To become a data scientist, you need to learn everything from the simplest statistics problem too complicated algorithms in detail. Additionally, it is also necessary to be familiar with various tools to use for analysis workflows.
Below is a list of 10 useful YouTube videos to learn data science. These courses can teach the essential skills to get started on the road to landing a data science job.
You can make use of all these YouTube courses as a whole or you can choose topics that interest you. The only thing you need is to have the love for learning and the will to enhance your career path and career prospects by expanding your knowledge into other areas of the data science field.
1. Python for Data Science
Python is a general-purpose, high-level programming language that is widely used for all kinds of applications as well as data visualization. Data scientists are finding it to be far more useful than other languages, and it is frequently used in place of R, which was previously the golden child of the industry. This is largely due to its robust ecosystem and the fact that it is easier for non-programmers to pick up and use.
This full 12-hour hands-on python programming course was created by Maxwell Armi on FreeCodeCamp.org. The course teaches Python from scratch, using Python-related tools like NumPy, Pandas, and Matplotlib.
2. Statistics and Probability – Statistics for Data Science
Statistics is the mathematical study of collecting, organizing, analyzing, and interpreting numerical data and this full course is the complete explanation of all statistics necessary for data science. Data scientists are bound to pick up a lot of math, science, and programming skills; however, in the realm of data science, these basic statistical skills are not all that matters. A data scientist has to learn how to analyze and interpret data, as well as how to make data-driven decisions.
This 11-hour course on statistics covers everything statistics. It covers graphs, distribution, hypothesis testing, dispersion, permutations, distribution, and more.
3. Machine Learning Full Course
Machine learning is a branch of artificial intelligence that allows computers to learn and make decisions on data without being explicitly programmed. This is a powerful tool, and it can be applied to just about any problem, including data science.
This course is jam-packed with 10 hours of machine learning content created by edureka. The training covers the most important aspects of machine learning and covers topics to tackle real-world problems using those concepts.
It will provide examples for utilizing machine learning algorithms. The course states it is useful for beginners to experienced professionals.
4. R For Data Science Full Course
R programming is a popular tool for data science, and for good reason. The R language and package ecosystem offer a huge number of tools for data manipulation, data visualization, and analysis. Many people know this language as the “GNU S” General Public License programming language, as it was designed by the GNU foundation. R is both interactive and a powerful scripting language.
This 7-hour course by Simplilearn teaches the key concepts in data science and will help understand the data science packages in R. It covers some of the popular data science algorithms, for example, linear regression, logistic regression, decision trees, random forest to include time-series analysis. The course even covers the job market with salary information, skills needed for R-related roles, jobs, and resume structure of a data scientist.
5. Big Data & Hadoop Full Course
As a data scientist, you have two primary tasks: collecting data and analyzing it. Collecting data is a straightforward process, which involves directly downloading your data from the source. While collecting data, you have to be careful to ensure that you are collecting the right data. Analyzing big data can be a time-consuming process and one that requires a lot of expertise. Fortunately, the open-source solution known as Apache Hadoop is available to help you with your data analysis needs.
This 10-hour course by edureka covers big data and Hadoop. It’s a beginner course that will examine the Hadoop Ecosystem thoroughly by fully comprehending and explaining Hadoop concepts. The course is ideal for both beginners as well as professionals who want to master the Hadoop landscape.
6. Practical Deep Learning for Coders
Deep learning is the much talked about branch of machine learning that uses neural networks, algorithms inspired by the human brain. Deep learning is becoming a powerful force in the world of computer vision, natural language processing, and other fields, but it can be a challenging field to enter.
This 11-hour course was conducted by Jeremy Howard to offer a full introduction to deep learning. Full disclosure, to benefit from this course, it’s best to have at least 1 year of Python programming and a high school level math course. Most data science professionals have statistics experience.
The course covers the production and deployment of models to natural language processing.
7. TensorFlow 2.0 Complete Course (Python Neural Networks for Beginners Tutorial)
TensorFlow provides a mathematically precise way to represent machine learning models. It is a deep-learning framework that creates complex architectures in a highly flexible and efficient way. It can be used to implement deep neural networks, and to use them for supervised or unsupervised learning.
The 7-hour TensorFlow course was designed for Python programmers and consists of 8 in-depth modules. The basic fundamentals and methods in machine learning and artificial intelligence are covered like “core learning algorithms, deep learning with neural networks, computer vision with convolutional neural networks, natural language processing with recurrent neural networks, and reinforcement learning” according to their description.
8. Data Structures Easy to Advanced Course
Data structures and algorithms are two essential parts of any data science project. Data structures are important for data science because they are used to organize the data. Data structures help determine how to store data, how you access it, and how you use it.
This is an 8-hour full tutorial conducted by Google Engineer William Fiset. He covers the data structures literally step by step on how to utilize them. Each data structure will have working Java source code to help understand the concepts.
9. Dynamic Programming – Learn to Solve Algorithmic Problems & Coding Challenges
A dynamic programming approach is a way to solve a complex problem by breaking it into smaller subproblems and solving the subproblems while storing their results. Dynamic programming is often used for optimization problems, especially in the field of computer science.
10. Supervised And Unsupervised Machine Learning Full Course
For uninitiated, unsupervised machine learning (unsupervised machine learning) is a classification of machine learning algorithms that are used to learn patterns from unlabeled data. Unlike supervised machine learning, where the samples are labeled, unsupervised machine learning algorithms are not provided with the class labels for the data.
This 6-hour course by Simplilearn covers supervised and unsupervised machine learning and will teach the fundamentals and advanced concepts of machine learning. Viewers also get a hands-on approach to the various algorithms as well as Python demonstrations.
There are tons of educational videos online about how to learn data science and many courses on YouTube. There are some courses that are worth the money then there are free ones. Take advantage of them to get the basics and move forward from there.
Data science has become an indispensable skill in today’s tech-driven world. As technology continues to permeate every aspect of our lives, the need for cost-effective data-driven solutions grows ever more important. As the demand for data scientists increases, so do the options for education. One of the best ways to learn the skills and abilities necessary to become a data scientist is through YouTube or similar free sources until you find your niche.
Want to learn about more data science courses?
It is most important to have a solid foundation in key skills and PRACTICE! To learn more about where to get additional free and paid training and practice data science projects, see below.