DATA SCIENCE AND ANALYTICS:
Data analysis is a process of inspecting, cleansing, transforming and modeling data with the goal of discovering useful information, informing conclusions and supporting decision-making. Data analysis is used in different business, science, and social science domains. In today’s business world, data analysis plays a role in making decisions more scientific and helps businesses operate more effectively.
Data Science helps design experiments, and data-driven decision making. This is shared among team members, engineers, and leadership in clear language and with data visualizations so that the other employees understand the implications.
We, at MIET simplify these complex processes of understanding Data Science into simple structures so that students master the concepts to become leaders of the subject.
We offer the following Courses in Data Science:
DATA SCIENCE FOUNDATIONS
This course helps in understanding the basic foundations of Data Science. The term was first coined by DJ Patil and Jeff while working for LinkedIn in 2008. It is an interdisciplinary field of data inference, algorithm building and systems to gain meaningful information from data.
DATA SCIENCE WITH PYTHON
Decision making for business, forecasting weather, or the study of protein structures in biology involves a multidisciplinary approach of using mathematical models, statistics, graphs, databases and the scientific logic behind these data analysis. This requires a programming language which can cater to all these diverse needs of data science. Python outweighs as one such language as it has numerous libraries and built in features that is easy to tackle the needs of Data science.
TOOLS AND TECHNIQUES OF DATA SCIENCE
The tools and techniques of data science are two different things. Techniques are a set of procedures that are followed to perform a task, whereas a tool is equipment that is used to apply that technique to perform the task.
Data scientists apply operational methods, which are called the techniques on data through various software, which are known as tools. These methods used by data scientists and engineers start from the collection of data to storing and manipulating it, performing statistical analysis on it, and visualization and preparing predictive models for further insights.
The lifecycle of a data science project is composed of various stages. Data passes through each stage and is then transformed into information required by the respective field. The productive tools and techniques used by the data scientists to accomplish the task include among others;
• Advance Statistics
• Data Mining
• Predictive Modeling
• Time Series Forecasting
• Machine Learning
• Optimization Techniques
The Domain Exposure block will provide a gateway into real-life problems of varied domains and teach the ways to solve these problems using principles of data science and analytics.
• Marketing and Retail Analytics
• Web and Social Media Analytics
• Finance and Risk Analysis
• Supply Chain and Logistics
Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions such as learning and problem-solving.
This program caters to working professionals from a variety of industries. The Artificial Intelligence role requires a combination of experience, knowledge in Data Science, and correct tools and technologies. AI is a solid career choice for both new and experienced professionals. The Program in AI and Machine Learning is for: IT professionals, Software developers, Data analysts, Analytics managers, Business analysts, Data engineers, Data scientists, Beginners or recent graduates with a bachelor’s or master’s degree.
We offer the following Courses in Artificial Intelligence:
Introduction to Artificial Intelligence
The Introduction to Artificial Intelligence course is designed to help learners decode the mystery of AI and its business applications.
A foundation of Artificial Intelligence—is the science of assigning a probability through the collection, classification, and analysis of data.
Python for Data Science
This Course would help one to write Python scripts and perform fundamental, and act on data analysis using the Jupyter-based lab environment.
Data Science with Python
This course gives an in-depth knowledge in data analytics, Machine Learning, data visualization, web scraping, and natural language processing.
Machine Learning, a part of AI help to automate data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming.
Deep Learning with TensorFlow and Keras
This course is an advanced form of machine learning providing an understanding of deep learning using TensorFlow and Keras. This course helps to unlock the power of data and view the new horizons in artificial intelligence.
Advanced Deep Learning and Computer Vision
Advanced deep learning skills is a high-level course. This Advanced Deep Learning and Computer Vision course covers real applications of computer vision, and generative-adversarial networks (GANs).
Natural Language Processing and Speech Recognition
This Natural Language Processing and Speech Recognition course is the science of applying machine learning algorithms to process large amounts of natural language data.
This course concentrates on the core concepts in deep reinforcement learning (RL).
AI and Machine Learning Capstone Project
The AI and Machine Learning Capstone help to implement the skills across domains such as e-commerce, finance, and retail.