Core Competencies in Data Science, Analytics & Machine Learning
SQL, Bigdata and Scala Statistical Programming:
- Database Programming: SQL, Bigdata and Scala
- Data Science 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 Data Science and AI delivers end-to-end training across the data-to-AI lifecycle. The curriculum covers database programming, statistical analysis, reporting, and data visualization, enabling you to work with large datasets and communicate insights effectively. You will learn SQL for data querying, use Python, R, and SAS for analytics, and build dashboards and reports using Excel, Tableau, Power BI, and VBA.
The program also includes data mining, advanced analytics, and machine learning, covering supervised and unsupervised learning, model evaluation, and deployment. You will study deep learning with Python, TensorFlow, and Keras, along with an introduction to reinforcement learning and generative AI. The curriculum further introduces cloud computing for scaling solutions and automating workflows, preparing you to build real-world, AI-driven business applications.
What will you learn in Data Science and AI?
The first step is to learn DBMS programming skills that help in managing complex data models from various information sources. These skills enable you to combine data from multiple files into a structured format for 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
The second step is to develop descriptive analytics by transforming raw data into meaningful business KPIs and presenting insights through storytelling dashboards using advanced reporting and visualization tools.
- Excel Reporting and Dashboard
- Tableau Reporting and Visualizations
- Power BI Reporting and Visualizations
- Qlikview Reporting and Visualizations
The third step focuses on exploratory data analysis (EDA) and advanced analytics techniques to identify patterns and trends in historical data and to predict future outcomes.
- Advanced Analytics in Excel
- Advanced Analytics in SAS
- Advanced Analytics in Python
- Advanced Analytics in R
The next step focuses on automating decision-making using supervised and unsupervised machine learning techniques to uncover hidden patterns in data. Learners will also develop AI-centric business applications and use reinforcement learning to build intelligent systems such as recommender engines.
- Machine Learning in Python
- Deep Learning on Python, Keras and Tensorflow
- AI in Python
A learning experience that equips you with responsibility and proficiency in data analysis and visualization:
- Analyze large datasets to identify meaningful patterns, trends, and relationships.
- Clean and preprocess data to ensure quality, accuracy, and suitability for analysis.
- Perform exploratory data analysis (EDA) to gain deeper insights into data behavior and structure.
- Use visualization tools to present findings clearly through charts, graphs, and dashboards.
- Communicate insights effectively to both technical and non-technical stakeholders, enabling informed decision-making.
- Apply machine learning algorithms to develop predictive models and support data-driven decision-making.
- Train models on historical data to recognize patterns and generate predictions or classifications on new data.
- Use techniques including regression, classification, clustering, and deep learning, among others.
- Use generative AI techniques such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) to create new data instances.
- Apply generative AI to tasks including data augmentation, synthetic data generation, and image and text generation.
- Utilize cloud computing platforms such as AWS, Azure, and Google Cloud to deploy machine learning models at scale.
- Use cloud infrastructure and managed services for model hosting, monitoring, and lifecycle management.
- Enable seamless integration with other cloud-based services and support automatic scaling based on workload demand.
- Ensure accessibility, scalability, reliability, and security for deployed analytics and AI models.
Industry-Driven Real-World Data Science Projects ( Put some appropriate pictures)
Become an Industry-Ready Data Scientist
Master DBMS programming, advanced analytics, machine learning, and AI through real-time industry projects. Gain hands-on experience with SQL, Python, SAS, R, Tableau, Power BI, and Generative AI—guided by industry experts.
INDUSTRY REAL-TIME
DATA SCIENCE PROJECTS
Retail Marketing
The retail industry is undergoing rapid transformation driven by digitalization, changing consumer behavior, and data-driven decision-making. Compared to a decade ago, today’s retail market is far more competitive, dynamic, and technology-enabled. With global retail sales valued in the trillions of dollars and continuing to grow, data analytics has become a critical enabler for business success. By leveraging data, retailers can make informed decisions across customer retention, sales forecasting, pricing, and inventory management. Advanced analytics and AI models enable organizations to uncover patterns, predict demand, personalize customer engagement, and optimize operations to drive sustainable growth and profitability.
- AgentifyAI Global include, but are not limited to, the following:
- 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 banking industry is undergoing profound transformation driven by digital innovation, evolving customer expectations, and increasing regulatory and security demands. These changes are not temporary; they reflect a long-term shift toward technology-enabled, data-driven banking. To make informed and strategic business decisions, banks must integrate and analyze data from both structured and unstructured sources. Banking analytics enables institutions to generate valuable insights by collecting, processing, and analyzing large volumes of data, helping them enhance customer experience, manage risk, improve operational efficiency, and drive sustainable growth.
- AgentifyAI Global can help you in the following:
- Customer identification and acquisition
- Portfolio analysis and risk management
- Customer retention
- Credit risk analysis
- Collection analysis
- Marketing analysis
Telecom
The telecommunications industry has undergone dramatic transformation over the past few decades, evolving from traditional voice services to high-speed broadband, satellite internet, and 5G technologies. As competition intensifies and customer expectations continue to rise, telecom companies face growing pressure to deliver superior services while maintaining profitability. In this highly dynamic environment, telecom data analytics has become a critical tool for solving complex business challenges. By applying techniques such as data mining, data manipulation, descriptive analytics, and predictive modeling, organizations can uncover trends, optimize operations, improve customer experience, reduce costs, manage risks, and drive sustainable revenue growth.
Some of the Telecom analytics solutions that AgentifyAI Global offers are:
- 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 has transformed the way businesses and consumers interact, enabling customers to purchase a wide range of products through websites and mobile applications. As online businesses operate in a highly competitive and fast-changing digital environment, data analytics has become essential for understanding customer behavior, predicting market trends, and making informed business decisions. E-commerce analytics provides actionable insights into shopper interactions, purchasing patterns, preferences, and demand dynamics. By applying statistical and machine learning techniques, organizations can anticipate market changes, assess risks, optimize pricing and inventory, enhance customer experience, and drive sustainable growth.
AgentifyAI Global ecommerce solutions consists of:
- Information analysis
- Inventory forecasting
- Customer experience analysis and targeted customer communication
- Fraud prevention
- Marketing analysis
- Price optimization
Healthcare
Healthcare encompasses a wide range of services, including hospitals, medical devices, pharmaceuticals, insurance, and community-based care. In an increasingly complex and data-rich healthcare ecosystem, data analytics has become a powerful enabler for improving outcomes, efficiency, and decision-making. While not all health events can be prevented, analytics allows organizations to anticipate risks, analyze trends, and prepare for future demands. By leveraging healthcare data, organizations can gain actionable insights that support better clinical decisions, optimize operations, improve patient care, manage costs, and drive meaningful business and public health impact.
We at AgentifyAI Global can help your organization with:
- Risk Analysis
- Insurance claim analysis
- Operations analysis
- Patient care analysis
- Performance monitoring
- Operational and interactive dashboards
Our Alumni Working With Industry Leaders
DATA SCIENCE PROJECTS
Become an Industry-Ready Data Scientist
Master DBMS programming, advanced analytics, machine learning, and AI through real-time industry projects. Gain hands-on experience with SQL, Python, SAS, R, Tableau, Power BI, and Generative AI—guided by industry experts.