Data Science is a rapidly growing field, and it is easy to get lost in the plethora of information available. If you are a beginner in Data Science, the learning process can be overwhelming. In this post, we will provide you with a step-by-step guide to learn data science effectively. Python is one of the most widely used programming languages in the Data Science industry. Its popularity is due to its simplicity and flexibility. Learning Python is essential for a career in Data Science.

Predictive Maintenance is one of the big revolutions happening across all major industries right now. Instead of changing parts regularly or even only after they failed it uses Machine Learning methods to predict when a part is going to fail. If you want to get an introduction to this fascinating developing area, read on! Predictive maintenance techniques are designed to help determine the condition of in-service equipment in order to estimate when maintenance should be performed. This approach promises cost savings over routine or time-based preventive maintenance, because tasks are performed only when warranted.

As the use of data becomes more popular, it results in high demand for Data Scientists. Every day there are new companies offering bootcamps, and Universities curating new courses to meet this demand. However, it can be difficult to choose where to go to get the right content and the best resources. With the world being forced to work from home due to the pandemic, there are a lot of people who are studying remotely. We are becoming more prone to watching lectures from a Zoom call or a video.

An array of new-age technologies is supporting three vital business needs: automation of processes, gaining insights through data analysis, and engaging with customers effectively. Operating and sustaining AI tools and software requires a specific set of skills, knowledge, and attributes that are lacking among graduates. The gap between demand and supply in the fields of data and AI is an opportunity for freshers and professionals to upskill and make a successful career. AI and data science technologies are being adopted by almost every sector for better outcomes. Despite their size, all businesses are looking at leveraging data to drive efficiencies.

Deep learning frameworks are trending among machine learning developers Deep.

Mixed-integer programming saves the day. Taking a cue from consumer supply chains and the data-driven advances that have revolutionized them in recent decades, Gabe Verzino walks us through a scheduling program that would empower both patients and healthcare providers to use their time more efficiently. Bayes' Theorem might sound, well, theoretical. As Khuyen Tran shows in her recent tutorial (based on the traffic patterns of her own website), it can also be a powerful tool for detecting and analyzing change points in your data. The road to the perfect shot of espresso passes through a lot of data.

Automate Microsoft Excel and Word Using Python, by Mohammad Khorasani Django's 9 Most Common Applications, by Aakash Bijwe Learning Data Science and Machine Learning: First Steps after the Roadmap, by Harshit Tyagi How to Engineer Date Features in Python, by Matthew Mayo The Most Important Tool for Data Engineers, by Leo Godin Django's 9 Most Common Applications, by Aakash Bijwe Automate Microsoft Excel and Word Using Python, by Mohammad Khorasani Learning Data Science and Machine Learning: First Steps after the Roadmap, by Harshit Tyagi The Significance of Data-centric AI, by Vidhi Chugh The Most Important Tool for Data Engineers, by Leo Godin

At the start of this year, I published a mind map on the Data Science learning roadmap (shown below). The roadmap was widely accepted, that article got translated into different languages, and a large number of folks thanked me for publishing it. Everything was good until a few aspirants pointed out that there are too many resources and many of them are expensive. Python programming was the only branch that had a number of really good courses, but it ends right there for beginners. Answers to a lot of these questions can be found in the book Deep Learning by Ian Goodfellow and Yoshua Bengio.But that book is a bit too technical and math-heavy for many.

Data science is a success. The data science field is a very competitive market, especially to get one of the (supposed) dream jobs at one of the big tech companies. The positive news is that you have it in your hand to gain a competitive advantage for such a position by preparing yourself adequately. On the other hand, there are (too) many MOOCs, master programs, bootcamps, blogs, videos and data science academies. As a beginner, you feel lost. Which course should I attend? What topics should I learn?

There is a vast and growing number of Data Science resources. It can be hard to find the best ones for you. It may even be hard to find the right "Roadmap for Data Science" or "Top Skills to Learn for Data Science". I don't claim to have the best resources or the correct path to a career in Data Science. What I have is a list of useful resources and if even one of them furthers your learning my goal is accomplished.