Data Science Tips for Beginners!

  Non-Technical Skills These skills won’t require as much technical training or formal certification, but they’re foundational to the rigorous application of data science to business problems. Even the most technically skilled data scientist needs to have the following soft skills to thrive today.

Here is what we're finding mistakes in thousands of cvs: 1. Summary: CV should have a short summary of your experience, skills, achievements, so that a recruiter need not read the full cv. 3-4 Bullet points at the top of the CV can summarize it. 2. Length: CV should

It is so good to be interested about Data Science and Machine Learning. Many students think that Data Science is a non technical subject and people with zero coding knowledge, no logical thinking, no creativity can pursue data science courses and can become a Data Scientist. To all those students, looking for building a good portfolio for Data Science, here are some tips to follow: 1. Certifications and courses are not going to take you

Have you ever been confused between paying for a course on coursera, udemy for certificate or downloading it from internet for free. "One good Project is always superior than 5 certificates". With zero knowledge of C and a keen interest to learn it, I looked upon the internet for the courses offered by some websites which I found paid. In reality, many students

I have summarized in this text best tips for data scientists to progress along their career. Whether you’re just starting or you want to go from junior to mid or mid to senior, there’s something new here for you. It's good to know about regression, Clustering,classification ensemble models however theses algorithms are now being taken care by Auto ML packages. Now, the hiring organisations are

Solved a DS/Algo question & forget it after a week? Don't worry I have a SECRET tip for you. "update your `mind cache` with Fibonacci Sequence Interval, starting from index=2" Explanation: 0. Suppose you solved a problem on

These are the Mistakes that I generally did and learned from that. Undermining the Power of Statistics and Probability. Ignoring the Outliers and Missing Values or Using a general method to treat them. they have a strong pattern hidden inside them, so do not take them lightly. It is more Important to make our Data better instead of using fancy models. because if data is poor, the results will be

Good news to those who are struggling to get past interviews! Here is the compiled counter moves for four of the most common interview questions. 1. Can you tell me a bit more about yourself? 2. What kind of value can you bring to this role? 3. Where do you see yourself in 5 years? 4. Why do you want to leave your last role? →  Click Here   ← to Access the PDF file.

ML systems are increasingly used in day to day applications. Data scientists often spend a lot of time in designing the model but little on the model post-deployment. In contrast, the software development life cycle emphasizes on testing and production systems post-deployment. ML systems will flourish and can make more impact if they mimic the best practices of software engineering. Machine learning systems directly or indirectly can influence the customers that they are intended to serve. A mis-classification could result in a catastrophe for some random customer. Therefore, it is prudent for data scientists to meticulously emphasize on the testing and production. This article is an extension of the tweet that I posted. I would like to thank Practical AI podcast members and Tania Allard for sharing useful tips. Tips on testing and production — ask these

'Data Science' is a very attractive career path today. A huge lot of individuals start walking on this path and majority of them fall in the trap of these luring mistakes. I also belong to this majority. That is why I am writing this post so that you learn from my mistakes as it is rightly said “Intelligent is the one who learns from others’ mistakes.” Learning theory first and implementing later : Like many others we start our journey by taking a course and learn ‘Data Science’. Mine was no different. Majority of these courses will give you lots and lots of theory. Many courses have quizzes and assignments, but still the practical aspects lack in them because you don’t practically implement things along with the course. Theories are definitely important but they are of no use if you don’t implement and understand its actual use. Relying just on the

Great introduction to data science for total beginners "I wanna be a data scientist, but… how?". Where to start, and what to do step-by-step. Plus, the author gives a few recommendations for what classes to take and lists what skills are required to become a data scientist. Great visualizations too! I wish I had it a year ago. Instead, I had to go through hundreds of job postings to see what skills are required by the job market, and doing many-hours research on upvoted online courses. I found Kaggle and GitHub through my LinkedIn connections and learned them by myself (by the way, there are courses for everything). Good luck in your

I guess, we all make mistakes and even scientists do. Here is an article highlighting  3 most common mistakes for Data Scientists . As for me, I do tend to jump between libraries when I accidentally find something interesting. And I want to cover all at once. What about you? Where are yours?