10 Best Data Science Courses for Coding Interviews
These are the best online courses to learn Data Science from Udemy, edX, Programmerspace, Learnbay and Coursera for coding interviews.
Hello guys, if you are preparing for Coding interviews and looking for the best Data Science courses then you have come to the right place. Data Science is one of the most difficult topics and many candidates fail to answer questions related to Data Science, each code line may require some hundreds of programming instructions to execute, in a dual stack, recursive descent parsing algorithm.
I have been sharing useful courses for coding interviews for quite some time like earlier, I have shared best algorithms courses, Adobe Illustrator courses, Python, Data Science, and System Design courses for programmers, and today, I am going to talk about the best online courses to master Data Science for interviews.
These Data Science courses have been chosen from popular online learning platforms and websites like Udemy, Coursera, edX, Educative , Learnbay and these are created by experts who have seen the interviews from both sides of the table.
Along the way, I have also shared useful techniques where you can learn about some popular Data Science questions like DatAmin and designing a UI using data science and data analytics.
Our team of global experts have compiled this list of the 10 Best Data Science Certification, Tutorial, Course, Classes & Training program available online in 2021 to help you learn Data Science. These are suitable for beginners, intermediate learners as well as experts. Also, if you are interested, do check out Best Data Science Certification.
10 Best Data Science Courses for Programmers and Developers
Without wasting any more of your time, here is my list of some of the great, interactive, and fun online courses to learn Data Science. These are truly the best online courses you can join to learn this essential skill or improve your Data Science skill.
This Online Data Science 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 jumpstart in roles of a data scientist.
This is a very reputable and intensive 2 to 4 months long self-paced program. It includes 9 graduate-level courses that are taught by Harvard’s Professor of Biostatistics Rafael Irizarry and offered entirely online at a fraction of cost of traditional college, making it very accessible, affordable and flexible. The 9 courses that make up this data science program include R Basics, Visualization, Probability, Inference and Modeling, Productivity Tools, Wrangling, Linear Regression, Machine Learning and a Capstone project. Thus the courses begin with basic fundamentals and progress to culminate with a Capstone project where you apply the skills and knowledge acquired throughout the course series to a real world problem. By the end of the program you learn how to independently work on a data analysis project.
Upon completion students receive a Professional Certificate in Data Science that they can highlight to their potential employers.
If you choose the right set of courses, Harvard Data Science certificate is an appropriate option to enter the field of data science. You are going to get amazing values for the time and money you invest.
Duration: 9 courses, 2 to 8 weeks per course, 102 to 184 hours of total effort; Rating: 4.9
2. 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 Coursera platform. It aims to develop capability of learners to ask the right kind of questions, manipulate data sets, make inferences and create visualizations to publish results.
The Johns Hopkins’ University’s Data Science Specialization is the original flagship data science track. Being offered in collaboration with SwiftKey and Yelp, this specialization centers on the R programming language and its ecosystem. This program promotes practicality yet has an academic slant as well, manifested in its emphasis on the reproducibility of data science research.
There are 10 courses in this certification program with a Capstone project at the end. These courses cover tools that data analysts and data scientists work with like version control, markdown, git, GitHub, and RStudio, R Programming, Getting and Cleaning Data, Exploratory Data Analysis techniques for summarizing data, Reproducible Research, Statistical Inference, Regression Models, Machine Learning, Developing Data Products. The Capstone Project will be drawn from real-world problems and conducted with government, industry or academic partners. It will give the students an opportunity to demonstrate their data science skills to potential employers.
Beginner level experience in Python and some familiarity with Regression are listed as requirements for this course.
Duration: 10 courses, 8 months, 5 hours per week; Rating: 4.5
4. IBM Data Science Professional Certificate (Coursera)
This is a stand-alone Data Science and Statistics Certification program designed by the MIT Institute for Data, Systems, and Society (IDSS) and delivered by edX. The goal of this Micromasters data science program is to master the foundations of data science, statistics and machine learning.
This program features a five-course series formulated to strengthen their foundation in machine learning, data science, and statistics. It is an ideal course for students who wish to learn big data analysis.
It is one of the top data science programs and 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 same as the introduction to probability course taught on MIT campus and refined for 50 years. All the courses are taught by MIT faculty with high quality and hands-on learning approach. It is suggested that you have grasp of single and multi-variable calculus and linear algebra, as well as mathematical reasoning and Python programming to take up the program.
Each course in this MIT Data Science Certificate program runs for between 13 to 16 weeks and one is expected to spend approximately 12–14 hours per week on each course. Learners earn an individual Verified Certificate for each course that they pass and learners who pass the capstone exam at the end of the program receive a MicroMasters Program Credential.
Duration: 5 courses, 2 to 16 weeks per course, 12 to 14 hours per week; Rating: 4.6
5. Applied Data Science with Python Specialization by University of Michigan (Coursera)
This Coursera Data Science program has been developed by 4 professors of University of Michigan. It aims to enable learners with a basic understanding of programming to effectively manipulate and gain insight into data. It comprises of 5 courses that delve into data science methods, techniques and skills using Python programming language. It is expected that learners have a basic working knowledge of Python or at least other programming background. This program focuses on the application of statistical analysis, machine learning, information visualization, text analysis and social network analysis. It teaches popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into data. Specifically the 5 courses are — Introduction to Data science in Python, Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python and Applied Social Network Analysis in Python. Learners need to complete all five courses to earn the specialization certificate.
Each Coursera course is broken down into 4–10 weeks, each composed of video lectures, quizzes, assignments, projects, and assessments. Coursera “Specializations” can be obtained by completing a group of courses related to a specific skill. In this case, Data Science with Python.
Duration: 5 courses, 5 months, 7 hours per week; Rating: 4.6
6. Deep Learning Specialization (Coursera)
Deep Learning and Machine Learning skills are highly in demand. If you want to master them and build a career in AI, this Deep Learning Specialization course by deeplearning.ai is your best bet. Andrew Ng (CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist, Baidu and founding lead of Google Brain), a very reputed and respected name in AI industry has developed this program along with 2 professors of Standard university. This is one of the most sought after programs on deep learning.
Delivered as five courses, this data science specialization program teaches foundations of Deep Learning, how to build neural networks, and how to lead successful machine learning projects. It is a bottom-up approach to learning neural networks — powerful non-linearity learning algorithms, at a beginner-mid level. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. The 5 courses are namely, Neural Networks and Deep Learning, Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization, Structuring Machine Learning Projects, Convolutional Neural Networks and Sequence Models.
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.
The content is well structured and good to follow for everyone with at least a bit of an understanding on matrix algebra. Some experience in writing Python code is a requirement. The programming assignments are well designed in general. Finally, doing this specialization is a fantastic way to get you started on the various topics in Deep Learning.
Duration: 5 courses, 3 months, 11 hours per week; Rating: 4.9
7. Machine Learning Certification by Stanford University (Coursera)
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 real world. 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.
It’s no doubt that the Machine Learning certification offered by Stanford University via Coursera is a massive success. This is undoubtedly in-part thanks to the excellent ability of the course’s creator Andrew Ng to simplify some of the more complex aspects of ML into intuitive and easy-to-learn concepts.
Following topics are covered in this course –
- supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines);
- unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. Learners should have a basic knowledge of computer science principles and be familiar with basic linear algebra and basic probability theory.
The data science machine learning course is highly involving with multiple videos in each lecture, followed by quizzes and assignments. It is approximated that one would need 11 weeks to take the course spending around 5–7 hours a week.
Duration: 55 hours; Rating: 4.9
8. Data Science MicroMasters Certification by University of California, San Diego (edX)
This MicroMasters program is a series of graduate level courses in data science, designed by professors of University of California, San Diego and delivered online via edX. It is a very immersive program that can help to gain critical skills needed to advance as a data scientist. It aims to develop an in-depth understanding of the mathematical and computational tools that form the basis of data science and usage of those tools to make data-driven business decisions.
In this program, we will develop a well-rounded understanding of the mathematical and computational tools that form the basis of data science and how to use those tools to make data-driven business recommendations.
This UCSD Data Science certification program very effectively encompasses 2 sides of data learning — the mathematical and the applied in the form of 4 courses. These courses are — Python for Data Science, 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. Specifically, they learn how to use:
- Python
- Jupyter notebook environment
- Numpy
- Matplotlib.
- Git
- Pandas.
- Scipy.
- Apache Spark.
At each stage of completing a course learners earn a verified certificate for the course. After completing all four program courses, they earn the MicroMasters Program Certificate.
Duration: 4 courses, 10 to 15 weeks per course, 8 to 10 hours per week; Rating: 4.6
9. The Data Science Course 2021: Complete Data Science Bootcamp (Udemy)
The Complete Data Science Bootcamp program from Udemy provides the entire toolbox you need to become a data scientist. It progressively takes you from basics of mathematics and statistics to advanced statistics, machine learning and tableau and more. This course includes 27 hours of on-demand video, 88 articles, 144 downloadable resources and full lifetime access.
This Udemy data science course is the one of the most effective, time-efficient, and structured data science training available online. It covers 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 scikit-learn, Deep learning with TensorFlow. It includes wide variety of animations, quizzes, exercises and bonus materials. One does not need any prior experience to take up this course, everything is taught from the scratch with each topic building on the previous ones so you are prepped to work as a data scientist, handle real-life business cases and can take up more advanced specializations.
This course is made for those who have work experience less than 5 years and no experience. This course doesn’t require any prerequisites.
Duration: 27 hours on-demand video, 88 articles, 144 downloadable resources; Rating: 4.5
10. Machine Learning A-Z™: Hands-On Python & R In Data Science (Udemy)
Machine Learning is a very broad subject and becoming an expert in this field can be very challenging. This Data Science Machine Learning course on Udemy provides a clear pathway into the world of machine learning so participants can learn complex theory, algorithms and coding libraries in a simple and effective way. The course provides instruction in both Python and R programming languages, which is very distinguishing in itself. It has around 100,000 5-star ratings and more than 665,000 students enrolled making it the most popular Udemy Data Science course.
The course is very detailed and dives deep into all aspects of machine learning with over 44 hours of video content spread across 290 lectures. It covers Regression, Classification, Clustering, Association Rule Learning, Reinforcement Learning, Natural Language Processing, Deep Learning, Dimensionality Reduction. For each of these branches of machine learning, the course discusses between 2–7 different algorithms and shows how to create and code each one of them in Python and R. There are also takeaway templates included (in both Python and R) that students can download and use on their own projects. Students have the option of going with either Python or R (and skip the other) or try out both languages to truly master their machine learning skills.
The course takes an applied approach and is lighter math-wise. It is packed with practical exercises that are based on real-life examples, so apart from learning theory students get hands-on practice building their own models. There are quizzes and homework challenges too. Additionally students are expected to post solutions to exercises via Q&A or PM to allow discussion and feedback by instructors and fellow students.
The course has been created by two professional data scientists Kirill Eremenko and Hadelin de Ponteves, both of whom have years of real world data science experience under their belt. They bring both academic knowledge and real-life experience to the students and are known for their ability to make complex topics simple and easy to grasp.
Have a look at this option Machine Learning. We’ve taken Udemy but figure there are more options and learning curves that we should have been introduced with from basic to advanced and found really good course map under said site. We’ve been able to successfully apply this learnings and happy that we opted to explore on other alternative that offered more.
Duration : 44 hours on-demand video; Rating : 4.5
