What is even more amazing is how small a team they had, handling such large amounts of revenue. At a dozen employees, th

If you are new to data science then begin with the dataset explorations. Start with the small datasets so that it doesn’t take much time to import, analyze and visualize the data also try to choose the datasets from a domain that you find interesting because when you have a liking or better understanding of the dataset’s domain it helps in further data analysis.

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ompany where I worked, where I totally lost it, and then gradually found what mattered again. Recently, the company sold for a vertiable shitload of money (add a bunch of zeros to what you’re thinking). They made an example out of me, in stripping me of the phantom equity for going to a competitor (as far as I know, I’m the only one they did that to). For asking for what I needed. I will admit that I did some math this morning that made me sick to my stomach.

Like alcohol, however, it is important to limit your consumption of social media, even if it’s based on facts. Forty minutes per day broken into four chunks feels sufficient. I would also advise you to keep feeding yourself with good quality brain food to counteract these negative inputs.

It’s actually interesting to analyze this because it’s to do with multiple reasons. One of the most important features of the game is its open-world nature and crafting mechanisms. Because of this flexibility, the game is enjoyed by a wider target audience, from kids to adults.

Try exploring different kinds of data, slowly move out of your comfort zone and get familiarize with datasets from domains which you haven’t worked. You can even submit your analysis and see how the community reacts to it.

Challenges can be used as fuel. You don’t just want fuel — you need it for growth. Resilient people know this. Instead of merely coping with what life throws at them, they lean into adversity and use it for growth.

We could simply avoid anxiety-inducing platforms, but it’s not that easy — we need to stay informed. That’s why we need to get our information from reliable sources. I recommend the WHO and CDC who provide excellent up-to-date information.

Check the description of the datasets, here usually details about how the data were collected and the time period to which the data belong and other details would be provided, this would help in framing your questions for the exploratory data analysis.

Start exploring the dataset and captures the insights. For further ideas on analysis, check out the “Tasks” tab, this is a recently launched feature where people can add interesting things that can be done using the data and others can submit their solutions to it.

If you want to practice this habit, you need to focus on progress when life throws you a curveball. It doesn’t have to be a major setback — opportunities for growth are everywhere.

It’s time to learn data exploration from the best people. For the datasets which you have been working on, go to the Notebooks tab and look for the analysis code snippets with a high number of upvotes and those that come from highly qualified users. Explore the analysis that is being done and try to compare it with what you have done. Identify the gaps or the analysis that you have missed, this retrospective analysis will ensure that the learning will come a long way.

It is important to spend some quality time in these steps because your data analysis quality will have a direct impact on the quality of the model/solution you are building, so ensure you spend enough time to explore and learn from the experts on data analysis

Similarly, check the other datasets and the notebooks with the analysis scripts and understand the kinds of analysis that have been done by some of the experienced data scientists.

Tedros Adhanom Ghebreyesus, director-general of the World Health Organisation (WHO), pulls no punches on this topic: “We’re not just fighting an epidemic; we’re fighting an infodemic. Fake news spreads faster and more easily than this virus, and is just as dangerous.”

Now having learned from some of the experts it’s time to put them into use. Pick a new dataset and start analyzing the data and I am sure your analysis will be much better now and try to incorporate the other standards like documenting the scripts and formatting to ensure they are easy to read and understand.