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Beginner Guide using Python, Pandas, NumPy, Scikit-Learn, IPython, TensorFlow and Matplotlib

Why read on? First, you’ll learn how to use Python in data analysis (which is a bit cooler and a bit more advanced than using Microsoft Excel). Second, you’ll also learn how to gain the mindset of a real data analyst (computational thinking). More importantly, you’ll learn how Python and machine learning applies to real world problems (business, science, market research, technology, manufacturing, retail, financial). We’ll provide several examples on how modern methods of data analysis fit in with approaching and solving modern problems. This is important because the massive influx of data provides us with more opportunities to gain insights and make an impact in almost any field. This recent phenomenon also provides new challenges that require new technologies and approaches. In addition, this also requires new skills and mindsets to successfully navigate through the challenges and successfully tap the fullest potential of the opportunities being presented to us. For now, forget about getting the “sexiest job of the 21st century” (data scientist, machine learning engineer, etc.). Forget about the fears about artificial intelligence eradicating jobs and the entire human race. This is all about learning (in the truest sense of the word) and solving real world problems. We are here to create solutions and take advantage of new technologies to make better decisions and hopefully make our lives easier. And this starts at building a strong foundation so we can better face the challenges and master advanced concepts. 2. Why Choose Python for Data Science & Machine Learning Python is said to be a simple, clear and intuitive programming language. That’s why many engineers and scientists choose Python for many scientific and numeric applications. Perhaps they prefer getting into the core task quickly (e.g. finding out the effect or correlation of a variable with an output) instead of spending hundreds of hours learning the nuances of a “complex” programming language. This allows scientists, engineers, researchers and analysts to get into the project more quickly, thereby gaining valuable insights in the least amount of time and resources. It doesn’t mean though that Python is perfect and the ideal programming language on where to do data analysis and machine learning. Other languages such as R may have advantages and features Python has not. But still, Python is a good starting point and you may get a better understanding of data analysis if you use it for your study and future projects. Python vs R You might have already encountered this in Stack Overflow, Reddit, Quora, and other forums and websites. You might have also searched for other programming languages because after all, learning Python or R (or any other programming language) requires several weeks and months. It’s a huge time investment and you don’t want to make a mistake. To get this out of the way, just start with Python because the general skills and concepts are easily transferable to other languages. Well, in some cases you might have to adopt an entirely new way of thinking. But in general, knowing how to use Python in data analysis will bring you a long way towards solving many interesting problems. Many say that R is specifically designed for statisticians (especially when it comes to easy and strong data visualization capabilities). It’s also relatively easy to learn especially if you’ll be using it mainly for data analysis. On the other hand, Python is somewhat flexible because it goes beyond data analysis. Many data scientists and machine learning practitioners may have chosen Python because the code they wrote can be integrated into a live and dynamic web application. Although it’s all debatable, Python is still a popular choice especially among beginners or anyone who wants to get their feet wet fast with data analysis and machine learning. It’s relatively easy to learn and you can dive into full time programming later on if you decide this suits you more. Widespread Use of Python in Data Analysis There are now many packages and tools that make the use of Python in data analysis and machine learning much easier. TensorFlow (from Google), Theano, scikit-learn, numpy, and pandas are just some of the things that make data science faster and easier.

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