Challenges to grab while landing on Data Analytics👩‍🎓

Challenges to grab while landing on Data Analytics👩‍🎓

Discussion about Kaggle Platform

A little introduction to Data Analytics

Before jumping on the challenges to grab while learning Data Analysis, let's get started with what is Data Analysis?🖥️

Data Analytics in simple words can be defined as- converting raw data into actionable insights. These insights can be performed with various tools such as Tableau software for visualization, SQL for storing the databases & python programming for regressive analysis.

Let's start our new journey with an open-source platform!🔥

Many freshers after completion of the Data Analytic course, start applying for jobs/ Internships but I highly recommend not to directly apply for the internships just because you have a certificate because many recruiters look for projects worked on good datasets.

Before applying to any other internships, first hop on Kaggle- which is an open-source platform for Data Science.

How to get started with Kaggle?- After creating your account, start your analysis with the first Kaggle Competition i.e. Titanic- Machine Learning disaster. Don't worry, if you are a beginner in Data Analytics and have a brief idea about Machine Learning- this is the best platform to escalate your Machine Learning concepts from basic to advance. Try to participate in different Kaggle competitions or choose any good dataset for your analysis to determine your Data Analysis grip.

If you are finding difficulties while analyzing the datasets, don't worry you can refer to others' code- go through it, know the concept behind and then execute it for the analysis. Also, you can use Tableau Desktop for enhancing your visualization skills and you can go for SQL challenges which I'll discuss in my upcoming blog.

Here are a few datasets where you can practice on your Jupyter notebook or Kaggle itself💡

Conclusion

It doesn't matter whether you are a beginner or an expert in Data Analysis, you can always improve yourself by knowing your own mistakes and coming up with great concepts which might lead to new discoveries, so All the best folks!!