What is the secret behind the writers who write every day and still produce great content?
Every writer has a unique way of finding a topic based on their personal taste. While every writer tries to write about something they care about getting attention from readers is essential.
Being a Data Analyst I got curious to see the possibilities of solving this problem with the skills I have. I wanted to know how does any successful writer search for topics. Especially when he/she is out of ideas what is their approach to finding topics.
9x Top writer in Medium Esat…
I will be straight with you. It’s absolute torture if you are trying to find a job right now, as Data Scientist is the sexiest job of the 21st-century¹ due to enormous competition, which is growing day by day.
The reality is that unless you stand out and brag about yourself you are not going to make it happen. Hey, if you don’t do it for yourself no one else will.
This is what I told myself when I was hunting for a job and not getting anywhere by simply applying with CV and sharing LinkedIn and GitHub profiles.
A typical Data scientist's goal is to provide solutions for real-world problems using analytics and machine learning but it doesn’t have to be like that always. You can be artistic as well.
If you are spending every breath of your data science life to solve business problems it will be exhaustive after a certain point and there is a danger of losing the love towards data. If you are sick and tired of the exhaustive Data Science projects it’s time for you to do something fun to fill the fuel in your mind and keep the spark running.
Deepnote — a Python notebook with real-time collaboration in the browser.
Have you ever used Jupyter Notebook or Google Colab for your data science projects? I know this is a stupid question to ask a data science candidate.
Jupyter notebooks are famous and used daily by most if not all of Data Analysts and Data Scientists.
Jet brains have done a survey in 2018 and see what they found —
Web developers have slightly different editor preferences from data scientists. …
The most contributed Data Science researchers this year so far
Throughout the centuries there were men who took first steps down new roads armed with nothing but their own vision.- Ayn Rand
This post is a shoutout to all the researchers who are trying to carry forward the legacy of Artificial Intelligence(AI). They opened the doors that were never knocked and allowed us to see new paths in AI.
Even if you are highly experienced, well established and famous in your field you should not let the knowledge go stale and let that spark in your mind go dark. …
Aren’t you tired of people telling you that if you want to be a Data Scientist you need to be really good at Math? And then you see somebody you know who is not as good as you in Math/Statistics brag about his Machine Learning skills in Linkedin.
You can learn all the beginner projects that are out there on Machine Learning but when you want to do something on your own you are not able to implement it just because there is no Training Data or You just don’t have a GPU with you? …
Rate your CV before submitting using this simple Python program
Are you one of the victims of polishing your CV for the nth time and applying for your dream jobs but ending up with no response from the recruiters? I can understand how frustrating it can be. But the simple step of learning the way recruiters look at your CV to shortlist and addressing the problem can turn the odds in your favour.
In case you are wondering why you are ending up with no callback from recruiters although you have all the matching skills and experience and after all…
This story is the continuation of Part-1 where we went through Introduction, Dataset building and analysis of medium articles data.
Machine learning (ML) made an outstanding impact on the culture of using machines to its great potential. Though the initial stages of machine learning are confined to predictions using structured data the advancements in applications of ML on unstructured data made it more interesting as now it can efficiently deal with images, videos, sound e.t.c
Close the gap between academic/kaggle Data Scientist and real world Data Scientist.
I was talking to my friend who recently joined in a big tech company as a Data Scientist and it didn’t take much time for her to realize how different the approach is to build Machine Learning models and put them into production when compared to online tutorials. Well, the first and most hard hitting fact she realised was how data is acquired and processed to train a model in business environment.
If any experienced Data Scientists out there, this is not at all a surprise for you.