Deep Dive into the World of Data Science Through this Blog, we will read about what is data science, why it is such a buzzword these days, what makes data science such an effective and a hot technology to look forward to, what is it like to be a data scientist, what do you need to achieve to be a data scientist. You will also be made familiar about the applications, advantages, disadvantages, examples, real-life use cases, differences between machine learning and artificial intelligence vs neural networks vs deep learning vs prediction analysis. We will also be reading about the various frameworks and libraries which are in very popular demand these days such as Numpy which stands for numerical python, Pandas for data frames, Scikit learn for cross-validation techniques and other model fitting techniques, seaborn for analysis, heatmaps, Tensorflow, etc. Data science is probably the most unexplored territory today and the scope to learn and create and do something out of the box is way too much in this technology and field of sciences and mathematics. Through this Blog, we will read about what is data science, why it is such a buzzword these days, what makes data science such an effective and a hot technology to look forward to, what is it like to be a data scientist, what do you need to achieve to be a data scientist. You will also be made familiar about the applications, advantages, disadvantages, examples, real-life use cases, differences between machine learning and artificial intelligence vs neural networks vs deep learning vs prediction analysis.

This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more. Numerical Python, Second Edition, presents many brand-new case study examples of applications in data science and statistics using Python, along with extensions to many previous examples. Each of these demonstrates the power of Python for rapid development and exploratory computing due to its simple and high-level syntax and multiple options for data analysis. After reading this book, readers will be familiar with many computing techniques including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling, and machine learning.

Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical capabilities in Python, its standard library, and the extensive ecosystem of computationally oriented Python libraries, including popular packages such as NumPy, SciPy, SymPy, Matplotlib, Pandas, and more, and how to apply these software tools in computational problem solving. Python has gained widespread popularity as a computing language: It is nowadays employed for computing by practitioners in such diverse fields as for example scientific research, engineering, finance, and data analytics. One reason for the popularity of Python is its high-level and easy-to-work-with syntax, which enables the rapid development and exploratory computing that is required in modern computational work. After reading and using this book, you will have seen examples and case studies from many areas of computing, and gained familiarity with basic computing techniques such as array-based and symbolic computing, all-around practical skills such as visualisation and numerical file I/O, general computational methods such as equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning. Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical capabilities in Python, its standard library, and the extensive ecosystem of computationally oriented Python libraries, including popular packages such as NumPy, SciPy, SymPy, Matplotlib, Pandas, and more, and how to apply these software tools in computational problem solving.

In the last 50 years there are two things that have emerged in a technological world. First, applied mathematics has moved much more into numerical methods than in trying to solve problems analytically. The second thing that has emerged is that computing has both led and followed the numerical computing revolution. Python, amongst languages, is arguably a language with links to optimized code (such as C or Fortran) plus a language capable of a plethora of tasks, including scientific calculation, statistical modelling, network analysis, machine learning, language processing, and so forth. Johansson's book fits beautifully into a niche where serious science or other endeavour requires both some cookbook code and explanation of some basics.