Big Data Full Course for Beginners
12 hours
Eleven hours on the big data stack, from what the term means to the tools that do the work.
Data science here means the whole path from a raw table to something somebody can act on: cleaning and shaping data, the statistics that stop you fooling yourself, and the charts that carry a finding to people who were not in the room. Data visualisation has a page of its own beneath this one, covering the individual tools in depth.
Everything listed under data science is free to start, and most of it is pitched at beginners, with a smaller set of intermediate and advanced courses for people who already work with data and want a specific gap filled. The courses come from YouTube, where the teaching is done in public and you can judge an instructor before spending an evening with them.
Each course is compared on the same terms: level, length, price, certificate and the source it comes from. Filter by level to skip the introductions if you have already done them.
120 courses
12 hours
Eleven hours on the big data stack, from what the term means to the tools that do the work.
1 hour
A live session working one real dataset end to end, from first look to engineered features.
2 hours
Exploratory Data Analysis (EDA)
Two and a quarter hours of exploratory data analysis in Python, taught in Hindi.
3 hours
Exploratory Data Analysis (EDA)
Three hours on looking at a dataset properly before modelling it, and on the features built from what you find.
4 hours
Three and a half hours of OpenCV in Python, from reading an image to detecting faces.
7 hours
Seven hours building neural networks in TensorFlow 2, for people who have not built one before.
57 minutes
The opening lecture of MIT's introductory deep learning course.
9 hours
Eight and a half hours on deep learning, from neural networks to the frameworks that train them.
10 hours
Ten hours of deep learning from Edureka, from the neural network up.
1 hour
An hour on descriptive statistics: the measures that summarise a dataset before any model touches it.
7 hours
A seven-hour tutorial on probability and statistics for data science, from a Stanford PhD.
7 hours
Twenty-seven lessons on statistics for data analysis and data science, taught in Hindi.
24 hours
Forty-four lessons covering the statistics behind machine learning, from sampling to hypothesis testing and interview questions.
5 hours
Six hours of statistics for data science in one sitting, from descriptive measures through distributions to hypothesis testing.
1 lesson, 59 minutes
A single session, 59 minutes, published on YouTube by storytelling with data. Watched here on the course page.
1 lesson, about 2 hours
A single session, about 2 hours, published on YouTube by Flourish. Watched here on the course page.
1 lesson, 53 minutes
A single session, 53 minutes, published on YouTube by storytelling with data. Watched here on the course page.
1 lesson, 51 minutes
A single session, 51 minutes, published on YouTube by storytelling with data. Watched here on the course page.
1 lesson, about 2 hours
A single session, about 2 hours, published on YouTube by Pragmatic Works. Watched here on the course page.
1 lesson, 49 minutes
A single session, 49 minutes, published on YouTube by Intellipaat. Watched here on the course page.
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