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Last Updated on January 24, 2022
Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process. Let’s find out the best online courses on data science.
Why Data Science?
Data science or data-driven science enables better decision-making, predictive analysis, and pattern discovery. It lets you:
- Find the leading cause of a problem by asking the right questions
- Perform exploratory study on the data
- Model the data using various algorithms
- Communicate and visualize the results via graphs, dashboards, etc.
Will Data Science be in Demand in 2022?
In 2020, every person generated 1.7 megabytes of data in just a second. Internet users generate about 2.5 quintillion bytes of data each day. Data is being used to create massive change in many industries — healthcare, finance, marketing, business, etc.
However, data science in practice is very different from data science in theory. There are skills that will set you apart from the average data science aspirant.
When working in the industry, it doesn’t matter if you work an entire day to make your model 1% more accurate than before. These things might be important in a Kaggle competition, but they don’t matter to stakeholders.
What’s actually happening is that Data Engineering is replacing Data Science!
In the past, these organizations placed too much value on data scientists. They hired data scientists to build profitable models when they didn’t have a proper data pipeline in place.
Data scientists specialize in model building and are unable to do much with massive amounts of real-time, unstructured data flowing in. This meant that they were unable to add much value to the organization — since the data wasn’t prepared in a way that they required it to be.
Now that companies are starting to realize this, they are putting more emphasis on hiring data engineers. Read the following articles for a more in-depth understanding-
What Does a Data Scientist Do?
A data scientist analyzes business data to extract meaningful insights. In other words, a data scientist solves business problems through a series of steps, including:
- Ask the right questions to understand the problem
- Gather data from multiple sources—enterprise data, public data, etc
- Process raw data and convert it into a format suitable for analysis
- Feed the data into the analytic system—ML algorithm or a statistical model
- Prepare the results and insights to share with the appropriate stakeholders
If you are planning to start your career in Data Science and wish to know the skills related to it, now is the right time to dive in. Some popular websites offering the best Data Science courses around the web are listed down below.
Best Online Data Science Courses
Google Data Analytics Professional Certificate
4.8 (14,555 ratings) || 250,000 students enrolled
Over 8 courses, gain in-demand skills that prepare you for an entry-level job. You’ll learn from Google employees whose foundations in data analytics served as launchpads for their own careers. At under 10 hours per week, you can complete the certificate in less than 6 months.
Data Science Specialization
4.5 (37,544 ratings) || 445,639 students enrolled
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. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
4.9 (161,517 ratings) || 4,259,647 students enrolled
This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition.
- (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
- (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
- (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).
The course will also draw from numerous case studies and applications so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
Applied Data Science with Python Specialization
4.5 (23,628 ratings) || 307,397 students enrolled
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistically, 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 insight into their data.
Deep Learning Specialization
4.9 (115,754 ratings) || 614,383 students enrolled
In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.
Mathematics for Machine Learning Specialization
4.7 (10,547 ratings) || 135,956 students enrolled
For a lot of higher-level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics – stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.
How to Win a Data Science Competition: Learn from Top Kagglers
4.7 (1,106 ratings) || 104,862 students enrolled
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 Visualization with Tableau Specialization
4.5 (5,378 ratings) || 75,626 students enrolled
This Specialization, in collaboration with Tableau, is intended for newcomers to data visualization with no prior experience using Tableau. We leverage Tableau’s library of resources to demonstrate best practices for data visualization and data storytelling. You will view examples from real-world business cases and journalistic examples from leading media companies.
Statistics for Genomic Data Science
4.2 (1,295 ratings) || 27,939 students enrolled
An introduction to the statistics behind the most popular genomic data science projects. This is the sixth course in the Genomic Big Data Science Specialization from Johns Hopkins University.