe that providing answers to these questions will help you get what you are currently looking for.
What does a data scientist do?
The onus on on data scientists to leverage on their business and analytical skills as well as their ability to fetch, clean and present data for proper decision taking. A data scientist is poised with the responsibility of examining the questions that needs answers and where to actually get the right unstructured data that will be refined to help businesses and organization make the most informed decision.
What courses are best for data science?
Courses and prior skills that can reposition anyone for deeper data science include but not limited to computer science, statistics and mathematics. Other bodies of knowledge that can support your success of data science studies are artificial intelligence, deep/machine learning etc.
Which data science course has highest salary?
Take a loot at some high paying data science positions with average salaries hitting $65,000 per year:
- Business intelligence developer.
- Infrastructure engineer.
- Machine learning engineer.
- Data modeler.
- Big data engineer.
- Data architect.
- Data scientist.
- Enterprise architect.
Kindly Note: This data was directly gathered from Indeed( an online job placement/hunting website) with real people and organization searching and adverting jobs respectively. The domain and professional authority of this platform validates these data science careers as most paying in that companies and other organization, for the past few decades, have been leveraging on this portal for direct local and international job placement.
Which country pays highest salary to data scientist?
The country that pay data scientists the most are the United States and Switzerland with average salary $165,000 and $140,000 respectively. However, in Switzerland younger data scientists earn an average salary of $113,500. As senior level data scientists, it rises to an average of $130,000 a year while the chief data scientists can make an average of $48,000 a year.
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Which country has more demand for data science?
Here is a list of over 10 countries where data science experts are needed the most, around the world:
- The United States
- Switzerland
- United Kingdom
- Australia
- Isreal
- Norway
- China
- Canada
- India
- Belgium
- Netherland
- Germany
How long does it take to become a data scientist?
For an average person with zero prior knowledge of mathematics and statistics, it takes from 7 to 12 months of dedicated studies to become an entry-level data scientists.
What it takes to complete data science full course may vary from person to person and from schools to school based on a number of other variables such as the data science course in question, the institution and proximity with mentors and other helpful resources.
Which field of data science is in demand?
Answer: Data Analysis
Here is the reason: Data Analysis is that branch of data science that s concerned with analyzing data entails cleaning, altering, and processing raw data to extract useful information that can be used to guide business decisions are some of the most in-demand data analyst skills.
This aspect of the discipline is seen as the bedrock of data science in that it cover organizational and business needs deeper and encompasses of what forms a standard for other courses.
Note: All fields of data science are useful, and in demand all over the world.
Which country has shortage of data scientists?
A UK-based Calender has reported that Germany tops in the list of countries with shortage of several skilled workers. In the list released, it is estimated that by 2030, Germany could face an additional shortage of 3 million skilled workers with a huge number of these gaps favoring data science skilled workers.
This verifiable data and obvious shortage has created more rooms in the professional market, for anyone who really demonstrate deep knowledge in these areas mentioned here.
Which industry is best for data analyst?
Popular organizations/businesses with highest need for data analytics:
- Banking and Securities.
- Manufacturing.
- Insurance.
- Transportation.
- Government.
- Media & Entertainment.
- Pharma & Healthcare.
- Education.
Additionally, one of the amazing facts that make data science discipline great among many other modern courses is the fact that one could earn a degree from any of these best data science courses online, from any part of the world.
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Which degree course is best for data science?
Basically, a first degree in Computer Science with more emphasis on programming languages – this degree course will be more suitable for further training or degrees in data science and other analytic courses.
What are the courses in data science?
There are numerous courses in data science and they include:
- General Data Science.
- Big Data.
- Data Mining.
- Bioinformatics.
- Data Analysis.
- Data Visualization.
- Data Science Tools.
- Data Science Specialization.
- Introduction to Data Science.
- Applied Data Science with Python Specialization.
- Data Science MicroMasters.
- Dataquest.
- Statistics and Data Science MicroMasters.
- CS109 Data Science (Mostly Harvard).
- Python for Data Science and Machine Learning Bootcamp.
The list is endless and it is critical to access the course syllabus and also ascertain if or not the content of each courses, certifications and duration do align with your core interest; do this before proceeding with any of the data science courses that we continue to outline in this work.
What is the qualification for data science?
The best way to stand eligible for course enrollment and hiring afterwards is to get a relevant 1st degree in suitable courses such as computer science, statistic or even mathematics. Aside being accepted to go for any of the data science courses listed here, companies look into your backgrounds based as primary criteria.
