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Data Science Projects That Will help You to get The Job after Training ONLEI Technologies

 

Data Science Projects That Will help You to get The Job after Training ONLEI Technologies 

The best way to get a job in data science is to showcase your skills with a portfolio of data analytics projects. Data analytics projects not only help you in getting your first job but also help you to gain more exposure to data science. Some helpful projects will upskill you as well as make your resume more impressive. 

Getting a job as a data scientist is easy and you need to be very persistent to succeed in this field. One does not become a data scientist overnight. It takes a lot of learning, experience, and understanding of the concepts, especially if you want to start a career in data science as a fresher

It is easier to get into Data Science if you are from a Statistics background because Statistics is an integral part of various Data Science techniques such as Machine Learning, Deep Learning, etc

Data Analytics Project Ideas that You Need to Stay Away From

One piece of advice before we start talking about the components of a good project – There are two things you need to stay away from when you are trying to find or build a data analytics project.

We suggest not to include common projects in your resume or portfolio. You need to stay away from the most common data analytics project ideas.

A quick break down of the components of a good data analytics project:

  1. Working with real data

  2. Working with modern technologies

    1. APIs

    2. Databases in the cloud

  3. Building models

  4. Making an impact / validation

    1. Application frameworks


API

How are you going to get that real-life data that is updated in real-time?

You can use APIs to collect that data. Almost all apps and platforms these days rely on APIs to collect and pass information. Learning how to use APIs to get the data that you need for your analysis shows the interviewer that you have relevant skills to do the job.

Some popular examples of APIs are Twitter, Netflix, and Amazon. A good API for data analysis will include:

  • Real-time updates

  • Date and timestamps for each record

  • Geolocations

  • Numbers and text for data

Databases on the cloud

Databases in the cloud’ is the second modern technology. Once you collect the data from the API and maybe after you clean the data, you probably want to store it in a database. Why?

  • Because as it’s mentioned before, the data you pulled from the API is updated continuously, so if you pull the data again, you’ll get new records. So instead of pulling the entire dataset and cleaning all of it again, it’s nice to just pull and clean only the new records and store the old ones in a database when they’re safe.

  • Every company uses databases and many use cloud services like AWS and Google Cloud. Knowing how to build a data pipeline with a cloud provider is a real skillset to have and will definitely set you apart from others. If you have this experience, your interviewer would be very impressed because the interviewer knows that you can hit the ground running from day 1 on the job.




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