- Use R on stock market data for insight and ideas
- Apply basic technical analysis on stock market data
- Plot great looking financial charts
- Gain additional insights by comparing similar stocks

What this course will cover:

- Easily access free, stock-market data using R and the quantmod package
- Build great looking stock charts with quantmod
- Use R to manipulate time-series data
- Create a moving average from scratch
- Access technical indicators with the TTR package
- Create a simple trading systems by shifting time series using the binhf package
- A look at trend-following trading systems using moving averages
- A look at counter-trend trading systems using moving averages
- Using more sophisticated indicators (ROC, RSI, CCI, VWAP, Chaikin Volatility)
- Grouping stocks by theme to better understand them
- Finding coupling and decoupling stocks within an index

Analyzing Stock Market Data with R :Practical Data Science

- Guided Tour of Machine Learning in Finance
- Fundamentals of Machine Learning in Finance
- Reinforcement Learning in Finance
- Overview of Advanced Methods of Reinforcement Learning in Finance

The main goal of this specialization is to provide the knowledge and practical skills necessary to develop a strong foundation on core paradigms and algorithms of machine learning (ML), with a particular focus on applications of ML to various practical problems in Finance.

The specialization aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include:

(1) mapping the problem on a general landscape of available ML methods,

(2) choosing particular ML approach(es) that would be most appropriate for resolving the problem, and

(3) successfully implementing a solution, and assessing its performance.

The specialization is designed for three categories of students:

· Practitioners working at financial institutions such as banks, asset management firms or hedge funds

· Individuals interested in applications of ML for personal day trading

The specialization aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include:

(1) mapping the problem on a general landscape of available ML methods,

(2) choosing particular ML approach(es) that would be most appropriate for resolving the problem, and

(3) successfully implementing a solution, and assessing its performance.

The specialization is designed for three categories of students:

· Practitioners working at financial institutions such as banks, asset management firms or hedge funds

· Individuals interested in applications of ML for personal day trading

Machine Learning and Reinforcement Learning in Finance

- How Google does Machine Learning
- Launching into Machine Learning
- Intro to TensorFlow
- Feature Engineering
- Art and Science of Machine Learning

What is machine learning, and what kinds of problems can it solve? What are the five phases of converting a candidate use case to be driven by machine learning, and why is it important that the phases not be skipped? Why are neural networks so popular now? How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent and a thoughtful way of creating datasets? Learn how to write distributed machine learning models that scale in Tensorflow, scale out the training of those models. and offer high-performance predictions. Convert raw data to features in a way that allows ML to learn important characteristics from the data and bring human insight to bear on the problem. Finally, learn how to incorporate the right mix of parameters that yields accurate, generalized models and knowledge of the theory to solve specific types of ML problems. You will experiment with end-to-end ML, starting from building an ML-focused strategy and progressing into model training, optimization, and productionalization with hands-on labs using Google Cloud Platform.

Machine Learning with TensorFlow on Google Cloud Platform

Business analysts and developers are increasingly collecting, curating, analyzing, and reporting on crucial business data. The R language and its associated tools provide a straightforward way to tackle day-to-day data science tasks without a lot of academic theory or advanced mathematics.

*Practical Data Science with R* shows you how to apply the R programming language and useful statistical techniques to everyday business situations. Using examples from marketing, business intelligence, and decision support, it shows you how to design experiments (such as A/B tests), build predictive models, and present results to audiences of all levels.

This book is accessible to readers without a background in data science. Some familiarity with basic statistics, R, or another scripting language is assumed.

**What's Inside**

This book is accessible to readers without a background in data science. Some familiarity with basic statistics, R, or another scripting language is assumed.

- Data science for the business professional
- Statistical analysis using the R language
- Project lifecycle, from planning to delivery
- Numerous instantly familiar use cases
- Keys to effective data presentations

The book’s collection of projects, comprehensive sample solutions, and follow-up exercises encompass practical topics pertaining to data processing, including:

- Non-standard, complex data formats, such as robot logs and email messages
- Text processing and regular expressions
- Newer technologies, such as Web scraping, Web services, Keyhole Markup Language (KML), and Google Earth
- Statistical methods, such as classification trees, k-nearest neighbors, and naïve Bayes
- Visualization and exploratory data analysis
- Relational databases and Structured Query Language (SQL)
- Simulation
- Algorithm implementation
- Large data and efficiency

This book is for users who are familiar with computational methods. If you want to learn about the advanced features of R, including the computer-intense Monte-Carlo methods as well as computational tools for statistical simulation, then this book is for you. Good knowledge of R programming is assumed/required.

- The book aims to explore advanced R features to simulate data to extract insights from your data.
- Get to know the advanced features of R including high-performance computing and advanced data manipulation
- See random number simulation used to simulate distributions, data sets, and populations
- Simulate close-to-reality populations as the basis for agent-based micro-, model- and design-based simulations
- Applications to design statistical solutions with R for solving scientific and real world problems
- Comprehensive coverage of several R statistical packages like boot, simPop, VIM, data.table, dplyr, parallel, StatDA, simecol, simecolModels, deSolve and many more.

Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.

With this handbook, you’ll learn how to use:

With this handbook, you’ll learn how to use:

**IPython and Jupyter:**provide computational environments for data scientists using Python**NumPy:**includes the*ndarray*for efficient storage and manipulation of dense data arrays in Python**Pandas:**features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python**Matplotlib:**includes capabilities for a flexible range of data visualizations in Python**Scikit-Learn:**for efficient and clean Python implementations of the most important and established machine learning algorithms

Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users.

What you will learn

What you will learn

- Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux
- Get data ready for your data science project
- Manipulate, fix, and explore data in order to solve data science problems
- Set up an experimental pipeline to test your data science hypotheses
- Choose the most effective and scalable learning algorithm for your data science tasks
- Optimize your machine learning models to get the best performance
- Explore and cluster graphs, taking advantage of interconnections and links in your data

If you are a Python developer who wants to master the world of data science then this book is for you. Some knowledge of data science is assumed.

- Manage data and perform linear algebra in Python
- Derive inferences from the analysis by performing inferential statistics
- Solve data science problems in Python
- Create high-end visualizations using Python
- Evaluate and apply the linear regression technique to estimate the relationships among variables.
- Build recommendation engines with the various collaborative filtering algorithms
- Apply the ensemble methods to improve your predictions
- Work with big data technologies to handle data at scale

Anaconda is an open source platform that brings together the best tools for data science professionals with more than 100 popular packages supporting Python, Scala, and R languages. Hands-On Data Science with Anaconda gets you started with Anaconda and demonstrates how you can use it to perform data science operations in the real world.

The book begins with setting up the environment for Anaconda platform in order to make it accessible for tools and frameworks such as Jupyter, pandas, matplotlib, Python, R, Julia, and more. You'll walk through package manager Conda, through which you can automatically manage all packages including cross-language dependencies, and work across Linux, macOS, and Windows. You'll explore all the essentials of data science and linear algebra to perform data science tasks using packages such as SciPy, contrastive, scikit-learn, Rattle, and Rmixmod.

Once you're accustomed to all this, you'll start with operations in data science such as cleaning, sorting, and data classification. You'll move on to learning how to perform tasks such as clustering, regression, prediction, and building machine learning models and optimizing them. In addition to this, you'll learn how to visualize data using the packages available for Julia, Python, and R.

**What you will learn**

]]>The book begins with setting up the environment for Anaconda platform in order to make it accessible for tools and frameworks such as Jupyter, pandas, matplotlib, Python, R, Julia, and more. You'll walk through package manager Conda, through which you can automatically manage all packages including cross-language dependencies, and work across Linux, macOS, and Windows. You'll explore all the essentials of data science and linear algebra to perform data science tasks using packages such as SciPy, contrastive, scikit-learn, Rattle, and Rmixmod.

Once you're accustomed to all this, you'll start with operations in data science such as cleaning, sorting, and data classification. You'll move on to learning how to perform tasks such as clustering, regression, prediction, and building machine learning models and optimizing them. In addition to this, you'll learn how to visualize data using the packages available for Julia, Python, and R.

- Perform cleaning, sorting, classification, clustering, regression, and dataset modeling using Anaconda
- Use the package manager conda and discover, install, and use functionally efficient and scalable packages
- Get comfortable with heterogeneous data exploration using multiple languages within a project
- Perform distributed computing and use Anaconda Accelerate to optimize computational powers
- Discover and share packages, notebooks, and environments, and use shared project drives on Anaconda Cloud
- Tackle advanced data prediction problems