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Module 1. Fundamentals of Data Science[5 Hours]
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Module 2. Statistical Foundations for Data Science
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A) Descriptive Statistics[15 Hours]
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B) Probability and Probability Distributions[15 Hours]
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C) Statistical Computing Using R Software[10 Hours]
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D) Statistical Computing Using Python[10 Hours]
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Module 3. Data Science Using R[40 Hours]
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Module 4. Python for Data Science[40 Hours]
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Module 5. Data Analysis Using Advanced Excel and Power Query[25 Hours]
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Module 6. Data Visualization Using Excel and Google Data Studio (Self-paced Learning)
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Module 1. Inferential Statistics (Using R and Python)[20 Hours]
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Module 2. Data Science Using Python[25 Hours]
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Module 3. Machine Learning (Using Python and R)[60 Hours]
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Module 4. Matrix Algebra (Using Python)[5 Hours]
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Module 5. Pre-Calculus (Using Python)[10 Hours]
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Module 6. SQL for Data Science (MySQL/Microsoft SQL Server)[30 Hours]
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Module 7. Data Visualization and Storytelling[10 Hours]
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A) Principles of Data Visualization[10 Hours]
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B) Data Visualization Using R[5 Hours]
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C) Data Visualization Using Python[10 Hours]
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D) Data Visualization and Modelling Using Power BI[30 Hours]
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E) Data Visualization Using Tableau[25Hours]
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Module 8. Case Studies/Mini Projects[30 Hours]
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Module 9. Interview Preparation
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Module 1. Linear Algebra (Using Python)[15]
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Module 2. Multivariate Calculus and Optimization Techniques (Using Python)[15 Hours]
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Module 3. Advanced Topics in Machine Learning (Using Python)[20 Hours]
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Module 4. Deep Learning and Computer Vision (Using Python)[50 Hours]
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Module 5. Natural Language Processing (Using Python)[20 Hours]
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Module 6. Time Series Analysis and Business Forecasting (Using Python)[ 15 Hours]
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Module 7. Model Deployment[10 Hours]
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Module 8. Case Studies/Mini Projects[30 Hours]
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Module 9. Research Papers Reading[5 Hours]
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Module 9. Research Papers Reading
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Module 10. Web Scrapping (Self-paced Learning)
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A) Principles of Data Visualization[10 Hours]
A) Principles of Data Visualization[10 Hours]
C) Data Visualization Using Python[10 Hours]
C) Data Visualization Using Python[10 Hours]