The Secrets of Completing Highly Creditable Hand-on Project in 2021| A Data Science Job Seeker Guide by Learnbay

Enrich your Candidature Credibility with your own Hands-on Project in 7 simple Steps- Know about secret places storing useful datasets.

Online data science courses are now easily available. So it’s not a hard job to find a suitable course and get it completed. Yet, so many data science aspirants got stuck in the midway of their career transitions. Where? At the stage of completing a creditable hands-on Project.

Yes, without a valid real-time data science project, your CV can’t hold any place other than the trash box of recruiters.

Are you also stuck at the same stage? If yes, then you are on the right page. This blog will help you reveal the top secrets of completing a highly commendable project on your own; and, most importantly, at the lowest possible time.

7 Steps to structure a Worthy Project Plan

• Topic Research and Identification of best-fit topic

Whatever actions and incidents we experience around us, everything can be analyzed with data science. So, there are no limitations regarding the project topics. But, when it comes to the goal of completing an adequately laudable project, then you have to keep few measures in your mind.

First, choose such a project domain in which you feel real interest. In case you are a working professional and want to stick to your domain, then only choose a project that is relevant and on-demand in your domain.

Second, try to think of simple project ideas in an innovative way. For example, suppose you are a graphic designer. In that case, analysis of the change in design requirement by daily market trend and offering of customized design recommendation will be a great idea. But until this stage, nothing seems unique. What if users get auto-generated design recommendations according to their mood.

Well, this may seem a bit complex at your stage, but it’s possible. What I am trying to say is never be afraid to hit the chances of innovation while searching for a topic idea.

The most important thing is whatever topic you choose, you need to stay adequately motivated by that throughout the project.

Although online topic research has become the most popular method of topic research nowadays, the secret of success lies in researching through direct interactions. Are you getting confused?

Well, while you can reveal plenty of topic lists just by searching in google, to retain the uniqueness of your idea, the best way is that you interact with your targeted domain people.

Suppose you want to do a project on COVID-19 home care perspective analysis. Then talk to local COVID healthcare centers providing such services, healthcare workers, doctors, Covid survivors who recovered in-home care, and other relevant personnel.

Then run the following questions in your mind.

• What problem did they face?
• What were their expectations?
• What are the root-cause for those problems?
• What solutions are available to that problem?
• Why were the solutions not applied?
• How can that solution be applied with zero or minimal possible drawbacks?

As much as you question yourself, more valuable will be your identified project topic.

• Collect and explore maximum possible datasets

Once you are with your project topic, start collecting data from every possible resource.

As you are doing the project on your own, you have to be dependent on several open data sources, like Kaggle, etc. Lots of such options are available across the internet but be cautious about choosing the right ones. In the second part of this blog, I will share the top 3 open data sources from where you can extract extremely important datasets for your project.

Besides the open-source data centers, you should try to build connectivity with some private database relevant to your project. But yes, with authenticated ways only. In case you are a working professional, you can ask for help from your company’s data handling team. If you are registered with any institutional data science and ML program, you can ask for their help too.

The third option for your data search becomes the use of APIs. Lots of free public APIs lists are available on the internet. You can choose from those according to your project needs. A few of the popular free APIs are Genderiz.io (for gender-dependent data), 7Timer! (for weather forecasting data), etc. You can search for the public list of Indian free APIhere. Other than these, you can use Google API, Amazon API. Try to find out which API your organization or your known data science professionals are using. For a creditable project investing a few bucks on the personal user versions of this API seems worthy.

• Lookup for relevance and wipe out all the irrelevance

Now you have an ample amount of dataset in your hands. It’s the time to explore those datasets for the generation of important insights in the next steps.

To explore data, start merging, separating, resembling data from your collected datasets. Make a set of relevant data. Try to find out the links between different types of data within your collected dataset that comply with the relevance level of your targeted project topic.

Even if you got lots of data from internet sources, still don’t skip interactive data collections to cross-check the updation degree and present-day reliability of those datasets.

Yes, ‘present-day reliability’- While collecting data from internet sources, be cautious enough about the age of data. Lots of sources of free public APIs lack real-time data updation. If you lose your way inside the old data, then it’s not going to be a good sign for your presence in the data science project as well as your upcoming data science career transition.

Once you complete interlining all the data, identify all of the relevant dataset, then wipe out all the unnecessary data like irrelevant, redundant, duplicate, and corrupted data.

Amongst all the seven steps, this is the most stressful and boring step for a data scientist. Moreover, this is the most time consuming task throughout the entire project schedule.

• Reassemble, Resemble, and swapping of collected data

At this stage, you need to wake up your inner Sherlock Holmes. You are now with only the important data, so play with them in the maximum possible way to draw deeper insights. Apply your logical thinking ability and find out the maximum possible unique and relevant insights.

By the way, although you have already done the data clearing, don’t stay detached from what is happening outside. Stay updated on a regular basis and try to include all the latest data.

• Jot down every possible insight through visual outputs

Yes, you predicted right. I am talking about the most crucial part of data analysis- ‘Data Visualization.’ No matter how much detailed explanation you have generated for your analytical conclusion. At the end of the project, what matters only is how your visual presentation covers every aspect of your project insights.

Here other than conventional knowledge, you need to apply more ticks and tips for graphic insights. Choose the graphs with maximum possible customization options. Customize your graphs in such a way that they can provide the solutions for each and every possible query.

Apply the tips and tricks of Tableau or Matplotlib tools.

Yes, at this point, you need a bit of technical knowledge, at least ample knowledge about the above-mentioned data visualization tools.

• It’s time to play with the Machine Learning algorithm

This is the most challenging as well as the most interesting part of a data science project. You already have an insightful graph in your hand.

Analyze the graphs and try to ask the most critical queries that might come across your problem statements. Now analyze, if your graph can solve those queries. Not down all the queries for which your graphical analysis fails to provide a solution.

At this point, your data modeling journey starts. Work deep on several available unsupervised machine learning algorithms and land in with such a model that can cover up the queries and upcoming trends that are not distinguishable from the graph.

Give more effort to data splicing by which you will get two separate sets of data. One has to be for training your machine learning model and the rest for efficacy testing.

• Recheck, reanalyze, reinforce

Data modeling is not the end of your project. Your project has no meaning until it achieves adequate measures for organizational deployment.

Yes, the key target of your project is to enrich it with the most accurate level of predictions. So, the data model should have a high level of user acceptance possibilities.

And what is more important is that it should offer ample scopes of further modification. Keep an eye on this project as well as market trends and continue reinforcing your model.

Where to search for a relevant dataset for your Data Science Project?

As already told, the collection of data becomes a very critical task in terms of reliability. So, you need to choose the online source very cautiously. Here, I have come up with the top 3 online data sources that will help you to collect ample amounts of data for your next project.

Kaggle

The words “data scientist” and “Kaggle” are synonymous in the science community. Kaggle is great for people who want data before pre-processing. It is a perfect dataset finding tool as it hosts many data science and machine learning tasks for real-world problems. You will find datasets of varying sizes upto 2TB. We can get around 18,000 datasets in the Kaggle database. It is as simple as choosing the appropriate filter to locate a required dataset; the result can be previewed and bookmarked.

Google Cloud Public Dataset

Have you ever heard of Big Quarry?

Using Google Cloud’s ‘Big Query’ finding and analyzing big datasets is now super easy. But It is necessary to sign up for a Google Cloud account to access all of the datasets. Once you have done so, you will be instructed to create a project. Only then can you access all the datasets. While you get data storage of your organization would be billed for as well as usage, and while you may want to use it for research, everything happens on your terms. To make the first 1TB of requests every month, you need not pay anything. If you’re patient, to do collecting your data sets within the free 1 TB query quota, then you can use Google Cloud Public Dataset for free.

Knoema

Until 2018, Knoema was the first choice for data scientists and data engineers. However, due to the fast expansion of the volume and reliability of available datasets, Kaggle won the first position in terms of popularity. But still, Knoema can be considered as the one-stop solution to data science project topics as well as dataset researchers.

It also includes 12000+ resources like Facebook, WHO, Amazon, Google, etc. This is the best place for digging out efficient time-series data from 1000 plus domains.

Yet, the only drawback is you need to pay for a premium account to access the datasets in full. But you can always sign up for free to access basic datasets.

So, you are well aware of the key steps of achieving success for your very own data science project, as well as the sources where you need to look up.

Then, What to do next?

Self-paced learning is always appreciable, but the hard truth is it will take much time. So in case you are ready to gear up your data science career and the projects, you can join our Data Science and Machine Learning program.

At Learnbay, you will get end-to-end guidance for learning the tricks and tips of completing a successful data science project. In addition, as we have collaboration with IBM so, you can avail the access to requisite paid APIs and data sources for free. Moreover, you will avail yourself of the chances of doing projects on big MNCs data like Amazon, Netflix, Walmart, Uber, etc.