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

SQL, Bigdata and Scala Statistical Programming:

Advanced Program in Data Analytics (APDA)

The Advanced Program in Data Analytics (APDA) delivers comprehensive training in data analytics with a focus on practical industry applications. This program provides a holistic skill set essential for addressing industry demands, emphasizing DBMS programming, data analysis, advanced visualization, storytelling dashboard creation, and advanced analytics for predictive model and strategic action building.

Course Curriculum

What you will learn in (APIDS)?

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

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
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
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

  • 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.
  • 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.
  • 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.
  • 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.

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    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 needs?

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

    Training Supports & Benefits

    • Learn from the World’s Best Faculty & Industry Experts
    • Learn with fun Hands-on Exercises & Assignments
    • Participate in Hackathons & Group Activities
    • Dedicated Faculty
    • 9AM to 6 PM Support
    • Participate in Hackathons & Group Activities
    • Resume Building & Mock Interview Prep​aration.
    • We offer personalized access to our Learning Management System (LMS).
    • Moc Interview Practise + Real time Test

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      • Reflecting on my time at DV Analytics Training Institute, it was a truly quality and interesting experience. The curriculum was enriching, and the engaging learning environment made every aspect of the training enjoyable. Grateful for the valuable skills gained during this memorable journey

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