Learning online became one of the most popular forms of learning. With great platforms like DataCamp, Udemy, and Coursera, anyone at any level can find something to learn from.
In this, I want to highlight top Data Science Courses, and Coding Courses available right now on Coursera.
Traditionally, data analytics have been linked to the technology industry. However, today, big data has a universal application and is impacting all the industries - from sports to an AI-based technology solution.
The size of the data businesses are producing has grown multi-folds, highly refined algorithms are being developed, and computational power has taken a great leap.
The convergence of these developments is propelling rapid technology evolution.
These transformations are shifting traditional job roles.
Industry professionals will need cutting-edge data analytics skills to thrive in this changing landscape.
In such challenging and exciting times, companies and individuals alike need to learn these skills to achieve their businesses and individual goals.
To help you decide the right course for you, the following are 2020’s ten most popular data science courses on Coursera and we believe this trend will continue.
First, let’s talk about the top Data Science Courses on Coursera:
Data Science Courses on Coursera:
Data science is an exciting field and many young minds aspire to become data scientists.
The reason for such an inclination is the promising future that data science and related fields like business intelligence have to offer to anyone with the necessary skills.
Data Science has been ranked as one of the hottest professions and the demand for data practitioners is booming.
Data Science Training is a preparation for the growing demand for Big Data skills and technology.
It empowers professionals with data management technologies like Hadoop, R, Flume, Sqoop, Machine learning, Mahout Etc. The knowledge and expertise of the skills is an added advantage for a better and competitive career.
Data science deals with scientific methods, processes, algorithms, and systems, to make deductions and draw insights from recorded data. This may be structured or unstructured data that help in making future-oriented decisions.
If you are aspiring to be a data scientist, there are various data science courses available online. Depending on your interests and professional priorities, you can choose what you want to learn.
You will learn Machine Learning, R or Python Language, Data Mining, and Business Intelligence.
The top online data science courses on Coursera are listed below:
This specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results.
Towards the end of the course, you’ll apply the skills learned by building a data product using real-world data, and upon completion, you’ll have a portfolio demonstrating your mastery of the material.
This course has a balance of breadth and depth in the curriculum. One thing that’s included in this series that’s usually missing from many data science courses is a complete section on statistics, which is the backbone of data science.
Overall, this Data Science specialization is an ideal mix of theory and application using the R programming language.
1. The Data Scientist’s Toolbox
2. R Programming
3. Getting and Cleaning Data
4. Exploratory Data Analysis
5. Reproducible Research
6. Statistical Inference
7. Regression Models
8. Practical Machine Learning
9. Developing Data Products
10. Data Science Capstone
As far as prerequisites go, you should have some programming experience (doesn’t have to be R) and you have a good understanding of Algebra. Previous knowledge of Linear Algebra and/or Calculus isn’t necessary, but it is helpful.
IBM (International Business Machines) is constantly working on making Big Data and Data Science easy for everyone.
One of the courses in the specialization is “Data Science Fundamentals”. This helps you to get started with data science learning.
This is an intriguing specialization for data science enthusiasts who wish to acquire practical skills for real-world data problems.
It appeals to anyone interested in pursuing a career in Data Science and already has foundational skills (or has completed the Introduction to Applied Data Science specialization).
You will learn about data visualization and data analysis. Through the guided lectures, and projects you’ll get hands-on experience setting about interesting data problems.
This specialization will help to solidify your Python and data science skills before diving deeper into big data, AI, and deep learning.
An added advantage is, upon completing all courses in the specialization and receiving the Specialization certificate, you will also receive an IBM Badge recognizing you as a Specialist in Applied Data Science.
1. Python for Data Science and AI
2. Data Analysis with Python
3. Data Visualization with Python
Introductory Python with some knowledge in Statistics would be very helpful. Remember, if your basics are strong, you can crack the tough things easily.
The University of Michigan produced this fantastic specialization focused on the applied side of data science.
This means you’ll get a strong introduction to commonly used data science Python libraries, like matplotlib, pandas, nltk, scikit-learn, and networkx, and learn how to use them on real data.
This series doesn’t include the statistics needed for data science or the derivations of various machine learning algorithms but does provide a comprehensive breakdown of how to use and evaluate those algorithms in Python.
Because of this, this would be more appropriate for someone that already knows R and/or is learning the statistical concepts.
If you’re rusty with statistics, consider the Statistics with Python Specialization first. You’ll learn many of the most important statistical skills needed for data science.
This course will help you to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain an insight into your data.
1. Introduction to Data Science in Python
2. Applied Plotting, Charting & Data Representation in Python
3. Applied Machine Learning in Python
4. Applied Text Mining in Python
5. Applied Social Network Analysis in Python
To take these courses, you’ll need to know some Python or programming in general, and there are some awesome lectures in the first course dealing with some of the more advanced Python features you’ll need to process data effectively.
This skills-based specialization is intended for learners who have a basic python or programming background.
This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Data Science or Machine Learning.
This program consists of courses providing you with advanced industry-ready skills extending a broad array of data science topics including open source tools and libraries, methodologies, Python, databases, SQL, data visualization, data analysis, and machine learning.
You will practice hands-on in the IBM Cloud using real data science tools and real-world data sets.
It is a myth that to become a data scientist you need a Ph.D. This Professional Certificate is suitable for anyone who has some computer skills and a passion for self-learning.
1. What is Data Science?
4. Python for Data Science and AI
5. Databases and SQL for Data Science
As such, no prerequisites are needed but an introductory knowledge in python will be helpful and is recommended.
No prior computer science or programming knowledge is necessary. It starts small, re-enforces applied learning, and builds up to more complex topics.
Finally, since all the others are Data Science Learning courses, you would like to compete in contests, so here is a course for you.
In the course below, you will learn to analyze and solve competitively predictive modeling tasks.
If you want to break into competitive data science, then this course is for you!
Participating in predictive modeling competitions can help you gain practical experience, improve and harness your data modeling skills in various domains such as credit, insurance, marketing, natural language processing, sales forecasting, and computer vision to name a few.
At the same time, you get to do it in a competitive context against thousands of participants where each one tries to build the most predictive algorithm.
Pushing each other to the limit can result in better performance and smaller prediction errors. Being able to achieve high ranks consistently can help you accelerate your career in data science.
Data Structure courses on Coursera:
Now, let’s talk about the top Courses on Data Structures and Algorithms on Coursera
Did you always think that Computer Scientists are pretty cool and it must be nice to become one? Here are the top courses for Data Structures and Algorithms on Coursera.
This specialization is a mix of theory and practice: you will learn algorithmic techniques for solving various computational problems and will implement about 100 algorithmic coding problems in a programming language of your choice.
No other online course in Algorithms even comes close to offering you a wealth of programming challenges that you may face at your next job interview.
To prepare you, this course has invested over 3000 hours into designing their challenges as an alternative to multiple-choice questions that you usually find in MOOCs.
For each algorithm you develop and implement, the course has designed multiple tests to check its correctness and running time. Instead of regular assignments, this program has about 100 uniquely designed coding problems.
Another highlight of these series of lectures is that along with the assignments there are not only one but two very intriguing real-world projects. With so many stellar features this course is an evident crowd favorite.
1. Algorithmic Toolbox
2. Data Structures
3. Algorithms on Graphs
4. Algorithms on Strings
5. Advanced Algorithms and Complexity
6. Genome Assembly Programming Challenge
This algorithms course will help you to get started with the basics. This specialization is created by Stanford and is aimed at beginners with some programming experience.
The highlight of this program is that instead of jumping over to low-level mathematical details, it emphasizes the conceptual understanding of the subject.
This certification consists of 4 courses in total and covers all the famous standard algorithmic topics.
1. Divide and Conquer, Sorting and Searching, and Randomized Algorithms
2. Graph Search, Shortest Paths, and Data Structures
3. Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming
4. Shortest Paths Revisited, NP-Complete Problems and What To Do About Them
In this program, you will go over the crucial concepts of algorithms and data structures that are required by every programmer.
This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations.
Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.
The classes that emphasize applications and scientific performance analysis of Java implementations.
The introductory lectures talk about elementary topics and searching algorithms following which you will focus on a graph and string-processing algorithms.
By the end of the classes, you will be able to apply the techniques in relevant projects.
An introductory knowledge about programming and knowledge on Java is recommended.
This Specialization builds on the success of the Python for Everybody course and will introduce fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Python programming language.
Towards the end of the course, you’ll use the technologies learned throughout the Specialization to design and create your applications for data retrieval, processing, and visualization.
4. Using Databases with Python
No prerequisite knowledge is required, this specialization course deals with the basics very well and creates a foundation for other concepts as well but introductory knowledge on Python will be helpful.
These best data science and data structure courses will help you grow in the field of data science, AI, machine learning, computer science, and programming as a beginner to the professional level.
These courses are highly rated courses on Coursera and have been very popular among computer science students throughout the world.