Advanced Program in Industrial
Data Science (APIDS) ...

SQL, Bigdata and Scala Statistical Programming:

DBMS Programming: SQL, Bigdata and Scala
Statistical Programming: SAS,R and Python
Reporting and Visualization: Excel, VBA, Tableau and Power BI
Data Mining and Advanced Analytics: SAS/Excel/R/Python Advanced Analytics
Machine Leaming and Al: SAS/R/Python/Keras/TensorFlow

Advanced Program in Industrial Data Science (APIDS)

The Advanced Program in Industrial Data Science (APIDS) course equips you with essential skills in database programming, reporting, data visualization, statistics, advanced analytics, and artificial intelligence. You will learn to analyze and visualize large datasets, identify patterns and trends, and present insights effectively through charts, dashboards, and reports.

The curriculum covers machine learning and AI techniques, including supervised and unsupervised learning, deep learning with Python, TensorFlow, and Keras, as well as reinforcement learning for building AI-driven solutions like recommender systems. Additionally, you will explore generative AI techniques and cloud computing, enabling you to automate processes, uncover new data patterns, and develop AI-centric business solutions.

Course Curriculum

What you will learn in (APIDS)?

Skill-1 DBMS Programming and Reporting

First step to learn DBMS Programming skills that helps in managing complex data model from various sources of information files. Combine these files into structured form for data to analysis and reporting.

Excel Base and Advanced
SQL Base and Advanced Programming
Python Base and Advanced Programming
SAS Base and Advanced Programming
R Base and Advanced Programming
Alteryx

Skill-2 Reporting and Visualization

Second step is to develop descriptive analytics by transforming data into business KPI and present insights in a storytelling dashboard using advanced reporting and visualization applications.

Excel Reporting and Dashboard
Tableau Reporting and Visualizations
Power BI Reporting and Visualizations
Qlikview Reporting and Visualizations

Skill-3 Statistics and Advanced Analytics

Third step to Explore Data Analysis (EDA) using Advanced Analytics techniques to predict future outcomes based on history patterns.

Advanced Analytics in Excel
Advanced Analytics in SAS
Advanced Analytics in Python
Advanced Analytics in R

Skill-4 Machine Learning and Artificial Intelligence

Automate actions from known patterns using supervised and unsupervised machine learning, which can help you uncover new data patterns. Develop Al-centric businesses use reinforcement learning for recommender systems.

Machine Learning in Python
Deep Learning on Python, Keras and Tensorflow
AI in Python

A learning that makes you responsible for

Data Analysis and Visualization:

Analyze large datasets to identify patterns, trends, and relationships.
Clean and preprocess data to ensure its quality and suitability for analysis.
Exploratory data analysis (EDA) techniques are employed to gain a deeper understanding of the data.
Use visualization tools and techniques to present findings in a clear and understandable manner. Visualizations such as charts, graphs, and dashboards help stakeholders interpret complex data and make informed decisions.
Visualization also aids in communicating insights effectively to non-technical stakeholders.

Machine Learning & AI:

Apply machine learning algorithms to develop predictive models and make data-driven decisions.
Train models on historical data to recognize patterns and make predictions or classifications on new data.
Use techniques include regression, classification, clustering, and deep learning, among others.

Generative AI Techniques:

Use Generative AI techniques, such as generative adversarial networks (GANs) or variational autoencoders (VAEs), are used to create new data instances.
Employ generative AI for tasks like data augmentation, synthetic data generation, or image and text generation.

Cloud Computing:

Utilize cloud computing platforms like AWS, Azure, or Google Cloud to deploy machine learning models at scale.
Cloud platforms infrastructure and services for model hosting, monitoring, and management.
Facilitate seamless integration with other cloud-based services and enable automatic scaling based on demand.
Cloud deployment ensures accessibility, scalability, reliability, and security of deployed models.

Unsure about how to begin?

Allow our Learning Advisor to assist you.

INDUSTRY REAL-TIME
DATA SCIENCE PROJECTS

Retail Marketing

The world we live in is evolving every day. There are significant changes in the retail market as opposed to how it was a decade or two ago. With sales worth trillions of dollars worldwide, retail industry is expected to develop even further in the coming days. Data Analytics can be used in the retail industry for various reliable decisions. Be it customer retention or sales prediction, we can develop models using data to provide the best possible solutions.

Targeted customer communication
Price optimization
Demand prediction and inventory management
Customer experience enhancement
Market trend prediction
Customer retention
Strategic business decisions to increase sales

Banking

The remarkable variations that have happened in the banking industry over the past few years are not momentary. Many organizations are adapting to the latest trends in technology and are changing their business structure for enhanced security and the best customer experience. To make better business decisions, banks need to connect various data from both structured and unstructured sources. Banking analytics aids in providing valuable insights by gathering, processing, and analyzing data.

Customer identification and acquisition
Portfolio analysis and risk management
Customer retention
Credit risk analysis
Collection analysis
Marketing analysis

Telecom

Telecommunication industry has encountered extraordinary changes over the past few decades. From satellite internet to 5G services, the industry continues to grow together with the advancements in technology. With every company racing to get the best services delivered to each customer, it is challenging to gain a competitive advantage. Telecommunication data analytics (telecom analytics) can help solve your complex business problems by finding the most optimal solutions, using various statistical approaches such as data mining, data manipulation, descriptive modeling, or predictive modeling. We can discern and analyze the existing trends to find the most favorable outcome. Data analytics can help you save operational costs, maximize your profits, increase the sales, and even manage risks.

Targeted customer communication
Price optimization
Demand prediction and inventory management
Customer experience enhancement
Market trend prediction
Customer retention
Strategic business decisions to increase sales

E-Commerce

Ecommerce refers to trade that happens over the internet. Through online stores, it is possible to purchase a wide variety of products using your computer, tablet, smartphone, or other smart devices. Since ecommerce businesses exist in a virtual space, they need effective ecommerce analytics to predict the changes in the market. Ecommerce analytics can provide actionable insights on various aspects such as interaction of shoppers, online shopping trends, and common interests. Using statistical approaches, we can anticipate changes in the market, analyse risk, and make better business decisions.

Information analysis
Inventory forecasting
Customer experience analysis and targeted customer communication
Fraud prevention
Marketing analysis
Price optimization

Healthcare

Healthcare is a collective term for hospital services, medical devices, pharmaceutical services, insurance services, and any other medical care provisions provided for an individual or a community. It is said that prevention is always better than cure. While we cannot always prevent an event from occurring, we can always be prepared for its arrival. By gathering data, analyzing trends, and predicting possible outcomes, the application of Data Analytics in the healthcare industry are limitless. The insights that we obtain from healthcare data can support in making decisions that can have a significant business impact.

Risk Analysis
Insurance claim analysis
Operations analysis
Patient care analysis
Performance monitoring
Operational and interactive dashboards

What requirement do you need?

There are no prerequisites to attend this course online or ofline.
Elementary programming knowledge will be of advantage.

Training Supports & Benefits

Faculty are from IIT & IIM

Learn from the World’s Best Faculty & Industry Experts
Learn with fun Hands-on Exercises & Assignments
Participate in Hackathons & Group Activities

One to One Support

Dedicated Faculty
9AM to 6 PM Support
Participate in Hackathons & Group Activities

Career Assistance

Resume Building & Mock Interview Preparation.
We offer personalized access to our Learning Management System (LMS).
Moc Interview Practise + Real time Test

Our Student Placed in...

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Brings out the best in an employee. Provides multiple opportunities and avenues to excel. The management has been supportive of new ideas therein enabling a diverse learning curve. The overall culture of the organization is grounded and the fostering in a pleasant manner. The people in charge have set the standards high by practicing a hands on approach. Highly recommended for budding professionals to explore DV Analytics as a career option.

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