Whether you’re looking to change jobs, get a promotion or sharpen your current skills
Get your Data Science Professional Certificate and Stand Out in the Market
This course is a practical fast-paced training to get into data science field. This prepare you with how to work in data-scientific way, explore business activity, analyze data, develop predictive models and monitoring outputs.
We have created our learning paths in line with the Data Professional Skills Framework to support data and analytics knowledge development focused on four paths: BigData Management & Analytics, Data Analyst & Visualization, Machine Learning and Deep Learning & Commuter Vision.
1. BigData Management & Analytics Track: –
Today, organizations in every industry are being showered with imposing quantities of new information. Along with traditional sources, many more data channels and categories now exist. Collectively, these vastly larger information volumes and new assets are known as big data. Enterprises are using technologies such as MapReduce and Hadoop to extract value from big data. This course provides an in-depth overview of Hadoop and MapReduce, the cornerstones of big data processing. To crystalize the concepts behind Hadoop and MapReduce, write basic MapReduce programs, gain familiarity with advanced MapReduce programming practices, and utilize interfaces such as Pig and Hive to interact with Hadoop. You will also learn about real-world situations were MapReduce techniques can be used.
Module 1: Hadoop Introduction
Module 2: Hadoop Installation and Hands-on
Module 3: Introduction to ETL Tool (Pig)
Module 4: Introduction to Hive (Warehouse)
Module 5: Introduction to Map Reduce
Module 6: NoSQL Databases: Casandra and MongoDB
Module 7: Apache Spark Basics
2. Data Analytics & Visualization Track : –
This course introduces the basic goals and techniques in data science and analytics process with some theoretical foundations which include useful statistical concepts and data visualization to get insight about from the dataset. The course provides basic principles on important steps of the process which include data collecting, curating, analyzing, building predictive models and reporting and presenting results to audiences of all levels. R programming or Python Programming language and statistical analysis techniques are introduced based on examples such as from marketing, business intelligence and decision support. Finally, you get introduced to state of the art Business Intelligence Data Visualization tools like PowerBI and Tableau and how to use it in storytelling.
Module 1: Introduction to Data Science & Analytics
Module 2: The Stages of a Data Science Project
Module 3: Programming for Data Science (R / Python)
Module 4: Data Cleansing and Manipulation
Module 5: Exploring and Visualizing Data
Module 6: Data Visualization using Tableau Desktop
Module 7: Data Visualization with PowerBI Desktop
3. Machine Learning for Decision Making Track: –
Machine learning were around us for some time and it help us to solve so many problems, but when it comes to use huge amount of data the tradition machine learning can’t help a lot. Due to the development in computation capabilities, statistical and mathematical algorithms to improve our prediction and modelling. Here, where Artificial Neural Networks (ANN) and it’s Deep Neural Networks (DNN) comes to picture.
Module 1: Classification
Module 2: Regression
Module 3: Tree and Ensemble Methods
Module 4: Clustering and Recommenders
Module 5: Neural Networks
Module 6: Predictive Modeling
Module 7: Applied Machine Learning
Module 8: Capstone Project
4. Deep Learning & Computer Vision: –
In this course, you will develop an understanding of the principles of machine learning and derive practical solutions using predictive analytics. you’ll learn about some of the most widely used and successful machine learning techniques. You’ll have the opportunity to implement these algorithms yourself, and gain practice with them. You will also learn some of practical hands-on tricks and techniques (rarely discussed in textbooks) that help get learning algorithms to work well. This is an “applied” machine learning Course, and we emphasize the intuitions and know-how needed to get learning algorithms to work in practice, rather than the mathematical derivations. At the end of course, you are going to work on large scale real-world project to emphasize all skills learned in all three course.
Module 1: Introduction to Deep Learning
Module 2: Neural Networks with TensorFlow
Module 3: Convolutional Neural Networks (CNN)
Module 4: Recurrent Neural Networks (RNN)
Module 5: Boltzmann (RBM) and Autoencoders
Module 6: Introduction to Computer Vision
Module 7: Introduction to Keras & OpenCV
Module 8: Capstone Project