How to Be Career Ready Getting Hold of Practical Data Science Experience? – Analytics Insight

Data Science

These projects can help you have a great data science career

To become a professional in the field of data science, books are just not sufficient. You also need to gain a lot of real-world data set experiences to face real-world problems. So, to help you with that aspect, we are here with the resources that can help you develop and acquire practical skills to excel in your data science career. After completing these projects, we are sure you will start acquiring command over SQL, Pandas, and machine learning modeling. Why late? Let’s dive into the article and know more about it.

1 SQL Skills

Are you aspiring to be a data science professional? Then you must have strong SQL skills. But what if you are new to SQL? We are here to guide. Firstly, start with some good tutorials, which can help you learn the basic, intermediate and advanced SQL techniques.

2 Pandas Practice Questions

When you start developing machine learning models, without a hand over Pandas skills it can be difficult for you. But the sad thing is that, unlike Python and SQL, we have only limited resources available on the internet that can help you to gain a grip over Panda’s skills. The important aspects that you must practice are to filter and sort data, group, and aggregate data, use. Apply () to manipulate data, and to merge datasets. These can give you a good level of confidence in using Pandas for data science projects.

3 Machine Learning Modelling

The best way to gain data science experience is by designing machine learning models of your own. The process includes finding a public dataset, defining a problem, and also solving the problem using machine learning. You can also go ahead and choose online resources and websites to choose a perfect dataset for your project.

Steps to launch your data science career

Step 1: Understand what you need to learn

Most people think that you need to have a master’s in order to become a data scientist. But this simply is not true at all. Try asking yourself a question, gather data that can answer questions for you, explore, analyze and visualize data, build and evaluate machine learning models and then communicate results.

Step 2: Get a grip over Python

Python and R, both are great choices to learn programming languages for a data science professional. But the only thing is that Python is more popular in the industry. You can also join any of the short courses that are available on the internet to better understand the concepts.

Step 3: Give time for data analysis, manipulation, and visualization with pandas

If you want to work with Python then it is equally important to learn the Panda’s library too. This can also boost your efficiency to great heights.

Step 4: Learn machine learning

Building machine learning models to predict the future or to extract insights from data is an interesting part of data science projects.

Step 5: Keep practicing

Practice is a great way for learning data science and gaining data science experience to a great extent. This can also help in retaining the concepts for a long time too.

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