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Data Analytics Training & Placement

Duration : 3 to 4 Months

Course Fees : Negotiable After demo/Career Counselling

Real-time Project : 5 to 7

Mode of Class : Classroom / Online Available

Trainer Experience : 15 to 30 + ( IN IT )

Training videos , Materials & One 2 One Support Free

👉 Earn 5LPA to 60LPA Salary

Data Analytics is the process of analyzing raw data to uncover patterns, trends, and insights that aid in decision-making. It includes techniques like descriptive, diagnostic, predictive, and prescriptive analytics, using tools such as Excel, SQL, Power BI, and Python. It is widely applied in business, healthcare, finance, and marketing to improve efficiency and strategy.
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Data Analytics Syllabus

Features of Learning Data Analytics:

  • Hands-on data handling & visualization
  • Mastering tools like Excel, SQL, Power BI, Python
  • Understanding data-driven decision-making
  • Learning predictive & prescriptive analytics
  • Real-world business applications

Benefits of Learning Data Analytics:

  • High-demand career opportunities
  • Competitive salary packages
  • Improved problem-solving skills
  • Informed decision-making in business
  • Versatility across industries (IT, finance, marketing, healthcare)

Course content

  1. Overview of AI: History, Definitions, and Applications
  2. AI vs. Machine Learning vs. Deep Learning
  3. Types of AI: Narrow AI, General AI, and Superintelligent AI
  4. Ethics and Societal Impact of AI
  5. Real-world Applications of AI
  1. Introduction to Python: Basics, Syntax, and Environment Setup
  2. Data Structures in Python: Lists, Tuples, Dictionaries, and Sets
  3. Control Flow in Python: Conditional Statements and Loops
  4. Functions and Modules in Python    
  5. Working with Libraries: NumPy and Pandas
  1. Data Manipulation with Pandas
  2. Data Visualization with Matplotlib and Seaborn
  3. Introduction to Scikit-Learn: Basic Functions and Datasets
  4. Hands-on Session: Implementing Simple Machine Learning Models in Python
  5. Review and Q&A
  1. What is Machine Learning?
  2. Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
  3. Key Concepts in Machine Learning: Data, Features, Labels, Training, and Testing
  4. Overview of Machine Learning Algorithms
  5. Linear Regression: Concepts and Applications
  1. Polynomial Regression
  2. Evaluation Metrics for Regression: MAE, MSE, R-Squared
  3. Hands-on Session: Implementing Linear and Polynomial Regression in Python
  4. Introduction to Classification
  5. Logistic Regression
  1. Support Vector Machines (SVM)
  2. Decision Trees
  3. Random Forests
  4. Evaluation Metrics for Classification: Accuracy, Precision, Recall, F1-Score, Confusion Matrix
  5. Hands-on Session: Implementing Classification Algorithms in Python
  1. Introduction to Clustering
  2. K-Means Clustering
  3. Hierarchical Clustering
  4. DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
  5. Hands-on Session: Implementing Clustering Algorithms in Python
  1. Current Trends in AI
  2. Future Directions in AI
  3. Course Review and Recap
  4. Final Project: Guidelines and Expectations
  5. Final Project Work Session
  6. Final Project Presentation and Discussion
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Real-Time Projects

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