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Learn the answers to some of the most frequently asked questions about the data world.

Can You Get Paid for Scraping Data?
Learn with us what web scraping and Scrapy is and how you can use the same to your benefit.
What is an easy and sustainable approach to transition to Data Science?
If you are in undergrad and are contemplating a career in Data Science and Analytics, pick up tools and technologies that enjoy industry wide usage such as Tableau, PowerBI, R, Python and others that are commonly used. Learn from online courses and free YouTube resources such as freeCodeCamp. If you
If you had the chance to go back in time, what would you do differently about your data science journey?
Going back in time is a great way to understand what is it that one needs to do now to change course for the better. I love this trick that makes for great conversations and self-examination. One thing I will change if I can go back is to spend more
How to evaluate a Master’s degree in Data Science?
If you keep your notebook close, and your pencil even closer, and make notes and connect the dots, and you do this even for a few weeks (30-45 mins daily), at the end of a month you would have gained a whole lot of perspective and understanding.
How to know which domain of data science is suitable for you?
All of a sudden, a company will go from collecting very little data to now collecting data from their website (views, clicks), employee data, manufacturing (from machines, processes, employee activity) and any other aspect of their business that needs to be improved.
What is the “quick start” approach to learning Machine Learning?
Take up one topic from your notes and go research it by googling it, finding Wikipedia articles, blogs, more examples of its usage, read related discussions on StackOverflow and gain as much understanding of the concept as you can.
I am trained in statistics but worry what I can contribute? Should I focus on technical skills more? Please help.
The discussion shared below was part of many Q&A sessions Harsh Singhal conducted with Data teams at various companies and colleges.Analytics is statistics applied with code (code = SQL/Python/R/Java/etc.) E.g., when you identify the average or variance of an attribute in a dataset
How to build a data-driven mindset?
A mindset is a result of what you repeatedly do. A data-driven mindset means that you bring data to decision making, repeatedly. Bringing data to decisions implies a lot of things. It could mean developing engaging dashboards. Developing datasets to make it easy for other Analysts to run deep-dive…
How can a business analyst transition to ML? Especially when analysts get little or no exposure to ML in their job?
Participate in Kaggle.com and learn AI/ML on https://www.fast.ai/ (free). Do excellent Analytics work by day for your company and spend your evenings pursuing your AI/ML dream. A resource like www.fast.ai has made it very easy for folks to learn AI/ML by
Do I need to have basic knowledge in every tool out there for data science?
If you can create a list of every tool existing in data science, that list itself will be fascinating to share with others. After you have done the research to create such a list, add a few lines to summarise what each tool does and one or two pros and
Motivations for staying in a role for longer than a few years?
The discussion shared below was part of many Q&A sessions Harsh Singhal conducted with Data teams at various companies and colleges.People stay and leave for a variety of reasons. As long as you are convinced to stay or go, then your reasons are the right reasons. I
How to choose a path (Data Engineering, BI, Data Science, Product)?
Careers will span longer periods (decades and more) increasingly, so people have to be ready to make changes throughout their careers. Changes that are made in the pursuit of media-driven hype can be difficult to sustain as new hype cycles will quickly replace the previous ones. What is sustainable…
How to plan and execute the company’s ML project for better business impact?
An ML project, like any other project requires the project team to get together in the early stages to resolve lots of open questions to better scope the project. What is the problem being addressed and why is it seen as a problem that needs to be solved? Very often
How useful is a master’s degree in data science? Is it really worth it? Or can I learn the same things on the job?
First, identify what it is you want to learn. Can you come up with a list of topics and projects that you wish to work on? Then, you will have to research various degree programs and their curricula for overlap. Additionally, you should then look at different companies and the
How can I have a successful career in e-commerce analytics?
What does success mean to you? This is a broad question to an already broad question :-) Try to identify what interests you about e-commerce and aspects of e-commerce that Analytics can help improve? So many aspects are involved in making a sale online such as recommendations, product placements, a…
What are the essential skills required for each role in the Analytics domain? And how to pursue it?
If you have a job, use the industry you are in as a starting point to explore your aptitude and interest. You might like to write lots of SQL queries, extract insights, and make interesting presentations with spreadsheets and slides. This is as useful and important as someone writing data
How to clean up past projects ?Or call it “legacy” and just move on?
The first step is a detailed assessment. Is there a document that takes up different aspects of the previous project to identify pros and cons? Was there a requirements document that was used as a guide to developing the project that is now being called “legacy”? If not, then write
What can be done to enhance my career in the field of Data Science?
Everyone learns differently. But to know what works for you, choose a system that you found successful in school or college. Often we used to sit with a resource (usually a book) and read and take notes. Do the same now. The process is the same but the resources have changed.