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My Top 3 Data Science Projects for Beginners: This Guide will get you started!

    Data science is an exciting and evolving field. As our world becomes digitised, we have more data than ever before. Many data are waiting for analysis from your smartphone to the family photos.
    If you’re looking for some projects to get started with, here are 3 of the best data science projects for beginners.
    1.    Data Collection projects
    A data collection project is one of the best ways to learn about data science. You need to collect data before analysis, and this project will teach you how to do it. You’ll need a few different tools for this, but it’s worth the time investment.
    The first thing you will need is a web scraper. Web scrapers make it possible for you to mine information from websites without manually entering each piece of data individually. It is an excellent tool for beginners because completing a web scraper project will teach you to extract relevant data from any website they desire.
    In this project, you will use python and other packages such as beautifulsoup and selenium. You will have the opportunity to brush up on your Python skills. Check out the following web scraping projects ideas and choose one based on your interest.
    To understand the importance of web scraping, check this medium article; Automate day-to-day task tasks with web scraping.
    2.    Exploratory Data Analysis projects
    An exploratory data analysis (EDA) project involves analysing data to gather information. It’s a good first project because it teaches you how to make sense of large amounts of data and find new insights. It is one of the core skills of a Data Scientist.
    EDA helps you understand the state of your dataset. How many rows or columns? How many features and which of them are essential? You need to understand your data before you get meaningful insight from it. My favourite quote in this regard is, “You can’t build something on nothing.”
    Depending on the project, you can do EDA using Python or R. For example, this project used to explore the cars dataset from Kaggle. You can also check Exploratory Data Analysis in Python by DataCamp.

    3.    Data Visualisation projects
    “A picture speaks more than a thousand words.”
    A data visualisation project is an excellent place to start for beginners. Data visualisation tools make it easier to analyse data sets by turning them into interactive graphs, charts, maps, and more.
    Data visualisation projects can help you answer questions about your customers like “what are the most popular destinations for our product?” or “how do our customers react to different messaging?”.
    There are many tools to accomplish this task, but I recommend PowerBI or Tableau. These are the 2 most prominent vendors, and there is a high chance your future employer will use one of them. Check out these links to get started with PowerBI and Tableau.
    Bonus: Machine Learning projects
    IBM defines machine learning as a branch of artificial intelligence (AI) and computer science that focuses on using data and algorithms to imitate how humans learn, gradually improving its accuracy.
    Completing a machine learning project is essential, but it is advisable first to understand data collection, exploratory data analysis, and visualisation. Check this article to understand the steps necessary to complete a machine learning project in python.
    You can also review the following 180 data science and machine learning projects with python.

    Now that you have a good idea of what each project entails, it’s time to do some research. Complete at least one project in each area to develop an excellent portfolio. Do this by examining what you want to get out of your project, what skills you already have, and how much time you can dedicate to a project.
    Based on your answer, pick the project that sounds most appealing and exciting to you. All the best!
    Next week, I will explain why we all need to be data literate.
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