Business Analytics specalization in PowerBI
This course provides an in-depth exploration of business analytics using Power BI. Students will learn how to analyze and visualize data, use DAX for calculations, and create interactive dashboards. The course covers data preprocessing, model evaluation, integration with Excel, and real-time data analysis, culminating in hands-on Power BI projects for business applications.
- Bestseller
Mentor Sync Program from Navikshaa
- Upcoming batch 1st Feb
- English
Business Analytics specalization in PowerBI
A course by Karan
This course provides an in-depth exploration of business analytics using Power BI. Students will …
- 1,429
- 97%
₹17,990
What you will learn
- How to harness data for smarter business decisions.
- The basics of transforming raw data into insightful analysis.
- How to use Power BI to streamline data visualization.
- Secrets to effectively clean and prepare your data.
- Master Power BI’s interface for seamless data exploration.
- Techniques to integrate and transform data from multiple sources.
- Advanced data transformation skills to boost analysis efficiency.
- Understanding and building strong data relationships.
- Building robust data models for impactful insights.
- Introduction to powerful DAX formulas for custom calculations.
- How to leverage conditional logic to enhance your analysis.
- Using advanced DAX functions to calculate time-based metrics.
- The art of selecting the right chart type for your data.
- How to build interactive dashboards that tell a story.
- Designing visually compelling reports with custom features.
- Creating customized visuals to stand out in reports.
- How to publish and share your insights on the Power BI service.
- Managing data refresh and ensuring real-time reporting.
- Implementing security features to protect sensitive data.
The course includes :
- 45+ hours live Sessions
- 25 Quizes
- 4 Assessments
- Query Resolution
- 24/7 Professional Support
- No Resume Building Sessions
- No Mock Interviews
- Updated Recorded sessions
- MNC Simulated Project
- Training Completion Certificate
- Letter of Recommendation
- Wipro Credential Certificate
- No Placement Assistance
Course Content
- 45+ Hours Live Sessions
- 29 Quizzes
- 4 Assessments
Day 1: Introduction to Artificial Intelligence
- Overview of AI, its history, and evolution
- Types of AI: Narrow AI vs. General AI
- Applications of AI in business and technology
- Overview of AI, its history, and evolution
- Quiz
Day 2: Core Concepts of Machine Learning
- Introduction to machine learning
- ML vs. AI vs. Deep Learning
- Real-world examples of ML applications
- Quiz
Day 3: Machine Learning Basics and Tools
- Overview of common machine learning libraries: Scikit-learn, TensorFlow, Keras
- Data handling with Pandas and NumPy
- Basic data preprocessing techniques
- Quiz
Day 4: Supervised Learning – Regression Techniques
- Linear regression and multiple linear regression
- Hands-on with real-world datasets (e.g., sales prediction)
- Model evaluation: Mean Squared Error, R²
- Quiz
Day 5: Supervised Learning – Classification Algorithms
- Introduction to classification: Logistic Regression, K-Nearest Neighbors (KNN), Decision Trees
- Hands-on with classification tasks (e.g., binary classification)
- Quiz
Assessment 1
- Assessment 1
Day 6: Unsupervised Learning – Clustering
- Introduction to unsupervised learning and clustering
- K-Means Clustering, DBSCAN, Hierarchical Clustering
- Real-world applications: Customer segmentation
- Quiz
Day 7: Model Evaluation and Hyperparameter Tuning
- Evaluating machine learning models
- Cross-validation and overfitting/underfitting
- Hyperparameter tuning using Grid Search and Random Search
- Quiz
Day 8: Support Vector Machines (SVM)
- Introduction to Support Vector Machines
- SVM for classification and regression
- Real-world applications: Text classification, image classification
- Quiz
Day 9: Ensemble Learning Techniques
- Overview of ensemble methods: Random Forest, Bagging
- Hands-on with ensemble learning for improving model accuracy
- Quiz
Day 10: Advanced Machine Learning – Dimensionality Reduction
- Principal Component Analysis (PCA)
- Feature selection and reduction
- Applications: Reducing dimensionality in large datasets (e.g., image or gene data)
- Quiz
Assessment 2
- Assessment 1
Day 11: Introduction to Deep Learning
- What is deep learning? Differences from traditional ML
- Neural networks: Architecture and learning process
- Activation functions, loss functions, and backpropagation
- Quiz
Day 12: Convolutional Neural Networks (CNN)
- Introduction to CNNs and their architecture
- Applications in image classification, object detection
- Hands-on project: Image recognition using CNN
- Quiz
Day 13: Recurrent Neural Networks (RNN)
- Introduction to RNNs and LSTMs (Long Short-Term Memory)
- Applications in time series forecasting, speech recognition
- Hands-on project: Sentiment analysis with RNN
- Quiz
Day 14: Deep Learning Model Evaluation
- Evaluating deep learning models: Overfitting, underfitting, and tuning
- Hyperparameter optimization for deep learning models
- Advanced techniques: Dropout, Batch Normalization, Early Stopping
- Quiz
Day 15: Transfer Learning and Pre-trained Models
- Understanding transfer learning and fine-tuning pre-trained models
- Practical applications using models like ResNet, Inception, and VGG16
- Hands-on: Transfer learning for image classification
- Quiz
Assessment 3
- Assessment 3
Day 16: Natural Language Processing (NLP) Fundamentals
- Introduction to NLP and text preprocessing
- Tokenization, stemming, lemmatization, stop words removal
- Hands-on with Python libraries: NLTK, spaCy
- Quiz
Day 17: Text Classification and Sentiment Analysis
- Introduction to text classification techniques
- Sentiment analysis using deep learning (e.g., LSTM, BERT)
- Hands-on with real-world datasets (social media posts, reviews)
- Quiz
Day 18: Sequence Modeling with RNN and LSTM
- Advanced sequence modeling using RNN and LSTM
- Applications: Time series prediction, stock market forecasting, NLP tasks
- Hands-on project: Stock price prediction with LSTM
- Quiz
Day 19: Reinforcement Learning Basics
- Introduction to reinforcement learning (RL)
- Key concepts: Agents, environments, rewards, policies
- Hands-on with simple RL algorithms (Q-learning)
- Quiz
Day 20: Advanced Deep Learning: GANs (Generative Adversarial Networks)
- Introduction to GANs and how they work
- Applications: Image generation, style transfer, and data augmentation
- Hands-on with GAN-based projects
- Quiz
Assessment 4
- Assessment 4
Day 21: AI Model Deployment
- Introduction to model deployment concepts
- Packaging models with Docker, deploying on cloud platforms (AWS, GCP, Azure)
- API integration for model serving
- Quiz
Day 22: AI for Business Applications
- AI in business: Automation, recommendation systems, and predictive analytics
- Hands-on: Building a recommendation engine using collaborative filtering
- Applications in e-commerce, healthcare, and finance
- Quiz
Day 23: AI in Healthcare
- AI applications in healthcare: Diagnostics, personalized treatment, medical imaging
- Case studies: AI in radiology, drug discovery, and patient monitoring
- Hands-on: Building a simple AI model for medical diagnosis
- Quiz
Day 24: AI in Finance and Risk Management
- AI for fraud detection, credit scoring, and algorithmic trading
- Case studies: AI in financial institutions
- Hands-on: Building a fraud detection model
- Quiz
Day 25: AI in Manufacturing and Automation
- AI for predictive maintenance, robotics, supply chain optimization
- Case studies: Smart factories, automation solutions
- Hands-on: Building an AI-based predictive maintenance system
- Quiz
Day 26: Introduction to AI on the Cloud
- Cloud AI services: AWS, Google Cloud AI, Azure Machine Learning
- Hands-on: Using Google Cloud AI for model deployment
- Benefits and challenges of cloud-based AI solutions
- Quiz
Day 27: AI for Edge Computing
- Introduction to edge AI and its importance for real-time processing
- Case studies: AI in IoT devices, autonomous vehicles
- Hands-on: Deploying a model on a Raspberry Pi
- Quiz
Day 28: AI Ethics and Responsible AI
- Ethical concerns in AI: Bias, fairness, and transparency
- AI governance and regulation in MNCs
- Building ethical AI systems for business applications
- Quiz
Day 29: Capstone Project: Building an Enterprise AI Solution
- Work on a real-world AI project
- Apply concepts learned in the course to solve a business problem
- Quiz