This course is ideal for you, if you wish is to start your path to becoming a Data Scientist! Data Scientist is one of the hottest jobs recently the United States and in Europe and it is a rewarding career with a high average salary. The massive amount of data has revolutionized companies and those who have used these big data has an edge in competition. These companies need data scientist who are proficient at handling, managing, analyzing, and understanding trends in data. This course is designed for both beginners with some programming experience or experienced developers looking to extend their knowledge in Data Science!
The Complete Machine Learning 2019 Python,Math Dummy To Pro by SkyHub Academy Start Machine Learning & Data Science era with Math, Python & Libraries like: SKlearn, Pandas, NumPy, Matplotlib & Gym machine learning is becoming a widely-used word on everybody's tongue, and this is reasonable as data is everywhere, and it needs something to get use of it and unleash its hidden secrets, and since humans' mental skills cannot withstand that amount of data, it comes the need to learn machines to do that for us. So we introduce to you the complete ML course that you need in order to get your hand on Machine Learning and Data Science, and you'll not have to go to other resources, as this ML course collects most of the knowledge that you'll need in your journey. What you'll learn Achieve the mastery in machine learning from simple linear regression to advanced reinforcement learning projects. Get a deeper intuition about different Machine Learning nomenclatures. Be able to manipulate different algorithms with the power of Mathematics.
Write Excel tools with Python instead of VBA and call your code directly from within Excel, e.g. Get Coupon ED Bestseller What you'll learn Automate Excel with clean and powerful Python Code Learn and master the xlwings library from 0 to 100 Use Excel as Graphical User Interface (GUI) and run your Python code with Excel Create powerful Dashboard Apps with Excel (frontend) and Python (backend) Use powerful Data Visualization Tools (Matplotlib, Seaborn) in Excel Learn Python from scratch with a taylor-made Crash Course (For Python beginners) Write UDFs (user defined functions) and use Numpy, Pandas and Machine Learning Libraries directly in Excel Write Excel tools with Python instead of VBA and call your code directly from within Excel Use xlwings to automate Excel reports with Python Write and use Dynamic Arrays with xlwings Run your financial model 10,000 times & more with a Python Monte Carlo Simulation Load (financial) data from Web APIs directly into Excel Run Python Scripts from within Excel with Run main and RunPython Replace VBA macros with clean and powerful Python code Requirements A desktop computer (Windows or Mac) capable of storing and running Python/Anaconda. The course will walk you through installing the necessary free software. Please note that 10%-15% of the course content (UDFs) work on Windows only! Willingness to code and work with Python.
In the ever-changing ecosystem of data science tools, you often find yourself needing to learn a new language in order to keep up with the newest methods or to more effectively collaborate with coworkers. I've been an R coder for a few years, but wanted to transition to Python in order to take full advantage of the deep learning libraries and tools such as PySpark. Also, I joined the data science team at Zynga, where Python is the preferred language. It's only been a few weeks, but I'm starting to get the hang of performing exploratory data analysis and predictive modeling in this new language. This isn't the first time that I've tried to quickly ramp up on a new data science language, but it has been the most successful.
Let us understand what are the most important and useful python libraries that can be used in data science. Data Science, as you all know, it is the process involved in studying the data. Yes, all you got to do is study the data and get new insights from the data. Here there is no need to focus on applying from scratch or learning new algorithms, all you need to know is learn how to approach the data and solve the problem. One of the key things that you need to know is using appropriate libraries to solve a data science problem.