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In late September 2021, David was a Research Analyst with Texas A&M University. In March of 2022, less than 6-months later, he has accepted a position with Microsoft as a Machine Learning Support Engineer. In one of my webinars, David explained that he had just increased his salary by $50,000. I told him that's just the beginning. How was David able to get a job at an elite company like Microsoft?

Data wrangling is an essential job function for data engineering, data science, or machine learning roles. As knowledgable coders, many of these professionals can rely on their programming skills and help from libraries like Pandas to wrangle data. However, it can often be optimal to manipulate data directly at the source with SQL scrips. Here are some data wrangling techniques every data expert should know. Note for the purpose of this blog post I will not go into the details of the many versions of SQL.

Data science is a booming industry. If you've got the skills and the interest, you've got the opportunity to bring home the big bucks. Data scientists are well paid, even compared with others in the tech community. Because the data science field is constantly growing and changing, data science is widely ranked as the skill most in-demand across the tech sector. Data science is a great industry to get into if you're looking to make a lot of money.

Artificial Intelligence and Data science are cutting-edge technologies that are ruling a wide range of sectors and companies today. AI and data science skills are crucial in this age of AI, big data, and automation. Businesses are looking for skilled professionals who can manage the ever-increasing amount of data generated by their operations. There are various skills for CEOs that can help to make the most of their business data. Analyzing and gleaning insights from data requires a different skill set than simply storing and managing it.

We would all love it if we could say we knew everything. Unfortunately, that is not possible. Sometimes I tell myself "I don't know anything", to push myself to learn and continuously improve. Data Science is one of those sectors which require constant learning and there is always room for improvement. It's difficult to keep on top of things and feel a sense of accomplishment in the world of Data Science.

It started with a curiosity of doing something different and fulfilling. I was hearing a lot about data science but never understood the full meaning so one day my friend sent me an article about GPT3 and that was it. I was determined to learn this magical world of data science, so I decided to take the Data Scientist with Python Track -- DataCamp Learn. The learning path taught me about Python programming and statistical thinking. To complete the data science track, we had to solve Case Study: School Budgeting with Machine Learning and participate in a DrivenData competition.

Kaggle is a website where you can find competitions to solve data science problems. It's free to join and it gives you the opportunity to practice your skills on real-world datasets in various industries. This post will introduce 10 datasets that are great for practicing your skills before heading into an interview or just because they're interesting! The Titanic dataset is probably one of the most popular datasets on Kaggle. It's a great dataset to start with because it has a lot of Variables (13) and Records (over 1500).

Data science is a cutting-edge technology that is being adopted by a wide range of sectors and enterprises. Companies are seeking data scientists who can manage the ever-increasing amount of data generated by their operations. Data scientists assist companies in making the most of their business data. In the business, there is a scarcity of experienced data scientists who are up to date on the newest techniques and technology. A reputable online programme may be used to learn data science from the ground up.

With classic rock tunes playing, Adam Selipsky took the stage for his first re:Invent keynote as the new CEO of AWS, the $60-billion cloud juggernaut that was founded just 15 years ago. This was the first re:Invent without Andy Jassy, the charismatic leader who creatively used rock lyrics to tell product stories at past shows. The rock was still there, and product announcements abounded, but it's clear Selipsky is charting his own path forward as the head of AWS. Selipsky was one of Jassy's top lieutenants before he left five years ago to head up another Seattle, Washington-area mainstay in the big data world, Tableau Software, so he's very familiar with the company he's now charged with growing. From his perspective, the growth of cloud computing over the years has been astounding.