Python Data Science Project Ideas

by Kateryna Koidan Wondering what your first-ever data science project or your first big project in Python should be? Or are you looking for your next data science project? This article will give you some ideas and directions. Creating data science projects in Python is essential for your career development. It’s the best way to learn new data science tools, practice the skills you’ve acquired, and demonstrate your competencies to potential employers.

Who Are Data Scientists and What Do They Use Python For?

by Kateryna Koidan Are you wondering if a data science career is a good fit for you? In this article, I will try to explain what data science is and who data science specialists are. Check out what skills you need to become one of them – including Python. I have been observing a huge interest in data science for some time. Online forums and social media are constantly inundated with all kinds of information and questions on this topic.

What Are the Advantages of Using Python for Data Science?

by Soner Yıldırım Which language should you pick to start your data science journey? Python, of course! In this article, you will learn about the advantages of using Python for data science. Python was first released in 1991, but it has gained popularity in recent years. Data science is the most influential factor in the rise of Python. And this relationship between Python and data science has been mutually beneficial. Python eases and expedites the process of learning data science.

Top 15 Python Libraries for Data Science

by Kateryna Koidan We look at basic and advanced Python libraries for data science. Learn about getting, processing, modeling, and visualizing data in Python. The Python ecosystem offers a wide range of tools for data scientists. For newbies, it might be challenging to distinguish between fundamental data science tools and the ‘nice-to-haves’. In this article, I’ll guide you through the most popular Python libraries for data science. Python Libraries for Getting Data Data science starts with data.

Excel Alternative: What to Learn as a Data Analyst

by Usman Malik Excel spreadsheets are quickly becoming obsolete thanks to the emergence of the latest data analytics tools and languages such as Python, Java, R, and Microsoft HDInsight. However, a large number of companies still use digital spreadsheets, creating a lot of problems for modern business data analysts. Analyzing data through excel is a poor choice because of reasons like errors in data validation, a poor shared workbook feature, no multi-user editing, inaccurate data, and safety concerns, making it necessary for you to switch to better and advanced alternatives.

12 Python Tips and Tricks That Every Data Scientist Should Know

by Kateryna Koidan You already have some foundational knowledge of Python for data science. But do you write your code efficiently? Check out these tips and tricks to supercharge your Python skills. How to Write Efficient Python Code In this article, we'll take a look at some tricks that will help you write fast and efficient Python code. I'll start with how to optimize code that involves the pandas library. If you want to refresh your knowledge of pandas, check out our Introduction to Python for Data Science course.

New Course Launch: Intro to Python for Data Science (no IT background required)

by LearnPython.com Team Over the past three months, we've been working on something completely new. Please welcome our new course on Python data analysis! We got many emails from users like you with good feedback on Vertabelo Academy's Introduction to R course. So first, I want to start off with a big thank you—reading your wonderful comments was like a burst of energy! We're always looking to improve our offerings, and we greatly value your input.

Agile Data Science: Improving Your Workflow with Scrum

by Anuj Kumar Verma Within organizations, Scrum promotes efficient time and process management along with better team building and leadership. In order to implement Scrum, you'll need to follow a few simple rules. Introducing Scrum Today, we have the power to collect precise data both quickly and in vast quantities. In fact, 90% of the data available today was collected in the last two years alone. The rise of big data has greatly increased demand for data scientists, but the profession is one where few candidates possess the right skills.