Top 10 Data Science Courses that can Get You Placed at FAANG

Data science is one of the most lucrative career opportunities among modern tech aspirants

Modern organizations are ruled by data and disruptive technologies. Hence, the demand for efficient data science and data analytics professionals has risen exponentially. Data science, analytics, and machine learning are some of the most in-demand skills that all eligible FAANG aspirants should possess. Understanding the basics of data science is crucial for aspirants to produce high-quality solutions and aid their respective in achieving an edge over other companies. FAANG companies mostly fire data scientists who can work seamlessly and have a proper grip on the various technical skills that will help them become an asset to their organizations. Besides learning about the basics of data science, constant upskilling and specialization are also required to meet the evolving challenges that the industry faces every day. Here, we have mentioned the top data science courses that aspiring data scientists choose to land themselves a job in their dream FAANG company and embark on their career growth journeys.

Data Science Specialization

Offered by: Coursera in Partnership with John Hopkins University (Online)

This specialization covers the concepts and tools that the aspirants will need throughout the entire data science pipeline, starting from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, they will learn to apply the skills practically by building a product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.

Master of Applied Data Science

Offered by: University of Michigan, in partnership with Coursera (Online)

This master's curriculum will prepare the candidate to gain expertise in core data science concepts like machine learning and natural language processing. By diving deep into key topics such as privacy, data ethics, and persuasive communication, the aspirants will be prepared to succeed within modern organizations. They will also work with real data sets acquired from top companies and build a work portfolio, portraying all the skills.

Advanced Data Science with IBM Specialization

Offered by: IBM and Coursera (Online)

As a Coursera-certified specialization, this course will enable aspirants to have a proven deep understanding of massive parallel data processing, data exploration, and visualization, advanced machine learning, and deep learning. This course provides an in-depth understanding of the mathematical foundations behind all machine learning and deep learning algorithms.

Machine Learning, Data Science, and Deep Learning with Python

Offered by: Udemy (Online)

After completing this course, aspirants will learn to build artificial neural networks with Tensorflow and Keras, implement machine learning at a massive scale with Apache Spark's MLLib, and classify images, data, and sentiments using deep learning. This course provides abundant knowledge about the techniques used by real data scientists and machine learning practitioners in the tech industry and prepares the students for a move forward in this trending career path.

Statistics for Data Science and Business Analysis

Offered by: Udemy (Online)

This course is specially designed for candidates who wish to build a career in data science and business intelligence, and or anybody who wishes to learn about the function of statistics and its importance in the business domain. This program will help candidates understand the fundamentals of statistics, calculate correlation and covariance, make data-driven decisions, and carry out regression analysis.

M.Sc in Data Science

Offered by: Harvard University (Hybrid)

The Masters in Data Science at Harvard is a 2-year degree program organized and designed by the School of Engineering & Applied Sciences. Students will be required to take 12 courses that complement the learning outcomes set by the faculty of Computer Science and Statistics. At last, a thesis option is also available for those interested in gaining relevant research experience. By the end of the course, the graduates will be well versed in processes such as building statistical models, data collection and management, and several others.

M.Sc. in Statistics and Data Science

Offered by: Yale University (On Campus)

For this course, the department offers a broad training program in the main areas of statistical theory, probability theory, stochastic processes, asymptotics, information theory, machine learning, data analysis, statistical computing, and graphical methods. After completion of this program, the graduates can establish their careers in excellent positions in the best FAANG companies, and even in the government.

Master of Science in Statistics- Data Science

Offered by: Stanford University (On Campus)

This data science course helps develop strong mathematical, statistical, computational, and programming skills, in addition to providing fundamental data science education through general and focused electives requirements from courses in data science and its adjoining areas of interest.

M.Sc. in Data Science

Offered by: New York University (On Campus)

This M.Sc. in Data Science course is offered as a 2-years teaching program and also has a co-op work option. Some of the main courses that students will study in this program include hyper-personalization, artificial intelligence, machine intelligence, behavioral analytics, and graph analytics, to name a few. After completion of this course, the candidates will be eligible for positions like business intelligence analyst, data scientist, and data analyst, to name a few.

Principles, Statistical, and Computational Tools for Reproducible Data Science

Offered by: Harvard University (Online)

This course will appeal to students and professionals in biostatistics, computational biology, bioinformatics, and data science. The course content will blend video lectures, case studies, peer-to-peer engagements, and the use of computational tools and platforms such as R/RStudio, and Git/Github, culminating in a final presentation of a reproducible research project.