Statistics is at the heart of data science, and the link between the two fields keeps growing stronger. It's important to have a deep understanding of statistical concepts if you want to progress far in your career in data science, and that foundation can take a while to build. Springboard's Data Science Career Track is a great starting point, and it should be one of the first steps you take if you're serious about building your skills in this area. Let's take a look at the current state of statistics in data science, and what you can do to accelerate your learning. Some people like to say that machine learning is simply statistics with additional layers, and while that may be an exaggeration, there is still some truth to the statement. And that extends to the general field of data science.

It's no secret that STEM professions--shaped by years of gender and racial bias--lack diversity. Machine learning engineering and research is no exception. Women currently hold around 25% of all computer science-related jobs, and only 12% of machine learning roles, with factors such as a lack of pay and career advancement transparency and a lack of women role models contributing to those numbers. But leaders in the machine learning and AI industry have in recent years woken to the value that women bring to the workforce. It doesn't just look good for a company to have diversity--it's integral to the success of organizations that build machine learning algorithms and artificial intelligence.

The global pandemic has turned the entire world upside down with a huge impact on the Indian technology services industry. To visualize and springboard to the next level, there is a need to look at creating new business models using a'Digital First' mindset. A Digital First mindset challenges the conventional norms of doing business, and explores the unlimited possibilities of using a digital model to change the way we do business. Take the example of the travel and tourism industry, which is reeling from the effects of the pandemic in a big way. Can technology help in reviving this sector?

You might have heard of machine learning. The world seems to have been enveloped with hype around the topic, with everything from self-driving cars to human-like robots getting highlighted. As a Java developer, you might have felt a bit left out: most of the popular data science and machine learning frameworks are built for Python first. How can you get involved with machine learning as a Java developer? I work with Springboard, which offers the first machine learning career track with a job guarantee.

This International Women's Day, we have a treat for our Women viewers; A special career advice video that will pave the path for your career transitions into Data Science, AI, Machine Learning and Data Analytics. In this video, our star women mentors from Swiggy, G2 and AIFonic Labs explain how young technology professionals can make successful career transitions into Data Science, AI/ML and Data Analytics while explaining their own inspiring career transition journeys into emerging technologies. They also share what opportunities are available, especially for women, in AI, Machine Learning, Data Science and Analytics fields. Listen to them giving anecdote-led tips on how to crack the hot data job roles along with career advice for making the career transition of your dreams. Subscribe to our channel to get updates on the latest videos.

Microbiology and MBA grad JK started to learn about big data and machine learning in his job, but wanted to learn more about data science in a structured environment. He enrolled in Springboard's Machine Learning Career Track to learn about ML and AI online. JK tells us how he balanced his full-time job with the Springboard bootcamp (hint: he didn't sleep much), and how networking at conferences helped him land his new job as a Data Engineer at KPMG! What is your educational and career background? I didn't come from a computer science (CS) background. My undergrad was in microbiology, immunology and molecular genetics. I then completed an MBA with a concentration in Accounting and Finance, working at the Australian Chamber of Commerce in Korea. And that's where I got a taste of some CS database work.

We know tech giants like Amazon, Baidu, Facebook and Google have AI advantages like collecting enormous amounts of data, access to top talent, huge investments in research and development, over smaller companies. However, the possibilities offered by AI are not reserved only for the largest companies and biggest economies. Estonia is looking for ways how to attract international talent and investments; and on the other hand, its small size with limited resources requires the public administration and government to work efficiently. No wonder that in Estonia, both the government and companies have noticed the potential of AI technologies to solve these current demographic and economic challenges, as the impact of AI on GDP in the Nordics alone is expected to be considerable: 9.9% of GDP (1.8 trillion). There is a large spread of AI readiness in Europe, but even the most advanced countries are lagging the US in AI frontier.

Happening this year on March 7 in Tallinn, the North Star AI annual conference will bring together leading AI practitioners and companies around the world to talk about real-world applications of data science and machine learning. It also aims to stimulate AI adoption in Northern Europe and educate the population on how to harness the opportunities offered by AI while providing them with the necessary skills to partner with machines. We know that tech giants like Amazon, Baidu, Facebook, and Google have AI advantages like collecting enormous amounts of data, access to top talent, huge investments to R&D, over smaller companies. However, the possibilities offered by AI are not reserved only for the largest companies and biggest economies. Estonia is looking for ways on how to attract international talent and investments.

If you've been at machine learning long enough, you know that there is a "no free lunch" principle -- there's no one-size-fits-all algorithm that will help you solve every problem and tackle every dataset. I work for Springboard -- we've put a lot of research into machine learning training and resources. At Springboard, we offer the first online course with a machine learning job guarantee. What helps a lot when confronted with a new problem is to have a primer for what algorithm might be the best fit for certain situations. Here, we talk about different problems and data types and discuss what might be the most effective algorithm to try for each one, along with a resource that can help you implement that particular model.

I'm sure you've heard of the incredible artificial intelligence applications out there -- from programs that can beat the world's best Go players to self-driving cars. The problem is that most people get caught up on the AI hype, mixing technical discussions with philosophical ones. If you're looking to cut through the AI hype and work with practically implemented data models, train towards a data engineer or machine learning engineer position. Don't look for interesting AI applications within AI articles. Look for them in data engineering or machine learning tutorials.