In a real-world setting, you often only have a small dataset to work with. Models trained on a small number of observations tend to overfit and produce inaccurate results. Learn how to avoid overfitting and get accurate predictions even if the available data is scarce. Big data and data science are concepts often heard together. It is believed that nowadays there are large amounts of data and that data science can draw valuable insights from all these terabytes of information.
Data science is hot right now. If you want to learn more about it, where should you go? Online, of course! Check out our favorite data science sites. Whether you’re a beginner or a pro, these are the sites you should know. Not so long ago, if you wanted information on a topic like data science, you had to look for it—either at your local library or at a university.
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.
Data analyst is a relatively new position available at several companies. It’s also a high-salary specialization without a complex learning curve. Thus, many professionals are looking to make a career switch to this burgeoning field. In this article, I’ll explain what skills you need to become a junior data analyst. We’ll also review some tips for making this career change and see what an entry-level data analyst salary looks like.
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.
There I was—at Nvidia Deep learning & AI, the most prestigious deep learning event, waiting for my hands-on training to begin. It felt great to be there! But as I waited for things to start, observing the others who sat around me, I realized something: most of the attendants were men! In a crowded hall where around 200 people were waiting for a lecture, less than 10% were women. Where were the rest, and why was I one of the few female representatives who attended this conference?
Data Scienceand Big Data are the biggest industry buzzwords in 2018. Experts believe that artificial intelligence (AI) systems will continue to reign supreme in the technology marathon through 2019. “More than 40 percent of data science tasks will be automated by 2020.” –Gartner The data science trends for 2019 are essentially a continuation of some of the key trends of 2018, including areas such as: Machine learning (ML) Artificial intelligence (AI) Big data Edge computing Blockchain Digital twins Serverless computing Because data science is so vast and challenging to navigate, we’ve compiled a list of the most popular data science articles that dominated the industry over the past 12 months.
Unfortunately, data isn’t always available in the exact structure you prefer. And there’s nothing more frustrating than having inconsistent, untidy data that produces biased results. Let’s take a look at how the Tidyverse can help. What is Tidyverse? Before you can conduct any analyses or draw any conclusions, you often need to reorganize your data. The Tidyverse is a collection of R packages built around the basic concept that data in a table should have one observation per row, one variable per column, and only one value per cell.
Which influential data scientists are leading the charge? To help you keep up with the latest data science trends, we’ve compiled a list of the top data science and big data experts worth following. Putting Your Data to Work! Data science has long been a driving force in modern business, but even more so now with the wealth of data at our fingertips. Data is everywhere, thus, we need to learn how to make the most of it and get ahead in business.
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 our 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.
R and Python are two of the most popular data science languages, but which one is better? And will Python replace R in the near future? Let’s find out! R vs. Python: the Basics First, some history. R first appeared in 1990; it was derived from the language S, a statistical programming language developed for statisticians. It was (and still is) commonly used in educational settings and is a favorite among biostatisticians.
Programmers commonly have many questions about R, a popular programming language in data science and analysis. R is used all over the world by professionals in the fields of data science, data visualization, data mining, and statistical analysis. But what exactly is R? Where did it come from? And why is it being used specifically by data science professionals? This article attempts to answer all these questions, including the most important of them all: Should you be learning R as well?
Data science professionals frequently coordinate their workloads, host meetings to discuss and share ideas, and collaborate to solve problems. But all it takes for things to fall apart is a lack of clear communication. Data Science is a team sport that involves a variety of professionals working together to solve technological problems. However, you need good communication for your team to run like a well-oiled machine. You may be thinking that poor communication isn’t that big of a deal.
Will robots replace humans in the near future? As machine learning and artificial intelligence continue to grow in popularity, this question becomes all the more relevant. Which jobs will become extinct, and what will society looks like in the future? If you’re a data analyst whose worried about their job security, don’t worry—there’s still hope for you! In this article, we’ll take a look at the skills a data analyst can acquire to become a data scientist and rise above these pesky robots 🙂
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.