Data analysis will help you start your career promisingly, but every future employer must be aware of your data analysis projects. An aspiring data analyst must work in various domains and learn to translate into the next Big Data analyst project concept!

Organizations are currently searching for data analysts who know about a specific sector’s challenges and find all projects in their resume. Projects can be challenging, especially when you are new to your data analytics portfolio. The main thing is to show your abilities with an interesting dataset. It is good news that data is everywhere — you have to know how and when to find it.

Are you a fresher? Projects can be very familiar and easy to operate at these stages. Such projects do not need powerful application techniques for anyone starting in data analysis. Instead, you can move ahead quickly by using simple algorithms.

Data Analytics portfolio

You must demonstrate the possibility of conducting various types of data analysis in your portfolio. However, it must also show that you can gather, concise, and visually report your findings. With better skills, the portfolio is becoming more complex.

Types of Data Analytics project ideas

Six project ideas are presented here that will help you build your portfolio from scratch with three main areas: data scraping, exploratory analysis, and data visualization.

1. Data/web scraping project ideas
2. Exploratory data analysis project ideas
3. Data visualization project ideas

A) Data/web scraping project ideas: 

There are various names for Web scraping, such as Web harvesting, screen scraping, and others. It is a process by which vast amounts of data are collected from websites and stored at a specific location. The web scraping process can be automated using software such as Parsehub, ScraperAPI, or Octoparse or with libraries such as Beautiful Soup or Scrapy. Regardless of the method you use, it is vital to demonstrate how it works efficiently.

1. Job portals: Many new entrants like to scrap data from the job portal because they often contain standard data forms. Many online videos can also be found to illustrate how to operate. Why not concentrate on your field and keep it interesting? Collect job titles, firms, salaries, places, and the skills needed. It provides tremendous potential for later visualization.
2. Subreddit: Reddit is the world’s most popular social media sites. It has communities that you can imagine as subreddits for almost every topic. The prosperous communities of Reddit are an excellent location for checking your web scraping abilities. You can scrap your subreddits and find out what your users are thinking about them. For a specific subject/work, you can discard this subreddit. 

B) Exploratory data analysis (EDA) project ideas

Data analysis begins with EDA. The exploratory data analysis is vital in the data analysis process, as this step allows you to understand your data and includes the visualization of your data for better exploration.

1. Suicide clusters

The global suicide rates dataset covers suicide rates in many different countries, including the year, age, gender, population, and GDP. Ask yourself: What patterns can you see when performing your EDA? In different countries, do suicide rates go up or fall? What variables do you find (for example, gender or age) with suicide rates?

    2.World Happiness Report

This study demonstrates the world’s status in terms of happiness level calculated by the index of happiness. We will analyze some of these variables using various data analysis techniques for this project. Let’s first examine the countries’ regional happiness ratings. Ultimately, it can catch your attention whatever dataset you are using. You will probably run out of steam before you get too far if the data is too abstract or of no interest to you. Keep in mind what more research you should do to detect and learn exciting trends or patterns.

C) Data visualization project ideas

Data visualization is a core element of the entire workflow of data science. It shows data following data analysis, including chart design, dynamic blend, two-dimensional charts, 3-dimensional flow charts, linkage, and large-screen monitor.

1. Word Clouds: Word clouds, particularly in natural language processing work, are surprisingly instructive. A word cloud is a series of words of various sizes. These are perfect ways to delete text info sections, from blog posts to databases known as tag clouds or text clouds. They can also help users compare and check the wording similarity between the two separate text pieces.
2. Covid-19: On every portfolio, the concept looks great, and the pandemic is nothing but appropriate! Also, thousands of data sets of Covid-19 are already available on platforms like Kaggle. How do you show the data? Can you use a global heat map to display where there are very few cases? You may be able to create two bar charts overlapping to reveal known infections against expected infections.

Data Analytics – Career prospects

Professional data analysts are among the world’s most famous professionals. Data analysts are in charge of massive salaries and excellent returns even at entry-level, because demand is so high and supplies of people who can do this very well. Data analyst jobs cover a range of businesses and sectors. Any company using data includes analysis by data analysts.

How to get started a career in Data Analytics?

The Post Graduate Program in Data Analytics certification will offer a wide range of critical technologies and skills, including statistics, Python, R, Tableau, SQL, and Power BI, now performed in data analytics and data science. You should be able to think statistically and use today’s emerging software, and you should be able to get a degree in professional acumen after a Master’s degree in analytics. Choosing the right schedule will help you improve your career and prepare you for a future with increasing opportunities.

Verdict

Working on new, creative project concepts for data analytics represents the perfect way to show your expertise. It is only possible if you had experience in the field and faced different industry-specialized challenges. It’s the right way to remain optimistic and create projects!