Data Modeling

Data Modelling Specialist

Person who passes examination of Data Modelling Specialist at least will have the following competencies:

  1. Data Science Concepts
    In this data science course, you will learn key concepts in data acquisition, preparation, exploration, and visualization taught alongside practical application oriented examples such as how to build a cloud data science solution using Google or Microsoft platform, with R or Python.
  2. Statistics for Data Science
    In this course, you will start building the foundation you need to think statistically, to speak the language of your data, to understand what they are telling you. The foundations of statistical thinking took decades upon decades to build, but they can be grasped much faster today with the help of computers. With the power of Python-based tools, you will rapidly get up to speed and begin thinking statistically by the end of this course.
  3. Data Science Programming using Python
    Python is a general-purpose programming language that is becoming more and more popular for doing data science. Companies worldwide are using Python to harvest insights from their data and get a competitive edge. Unlike any other Python tutorial, this course focuses on Python specifically for data science. In our Intro to Python class, you will learn about powerful ways to store and manipulate data as well as cool data science tools to start your own analyses.
  4. Data Analysis Exploration
    When your dataset is represented as a table or a database, it’s difficult to observe much about it beyond its size and the types of variables it contains. In this course, you’ll learn how to use graphical and numerical techniques to begin uncovering the structure of your data. Which variables suggest interesting relationships? Which observations are unusual? By the end of the course, you’ll be able to answer these questions and more, while generating graphics that are both insightful and beautiful.
  5. Data Manipulation
    Harness the power of tools such as pandas so you can extract, filter, and transform your data quickly and efficiently.
  6. Essential Data Modelling
    The value of Data Scientists rests on their ability to describe the world and to make predictions. In this course, you’ll learn how to use Python to perform supervised learning, an essential component of Machine Learning. You’ll learn how to build predictive models, how to tune their parameters and how to tell how well they will perform on unseen data, all the while using real world datasets. You’ll do so using scikit-learn, one of the most popular and user-friendly machine learning libraries for Python.

Data Modelling Qualified

Person who passes examination of Data Modelling Qualified at least will have the following competencies:

  1. Spark Data Science
    Spark has emerged as the most promising big data analytics engine for data science professionals. The true power and value of Apache Spark lies in its ability to execute data science tasks with speed and accuracy. Spark’s selling point is that it combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing, and visualizations. It lets you tackle the complexities that come with raw unstructured data sets with ease.
  2. Intermediate Data Modelling
    Encompasses a variety of techniques in machine learning, from clustering to dimension reduction to matrix factorization. In this course, you’ll learn the fundamentals of unsupervised learning and implement the essential algorithms using scikit-learn and scipy. You will learn how to cluster, transform, visualize, and extract insights from unlabelled datasets
  3. Essential Spark SQL
    This course is equally applicable to data engineers, data scientist, analysts, architects, software engineers, and technical managers interested in a thorough, hands-on overview of Apache Spark. The course covers the fundamentals of Apache Spark SQL and other high-level data access tools. The class is a mixture of lecture and hands-on labs.

Data Modelling Professional

Person who passes examination of Data Modelling Professional at least will have the following competencies:

  1. Machine Learning
    This course is designed for data scientists who have a basic knowledge of Python and are looking for an extended overview of various tools, services and frameworks which become essential in machine learning. Deep learning is the machine learning technique behind the most exciting capabilities in diverse areas like robotics, natural language processing, image recognition and artificial intelligence. In this course, you’ll gain hands-on, practical knowledge of how to use deep learning with Keras 2.0, the latest version of a cutting-edge library for deep learning in Python.
  2. Text Mining
    This course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort. Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers. However, several statistical approaches have been shown to work well for the “shallow” but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and their potential applications.