During this Course you'll learn about Data Science, the goals and objectives of the Data Science Specialization and each of its components. You'll also get to know why data scientists are now in such demand, and the skills required to succeed in different jobs. Different business cases are presented during session implementation as case studies to view and discuss the data science economical potential.
 

Python is a popular, easy to learn programming language. It is commonly used in the field of data analysis because there are very efficient libraries available to process large amounts of data. This so-called data analysis stack includes libraries such as NumPy, Pandas, and Matplotlib that we will familiarize ourselves with. In this course, an overview is given of the different phases of the data analysis pipeline using Python and its data analysis ecosystem. What is typically done in data analysis? We assume that data is already available, so we only need to download it. After downloading the data it needs to be cleaned to enable further analysis. In the cleaning phase the data is converted to some uniform and consistent format. After which the data can, for instance, be combined or divided into smaller chunks; grouped or sorted; condensed into small number of summary statistics; numerical or string operations can be performed on the data.
 

Python is a popular, easy to learn programming language. It is commonly used in the field of data analysis because there are very efficient libraries available to process large amounts of data. This so-called data analysis stack includes libraries such as Pandas, and Matplotlib that we will familiarize ourselves with in this course.

This module presents the assumptions, principles, and techniques necessary to gain insight into data via EDA--exploratory data analysis. we will be covering the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data.
 

We will provide learners with an introduction to research data management and sharing. After completing this course, learners will understand the diversity of data and their management needs across the research data life cycle, be able to identify the components of good data management plans, and be familiar with best practices for working with data including the organization, documentation, and storage and security of data. Learners will also understand the impetus and importance of archiving and sharing data as well as how to assess the trustworthiness of repositories.
 

This Course will introduce Machine Learning concepts and practical information on how Machines learn to perform certain tasks as those used in the Artificial Intelligence world.

In recent years, big data has become a popular buzzword when talking about growth in the technology sector. However, there are confusions about what it is exactly, especially for people who are not involved directly in technical departments. This can be due to lack of leadership sophistication around the concept and its associated possibilities. Big Data: A Beginner’s Guide for Non-Technical People is a guide to address that. We will go through the core concepts and ideas behind Big Data and with the goal of keeping it jargon-free and simple.
 

The main objective of this course is to familiarize the students with recent technological advancements in manipulating, storing, and analyzing big data. The emphasis of the course will be on practicing different components of Apache Spark, as the most important big data framework. Students will gain hands-on experience through multiple practices on Spark SQL, Spark ML (Machine Learning) API and Spark Streaming. In addition, topics in analyzing huge amount of textual content using Spark NLP and Elasticsearch technology will be covered as well.