That confidence is fundamental. In fact, I couldn’t do what I do without it. I write a lot of stories that, at first gla

The most crucial part is maintaining momentum and learning or practicing regularly. But the most difficult part is sitting down with your computer to start working. Once you successfully convince yourself to start for the day though, it does get easier every time. At the end of the day, if you don’t get those bursts of your “feel-good hormones” when you convert the little bits of data into tangible knowledge, is it really something you wanna do for the long term?

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icans dying every day of Covid, the president of the United States refusing to concede an election he lost, and the West Coast continuing to burn, there is no shortage of pressing issues to be outraged over. That’s why I find it so strange that conservatives have focused their energy and ire on perhaps the most innocuous event of the year: a male celebrity in a dress.

However, this is where your domain expertise will dictate most of what you do. You have mastered the tool that Data Science is but what you want to do with it and how you apply it in your own domain is why you will be hired. You may want to explore Natural Language Processing to analyze big genome data or carry out sentiment analysis for a chatbot to automate customer service for a company. You may want to learn the ins and outs of how Convolutional Neural Networks work to detect objects through computer vision. Or you may just want to analyze marketing and customer behavior data to help create better-informed strategies for brand growth or profit.

The important thing to remember is that you need to love how you get to your destination, the journey, the process, not just the destination itself. Play around with every new concept you learn. Tweak things here and there. Let your curiosity flow.

Pandas is a widely-used data analysis and manipulation library for Python. It provides numerous functions and methods that expedite the data analysis and preprocessing steps.

I went to a mid-level UC school but maybe could have gotten into Berkeley if I had tried harder. I’ll need you to meet me at about that same level. I’d love it if you taught me things about the world but if you ever respond to my lack of knowledge with a condescending “You don’t know…/ haven’t heard of…” I’ll push you into the rising sea levels of the Pacific Ocean.

I went to a mid-level UC school but maybe could have gotten into Berkeley if I had tried harder. I’ll need you to meet me at about that same level. I’d love it if you taught me things about the world but if you ever respond to my lack of knowledge with a condescending “You don’t know…/ haven’t heard of…” I’ll push you into the rising sea levels of the Pacific Ocean.

You will need to learn about each of the components of an ML model. Why do we need to worry about overfitting? What are hyperparameters and how they affect the model? What are optimizers and how to use them effectively? Why is regularization important? Why is one particular model better at solving a particular problem than another one? Is a complex neural network more effective and really necessary to use, rather than simpler models like regression, or classification, or clustering?

This is purely based on my own experience and gross generalization. In most cases though, it’s not really as linear and you will bounce between the different chapters, especially chapters 3 and 4. The experience will definitely be different for you in some way or the other. There are other methods to ace each of these stages which are as effective as these, if not more. Some of you may focus on data visualization, some may specialize in Machine Learning. But I guess there is an element of commonality between them; they all fall under the umbrella of Data Science.

We should be in the same bracket of hotness, but I’m above you (not height-wise — see expectation #2). Your friends should think “wow, how did you land such a beautiful girl?” while still recognizing that our attractiveness levels are fairly compatible. Make sense?

A lot about writing is hard. Starting a piece is hard. Ending a piece is hard. Doing research can be tough. Figuring out a structure for a story is nearly impossible. The one thing I’ve always found easy is finding the confidence that the story was worthwhile and that it was important to write it.

There are loads of ML algorithms but you don’t “gotta learn them all” (*insert Pokemon theme song in the background*). You should figure out which ML techniques are more useful to your domain knowledge. Again, YouTube can be a good starting point as it has been so far. But this time you will definitely need to rely more on other resources like module documentations, or niche blogs like on Medium. By now you should probably be a God of looking things up online.

The important thing to remember is that you need to love how you get to your destination, the journey, the process, not just the destination itself. Play around with every new concept you learn. Tweak things here and there. Let your curiosity flow.

You are proficient in Python, R, and SQL. You have developed intuition to analyze almost any data. And you know how to apply ML models. Now is the time to take your skills to the next level. The last most crucial thing you will need to learn is the art of putting together the data pipeline, integrating with cloud services like AWS, Azure, IBM Cloud, Hadoop, Spark, to name a few, and pushing it into production. Again, there are loads of resources online. You just have to look them up.

Due to its popularity, there are lots of articles and tutorials about Pandas. This one will be one of them but heavily focusing on the practical side. I will do examples on a customer churn dataset that is available on Kaggle.