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Learning Path for Data Scientists Fundamental of Python and R(10Hrs) Basics of Python and R Conditional and loops String and list objects. Functions & OOPs concepts. Exception handling. Database programming. Data scientists must know how to code - start by learning the fundamentals of two popular programming languages Python and R. Data Wrangling(16hrs) Reading CSV, JSON, XML and HTML files using Python NumPy & pandas Relational databases and data manipulation with SQL Scipy libraries Loading, cleaning, transforming, merging, and reshaping data Once you have the core skill of programming covered – dip your feet in the nitty-gritty of working with data by learning how to wrangle and visualize them. Statistics and Probability(8Hrs) Probability mass functions Probability distribution functions Cumulative distribution functions Modeling distributions Inferential statistics Estimation Hypothesis testing Implementation of sta...

A Roadmap To Become An Android Developer This article is meant for all those bright minds out there who wants to learn Android Development but have no idea 'where to start'. Getting started with the Android Development First of all, you must have a basic knowledge of the Java language for Android Development. Learn the Java language from the Head-First Java book. Set up Android Studio IDE for Android Development on your PC or Laptop. Take a course or any book on Android app development with which you think you are comfortable. Being comfortable with the course or the book is very important. During the course build something on whatever you read. Setting up Kotlin in Android Studio. Introduction to Android Studio IDE. Android User Interface — XML and View Components. There is no better way to learn something than by doing it practically. Improving yourself as an Android Developer Understand the Activity Life-cycle as having the knowledge of...

Learning Path for Deep Learning in 2019 Here is a high-level overview of the core concepts you should and master in the deep learning sphere: Getting Started:  Deep learning is a vast field made up of several components. So to kick off your learning journey, it is recommended to start from the ground up. The first month will be all about understanding what deep learning means, covering basic descriptive statistics and probability concepts, and learning Python! Machine Learning Basics:  The logical next step in our learning path takes you into the world of machine learning. This includes techniques like linear regression, logistic regression, and regularization methods. Deep learning cannot be truly grasped until you know the core concepts of linear algebra and calculus, so complement your skill-set with an introduction to matrices, vectors and derivatives. Introduction to Deep Learning and Keras:  This is what you’ve been waiting for! March is ...