Learning new skills to enhance your abilities to do a task effectively can be a hectic schedule especially if you are an employee. It’s hard to chase coaching or learning centers after spending 8-10 hours in the office per day. And when it comes to becoming technology-efficient specifically in the field of data science, you need to have the best qualification, handy experiences to get better job opportunities in this high in-demand profession. To ease out people’s hectic schedules without compromising with the quality of the education, online platforms like Coursera, Udemy, eDX and many more have a collection of data science certification and courses. Adding a touch of extra bonanza, these courses are free of cost.
Here is the list of top 10 best and free data science certification and courses of 2019.
IBM Data Science Professional Certificate – Coursera
The certification consists of a series of 9 courses that help students to acquire skills required to work on the projects available in the industry. The lectures span a broad range of topics including data visualization, analysis, libraries, and open-source tools. By the end of the program, the student can showcase his skills and enhance his resume through multiple assignments and projects. During the program, instructors help students to work on the fundamental techniques through examples. Students get plenty of opportunities to implement the skills learned using real-world tools and real-world datasets. Notably, no prior programming or computer science knowledge is required.
Duration: 3 to 5 weeks per course, 2 to 7 hours per week
Data Science Certification from Harvard University – edX
This program will teach students key data science essentials like R and ML with the help of real-world case studies. It is spread across 9 courses and is rated as among the best online master’s programs available on leading e-learning platform edX. The course spans R Basics, Visualization, Probability, Inference and Modeling, Productivity Tools, Wrangling, Linear Regression, Machine Learning followed by Capstone project to test and try. Students can gain experience with the tidyverse, including data visualization with ggplot2 and data wrangling with dplyr. They can become familiar with essential tools for practicing data scientists such as Unix/Linux, git and GitHub, and RStudio.
Duration: 9 courses, 2 to 8 weeks per course, 2-4 hours per week
MIT Data Science and Statistics Certificate – edX
This certification contains a series of 5 courses that will help students strengthen their foundation of data science, statistics and machine learning. They will learn to analyze big data and understand how to make data-driven predictions through statistical inference and probabilistic modeling to extract meaningful data for decision making. To have college-level calculus, mathematical reasoning, and python programming proficiency is advisable. Students may apply to different job roles after the completion of this certification. Students will learn to analyze big data and make data-driven predictions through statistical inference and probabilistic modeling to extract meaningful data for decision making. Students can learn to build machine learning algorithms to make sense of the unstructured data and gain relevant information. They can work on unsupervised learning methods such as clustering methodologies and supervised methods such as deep neural networks.
Duration: 5 courses, 2 to 16 weeks per course
Applied Data Science with Python Certification – University of Michigan
It is a 5-course program that will help students learn data science through the python programming language. Students need to have basic knowledge of Python and will be taught about popular python toolkits such as pandas, matplotlib, nltk, and networkx. The 5 courses will cover Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python and Applied Social Network Analysis.
Students can go beyond the basics of the Python programming environment including fundamental programming techniques such as lambda, reading and manipulating CSV files and the numpy library. They will learn to work on text mining and manipulation basics.
Duration: 5 months, 7 hours per week
Deep Learning Certification – deeplearning.ai
Students can learn how to build neural networks and lead successful machine learning projects through this 5-course specialization. Students will be taught about Python, Tensor Flow, RNNs, LSTM, Adam, Convolutional Networks and Xavier initialization among other aspects. The tutors for this course are Andrew Ng, Co-founder, Coursera & Adjunct Professor, Stanford University; Younes Bensouda Mourri, Mathematical & Computational Sciences, Stanford University, and Kian Katanforoosh, Adjunct Lecturer at Stanford University, deeplearning.ai, Ecole Centrale Paris.
Data Science MicroMasters Certification by UC San Diego – edX
The course is spread across multiple months and is ideal for students and professionals looking for an immersive learning program that goes deep into the concepts of data science. They will develop a well-rounded understanding of the mathematical and computational tools and how to use them to make data-driven recommendations. The course covers Python for Data Science, Probability and Statistics, Machine Learning Fundamentals and Big Data Analytics using Spark. Students will use Apache Spark to analyze data that do not fit within the memory of a single computer. They can also work on practical assignments and projects to enhance their portfolio and implement the topics covered in the videos.
Duration: 4 courses, 10-15 weeks per course
The Data Science Course 2019: Complete Data Science Bootcamp – Udemy
Then topics covered in this course are required to become a successful data scientist. Every topic builds on the previous one and discusses in and out of each of these areas. It allows students to get a better grasp of the facts and figures related to data science. The course provides a complete toolbox to enable students to understand this subject thoroughly. Students can understand the mathematics behind machine learning. They can solve real-life business cases and can start coding in Python. They can also learn how to use it for statistical analysis. The course includes 434 Lectures, 80 Articles, 129, Downloadable resources and full lifetime access.
Duration: 25 hours
Machine Learning Certification by Stanford University – Coursera
The course has been created by Andrew Ng who is the former head of Google Brain and Baidu AI Group. He has created this along with other professors from Stanford University. In this course, students will learn about Supervised learning, Unsupervised learning among other key areas. The course includes multiple case studies and applications to help students learn how to apply algorithms to build smart robots. It gives a better understanding of parametric and non-parametric algorithms, clustering, dimensionality reduction among other important topics. The students can go through real-world based case studies that will provide the opportunity to understand how problems are solved on a daily basis.
Duration: 55 hours
Data Science Master’s Degrees Online – Coursera
It is a collection of master’s programs from Coursera offered by top-rated universities around the world, such as the University of Michigan, the University of Illinois at Urbana-Champaign, University of Colorado Boulder, etc. After completing any of these courses, students will be able to become professional data scientists. Students will be eligible to get a job in various fields, such as agriculture, manufacturing, finance, technology, and transportation. They can learn from applied skill-based programs in data science. They can also learn how to use big data to understand the world, discover new insights, and optimize their decision-making.
Professional Masters Certificate in Data Science – Dartmouth College
This certification program from Thayer School of Engineering is the best option for those who are willing to earn a professional certificate in Data Science by learning all the essential concepts and skills. Students can develop high-demand skills that are required in today’s job market, such as Data Visualization, Machine Learning, Risk Management, and Predictive capabilities. After completion of the course, students will be able to take advantage of new opportunities and face new challenges in the field of Data Science. During the course, students can learn how to create visualizations, build linear and logistic regression models, and apply standard machine learning algorithms. They will work with different projects and develop a data science portfolio at the end of the course that can be shared with employers to showcase their skills.
Duration: 6 months