Generally speaking, in a neural network, while the bottom and mid-level layers usually represent general features, the t

Sentiment Analysis in 10 Minutes with BERT and Hugging Face Learn the basics of the pre-trained NLP model, BERT, and build a sentiment classifier using the IMDB movie reviews…towardsdatascience.com

I am sure you have watched a video on how to write a function or a code. But, have you watched it whilst trying to understand the logic behind a code and typing on your keyboard? As Python and functions get more complicated, learning from videos, do not work. As a consequence, you will often have to watch over and over the same explanation, which is less effective and time-consuming. Basically, you will spend a lot of time pausing the video and trying to find the precise minute/second you want to start watching again. This is annoying.

- Monthly membership and a bit expensive: in this category, you can choose from three platforms (DataCamp, Codecademy or DataQuest). All of those platforms offer a Data Science with Python track.

For those with little or no background in programming, Python is the most accessible programming language. It is incredibly easy to learn due to its simplified syntax, which makes coding faster compared to other programming languages, such as Java. This is an advantage as Python allows those who are amateur programmers to read your code and collaborate with you. Therefore, Python can increase productivity and accelerate your career transition.

After dropping the top layers, we need to place our own layers so that we can get the output we want. For example, a model trained with English Wikipedia such as BERT can be customized by adding additional layers and further trained with the IMDB Reviews dataset to predict movie reviews sentiments.

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to double his money, he walks over, puts his cash down, confidently points to the card with the bent corner, and…loses. Dave can’t understand what went wrong, so he stays there and watches, and as he does, he realises that the guys that he’d seen winning were all stooges. They were in on the act, pretending the game was fair to draw in the suckers.

Python allows you to take data directly from the web, which is perfect for those who want to work with data analysis and generate insights or predict certain human behaviours. Data collection will be painless, which means you can build your own portfolio on GitHub at your own pace. More importantly, most of the data processing using machine learning and research around artificial intelligence is developed with Python language. This is because Python offers hundreds of libraries such as TensorFlow for neural networks and NumPy for working with arrays, matrices and high-level mathematical functions.

However, despite many experts online, maybe a few of them have been in a career transition to data science. Probably even fewer did such a change from a completely unrelated field in their late 30s. This suggests that what you have been watching/reading may not apply to your reality. That said, you should watch those videos with a pinch of salt. After all, you do not want to waste your valuable time. So, here are three things I wish someone in a similar career and life stage would have told me before making a career change to data science:

Apple, which has been ultra-consistent on its data and privacy strategy, is probably doing the right thing, but Facebook and others are doing right by their businesses (and shareholders) in raising the alarm. In the end, though, Apple’s App Tracking Transparency will launch and impact countless developers and millions of users. In the meantime, I expect Facebook and others to start figuring out how to knit a new data sharing sweater.

Because Data Science is a hot topic, multiple YouTubers are offering quick alternatives for beginners: ‘Learn Python fast and easy’ or ‘Pandas in 10 min.’ However, researchers say that the learning process is relatively slow, requires repetition and is most effective when distributed across spacing intervals [1][2]. Therefore, a well-structured curriculum is vital to building a solid foundation in a new skill, such as programming. Also, don’t forget that programming requires both cognitive (new syntax) and motor skills (typing). This means it is practically impossible for beginners to learn Python in a few hours, let alone in ten minutes from watching a video on their mobile phone.

You have reached a point in your career that it does not make sense to continue doing the same thing. Maybe you are bored, don’t earn as much as you deserve or, like me, simply never liked your job. Amidst a career turmoil, you came across data science and noticed there is a massive opportunity by switching careers. Also, you have found several coding tutorials on YouTube by Data Scientists.

Watching a video tutorial seems the preferred learning method of the 21st century. It is easy to find a video online; you only have to click on play and could even multitask. However, when learning data science and programming, watching videos is NOT the optimal learning format.

Here are the three pre-trained network series you can use for natural language processing tasks ranging from text classification, sentiment analysis, text generation, word embedding, machine translation, and so on:

If you have done your homework, then you know there are basically two programming languages optimal for a career in data science: R and Python. Although R is used among statisticians and researchers, and it can be used for Data Science, Python is by far your best choice.

My recommendation is to try DataQuest. Not only for the well-structure curriculum but also for something that might surprise you and is what brings me to the third and last thing I wish someone told me before.