About This Course
What You Will Learn
Course Curriculum
Module 1: Introduction to Data Analytics, Data Science and Artificial Intelligence
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 2: Understanding Data Types, Sources and Analytics Workflows
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 3: Advanced Excel Formulas and Functions
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 4: Data Cleaning and Validation Using Excel
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 5: Pivot Tables, Pivot Charts and Interactive Excel Dashboards
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 6: Introduction to Databases and SQL
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 7: SQL Queries, Filtering, Sorting and Aggregate Functions
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 8: SQL Joins, Subqueries, Views and Window Functions
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 9: Database Design and Business Data Analysis
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 10: Introduction to Python Programming
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 11: Variables, Data Types, Conditions, Loops and Functions
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 12: Python Collections and File Handling
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 13: Introduction to NumPy for Numerical Analysis
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 14: Data Manipulation Using Pandas
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 15: Handling Missing, Duplicate and Incorrect Data
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 16: Exploratory Data Analysis
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 17: Data Visualization Using Matplotlib and Seaborn
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 18: Statistics and Probability for Data Analytics
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 19: Correlation, Regression and Hypothesis Testing
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 20: Introduction to Power BI and Data Importing
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 21: Power Query for Data Cleaning and Transformation
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 22: Data Modelling and Relationships in Power BI
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 23: DAX Formulas, Measures and Calculated Columns
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 24: Interactive Dashboards and Business Reports
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 25: Introduction to Machine Learning
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 26: Supervised and Unsupervised Learning Fundamentals
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 27: Regression, Classification and Clustering Models
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 28: Model Training, Testing and Performance Evaluation
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 29: Introduction to Artificial Intelligence and Generative AI
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 30: Prompt Engineering and Responsible AI Usage
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 31: Using AI Tools for Data Cleaning, Analysis and Reporting
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 32: Introduction to APIs and AI Service Integration
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 33: Git, GitHub and Data Portfolio Development
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 34: Business Case Studies and Analytical Problem-Solving
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 35: Résumé, LinkedIn and Interview Preparation
Module lessons, practice tasks, and review sessions are covered during this stage.
Module 36: Final Real-World Data Analytics and AI Project
Module lessons, practice tasks, and review sessions are covered during this stage.
Projects You Will Build
Sales Performance Dashboard Using Excel
Practical portfolio project
Employee Attendance and Payroll Analysis
Practical portfolio project
Customer Sales Analysis Using SQL
Practical portfolio project
E-Commerce Database Analysis
Practical portfolio project
</>Technologies You Will Learn
Eligibility
Open to students, graduates, job seekers, working professionals, business owners, freelancers and entrepreneurs. Applicants from any academic background may join. Basic computer knowledge is sufficient, and no previous programming, statistics or data analytics experience is required.
Internship Availability
Eligible students can participate in an optional project-based Data Analytics and AI internship after completing the required course modules and assessments. Internship availability is subject to project requirements, student performance and available positions.
Certificate Information
Students who complete the required modules, assignments, assessments and final project will receive a Learns-Way Academy Certificate of Completion in Data Analytics and AI. Each certificate will contain a unique verification number that can be checked through the website.
Placement Assistance
Placement assistance includes résumé preparation, LinkedIn profile guidance, GitHub portfolio development, Power BI portfolio preparation, SQL and Python practice, aptitude preparation, mock interviews and suitable hiring-partner introductions. Employment is not guaranteed and depends on the student’s skills, project performance and employer requirements.