ACN Pro PRIME

The IT industry is increasingly seeing demand for a professional who has deep expertise in all complementary skills in the field of Data Science.

The course is designed to take the students from understanding the basics of handling data to the complexities of applying algorithms using Machine Learning.

Duration: 2 Years

Eligibility: 10+2/Any Graduate

Languages: English

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Exit Profile

Financial Data Analyst & Python Programmer, Data Analytics Professional, AI Developer, Machine Learning Specialist

Salient Features

Single comprehensive structured program

Focused and Specialized Technologies

Latest software & technologies covered

Learning in a chronological order

Blended learning approach

Practical exams

Capstone projects

Assignments

Experienced Instructors

Course Content

Module

Office Automation

Data Handling using T-SQL

Managing Large DataSets with MongoDB

Inferential Statistical Analysis

Application Based Programming in Python

Data Mining with WEKA

Capstone Project for Data Mining

Learning Outcomes

Work with spreadsheets and analyze data using MS Excel.

Learn fundamental concepts of Transact-SQL (T-SQL) commands.

Understand MongoDB concepts, features, architecture and data model, and learn to install, configure, and monitor open-source NoSQL databases.

Understand the basics of statistical analysis, descriptive statistics, predictive analytics, probability, and Bayes theorem.

Develop applications using Python language.

Use WEKA to perform data mining tasks.

Develop a Capstone project using WEKA and implement data mining tasks.

Job Opportunity as :

Financial Data Analyst & Python Programmer

Module

Data Science with Python

Data Science using R Programming

Foundation of Big Systems with Hadoop

Advanced Data Science with Python

Visual Analytics with Tableau

Capstone Project for Data Science

Learning Outcomes

Gain an introduction to Anaconda, Jupyter Notebook, and Google Collab for coding and data visualization.

Master data exploration, predictive & descriptive analytics techniques using R.

Understand the components of the Hadoop ecosystem-YARN, MapReduce, HDFS, and Apache Spark with the Big Data processing lifecycle.

Implement advanced data science techniques with Python libraries.

Build visualizations, organize data, and design dashboards to empower more meaningful business decisions using Tableau Desktop data visualization and reporting tool.

Develop a real-world advanced data science Capstone project

Job Opportunity as :

Data Analytics Professional

Module

AI Primer

AI Applications of NLP

AI and ML with Python

Applied Machine Learning using Python

Deep Learning using Neural Networks

Fundamentals of Computer Vision

Capstone Project-Recommendation Engine and Customer Churn Prediction

Learning Outcomes

Gain an understanding of AI.

Gain knowledge in NLP and learn the use of AI in NLP.

Use important building blocks of AI & ML with Python, make data modelling decisions, interpret output of the algorithms, and validate results.

Master ML concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop AI algorithms.

Master deep learning concepts and TensorFlow open-source framework, implement deep learning algorithms, and build ANN.

Understand the working of CV and learn about AI workflows and performance metrics.

Learn Convolution Neural Networks (CNN), object detection, and face recognition using CV.

Develop a real-world Capstone project on recommendation engine and perform customer churn prediction.

Job Opportunity as :

AI Developer

Module

Reinforcement Learning with Data Science

Applications of Computer Vision

Introduction to Data Lakes

Advanced NLP with spaCy

MLops Model Lifecycle and Deployment

Designing Efficient Deep Learning Systems

Capstone Project-Using Computer Vision and Deep Learning for Traffic Monitoring

Learning Outcomes

Gain an understanding of RL Framework and its components.

Understand Transfer Learning, Recurrent Neural Networks (RNN), and YOLO algorithm.

Learn the advanced concepts of image manipulation and face recognition with CV.

Understand Data Lakes, their components, and implement Data Lakes.

Understand sequential data, time-series analysis, advanced language models such as Transformers, BERT algorithm, XLNET algorithm, and use spaCy for NLP.

Implement ML model by building an end-to-end machine learning pipeline and deploying the model using Flask.

Implement sophisticated and cutting-edge techniques in ML using ANN, CNN, and RNN on text and image data.

Develop a Capstone project for traffic monitoring using CV.