Courses Details

• Business Analytics with Excel
• Data Acquisition and Manipulationusing SQL
• Extract, Transform, Load (ETL)
• Data Visualization using Tableau
• Fundamentals of Python Programming
• Data Analytics with Python
• Essentials of Generative AI, Prompt Engineering & ChatGPT
• Applications of Generative AI in Data Analytics
• Data Ethics
• Capstone Project

Learn about business analytics and its importance in the industry. Perform business analytics tasks using Microsoft Excel and create visual insights using charts and dashboards. Become proficient in business analytics with foundational statistics and techniques—an initial step in the data analytics program.

Learning Outcomes

• Understand the significance of business analytics in the industry
• Master Excel analytics functions and conditional formatting
• Analyze complex data sets using pivot tables and slicers
• Visualize insights using charts and dashboards
• Apply statistical tools in Excel, including moving averages,hypothesis testing, ANOVA, and regression.

Topics Covered

• Introduction to Business Analytics
• Dashboard Creation
• Conditional Formatting and Essential Functions
• Excel in Business Analytics
• Analysis with Pivot Tables
• Power BI

Acquire essential skills to begin working with SQL databases. Learn about fundamental SQL concepts, including statements, conditional queries, commands, joins, subqueries, and diverse functions crucial for effective SQL database management and scalability.

Learning Outcomes

• Grasp database structures and relationships
• Execute stored procedures with ease and understanding
• Utilize standard query tools to execute SQL commands
• Implement conditional queries for dynamic data retrieval
• Master table creation, transactions, and views

Topics Covered

• Fundamental SQL Statements
• Applying Joins and Subqueries
• Filtering Data with Selection Commands
• Creating and Managing Views and Indexes
• Using Aggregate Functions (SUM, AVG, COUNT)
• Working with String, Date, and Time Functions

Embark on a comprehensive exploration of ETL essentials crucial for robust data analysis. Learn about systematically extracting structured/unstructured data, rule formulation, and utilizing diverse ETL tools like Nifi and Talend. Develop repository data loading expertise, distinguish between batch and real-time ETL, and efficiently manage ETL workflows for seamless data analysis.

Learning Outcomes

• Understand the role of ETL in data management and analysis
• Identify and access diverse data sources, including databases and APIs
• Extract structured and unstructured data using various techniques
• Perform data cleansing, validation, normalization, and aggregation to handle quality issues
• Create and apply transformation rules and mappings for different data types
• Load transformed data efficiently into target repositories, optimizing loading for performance
• Optimize ETL processes for enhanced speed and efficiency and learn parallel processing techniques

Topics Covered

• ETL Fundamentals
• Data Source Identification
• Data Extraction Techniques
• Batch and Real-time ETL
• Data Transformation and Mapping
• ETL Tools and Technologies (e.g., Nifi, Talend)

This course thoroughly explores creating impactful visualizations and informative dashboards using Tableau. Discover key data visualization concepts and learn to craft diverse charts that drive better business decisions. Master the construction of interactive dashboards with filters, parameters, and sets and utilize various visualization techniques, including heat maps, treemaps, and Pareto charts.

Learning Outcomes

• Master visualization techniques, including heat maps, treemaps, waterfalls, and Pareto charts
• Understand the significance of metadata and its application in Tableau.
• Manipulate data effectively using filters, parameters, and sets.
• Master specific field types, Tableaugenerated fields, and the creation and utilization of parameters
• Learn to create various charts, interactive dashboards, and captivating story interfaces.
• Gain proficiency in data blending, creating extracts, and organizing and formatting data effectively.
• Master various calculations, including arithmetic, logic, table, and level of detail (LOD).

Topics Covered

• Data Visualization
• Introduction to Tableau and Power BI
• Chart Creation and Data Preparation
• Preparation Techniques
• Filters and Analytics in Tableau and Power BI
• Tableau and Power BI Dashboards
• Tableau and Power BI Workspace and Chart Types

Learn Python programming fundamentals and understand how it can be used in data analytics. Navigate Jupyter installations for hands-on learning and explore key concepts such as data types, loops, object-oriented principles, methods, attributes, and threading.

Learning Outcomes

• Attain proficiency in procedural and object-oriented programming
• Understand Python’s benefits and practical applications
• Master Python data types, operators, and string functions
• Explore Python loop types and variable scope within functions
• Grasp object-oriented programming principles and attributes in Python
• Gain insight into multithreading in Python
• MImplement Python basics, including identifiers, indentations, and comments

Topics Covered

• Programming Fundamentals
• Introduction to Python
• Python Data Types and Operators
• Python Functions
• Threading in Python
• Object-Oriented Programming in Python
• Conditional Statements and Loops in Python

Learn about various Python packages used in data analytics. Explore NumPy and Pandas and learn about the importance of statistics in data analytics. Understand essential concepts like data categorization, data wrangling, and data visualization. By the end of the course, you’ll gain practical skills to perform insightful data analysis, enhancing your ability to make data-driven decisions and solve complex problems efficiently.

Learning Outcomes

• ADevelop proficiency in retrieving and managing data through Application Programming Interfaces (APIs) for analytical purposes.
• Utilize SQL and Python for robust database management, ensuring seamless data handling and analysis.
• Employ sophisticated statistical methodologies for in-depth data analysis and interpretation.
• Analyze and interpret time-based data patterns and trends
• Apply various probability distributions as analytical tools for insightful data exploration and interpretation.
• Get practical experience in testing hypotheses and drawing conclusions from data samples.
• Conduct ANOVA and Chi-Square tests for varied data sets and derive meaningful insights

Topics Covered

• Data Acquisition with APIs
• Correlation and Regression Analysis
• Database Management with SQL and Python
• Statistics Fundamentals
• Probability Distributions
• Hypothesis Testing
• Time Series Analysis

Gain insight into the R Programming language with this introductory course. An essential programming language for data analysis, R Programming ts a fundamental key to becoming a successful Data Science professional. In this course, you will learn how to write R code, learn about data structures in R, and create your own functions. After completion of this course, you will be fully able to begin your first data analysis journey.

Key Learning Objectives

• Learn about key mathematical concepts, variables, strings, vectors, factors, and vector operations
• Gain fundamental knowledge on arrays and matrices, lists, and data frames
• Get understanding of conditions and loops, functions in R, objects, classes, and debugging
• Learn how to accurately read text, CSV and Excel files, and how to write and save data objects in R toa file
• Understand and work on strings and dates in R

Course curriculum

• Lesson Ol R Basics
• Lesson O2 Data Structures in R
• Lesson 03 R Programming Fundamentals
• Lesson 04 Working with Data in R
• Lesson 05 Stings and Dates R

*Data Analytics with R

The next step to becoming a Data Analyst is learning R—the most in demand open source technology. R is a powerful Data Science and analytics language, which has a steep learning curve and a very vibrant community. This is why it is quickly becoming the technology of choice for organizations who are adopting the power of analytics for competitive advantage.

Key Learning Objectives

• Gain a foundational understanding of business analytics Install R, R-studio and workspace setup, and learn about the various R packages
• Master R programming and understand how various statements are executed in R
• Gain an in-depth understanding of data structure used in R and learn how to import / export data in R Define, understand, and use the various apply functions and DPLYR functions Understand and use the various graphics in R for data visualization
• Gain a basic understanding of various statistical concepts
• Understand and use hypothesis testing method to drive business decisions
• Understand and use linear and non-linear regression models, and classification techniques for data analysis

This course offers an in-depth exploration of generative AI models, particularly focusing on ChatGPT. Participants will acquire comprehensive knowledge of foundational generative AI principles, including prompt engineering, explainable AI, conversational AI, ChatGPT, and other extensive language models.

Learning Outcomes

• Understand core principles and variations of generative AI models
• Apply prompt engineering techniques to optimize model performance.
• Deepen your knowledge of ChatGPT’s features, limitations, and operations.
• Explore explainable AI and strategies for ensuring transparency.
• Learn fine-tuning methods to personalize ChatGPT models.
• Explore various applications and contexts where ChatGPT can be utilized effectively
• Evaluate the impact of generative AI across industries and review leading tools.

Topics Covered

• Overview of Generative AI and its Landscape
• Understanding Explainable AI
• Fundamentals of Prompt Engineering
• Designing and Creating Effective Prompts
• Extensive Language Models
• ChatGPT: Applications, Functionality and Fine-tuning Strategies
• Exploring Conversational AI
• Ethical Considerations in Generative AI Models
• Responsible Data Usage and Privacy Measures
• AI Technologies Driving Innovation

This course will uncover the pivotal role of generative AI in data analytics, including its capabilities in generating synthetic data, enhancing visualizations, and finding hidden insights. Harness its potential to optimize ETL processes, improve forecasting accuracy, and tackle integration challenges. Delve into future trends and ethical considerations to comprehensively view generative AI’s impact. The curriculum spans data augmentation, visualization, and forecasting, offering a comprehensive understanding of how generative AI intersects with data analytics.

Learning Outcomes

• Use generative AI to generate synthetic data, enhance privacy, and address data scarcity.
• Create interactive and accessible data visualizations with generative AI tools
• Understand generative AI’s role in modern data analytics.
• Discover patterns, anomalies, and insights through AI-driven data exploration.
• Optimize ETL processes and data pipelines with AI for automated cleaning, intelligent transformation, and real-time analysis.

Topics Covered

• Overview of Generative AI in Data Analytics
• Data Augmentation using Generative AI
• Generative AI for Tailored Data Visualization
• Generative AI in Data Exploration
• Generative AI in Data Modeling and Forecasting
• Challenges in Integrating Generative AI
• Future Directions in Generative AI
• AI-Optimized ETL Processes

Upon completing the DA and GenAI courses, participants embark on a capstone project to apply their newly acquired skills. Guided by mentors, they will apply their skills in Excel, Python, SQL, Tableau, and more to a real-world project by solving data-related challenges. This project marks the culmination of their learning journey and provides an opportunity to showcase their abilities to potential employers in a real-world context.