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Data Science Syllabus MODULE 1: PYTHON FOR DATA SCIENCE Techedo Technologies 1.1. FUNDAMENTALS OF PYTHON 1.2. DATATYPES IN PYTHON 1.3. OPERATORS IN PYTHON 1.4. INPUT/OUTPUT 1.5. CONTROL STATEMENTS 1.6. NUMPY ARRAYS 1.7. FUNCTIONS IN PYTHON 1.8. MODULES AND PACKAGES 1.9. DATA VISUALIZATION USING MATPLOTLIB MODULE 2: STATISTICAL METHODS 2.1. INTRODUCTION TO STATISTICS 2.2. STATISTICAL TERMS 2.3. MEASURES OF CENTRAL TENDENCY 2.4. PROBABILITY 2.5. MEASURES OF SHAPE 2.6. MEASURES OF DISPERSION OR VARIABILITY 2.7. APPLICATION OF VARIANCE OR STD 2.8. PROBABILITY DISTRIBUTIONS 2.9. HYPOTHESIS TESTING MODULE 3: TABLEAU 3.1. INTRODUCTION TO TABLEAU 3.2. CREATING PIVOT TABLE 3.3. DATA BLENDING 3.4. CROSS-DATABASE JOINS 3.5. CALCULATIONS ON DATA 3.6. DATA VISUALIZATION IN TABLEAU 3.7. DASHBOARD CREATION Techedo Technologies MODULE 4: MACHINE LEARNING IN AI 4.1. EXPLORATORY DATA ANALYSIS (EDA) 4.2. OUTLIERS AND THEIR TREATMENT 4.3. SUPERVISED LEARNING VS UNSUPERVISED LEARNING 4.4. FEATURE EXTRACTION AND CONVERSION 4.5. REGRESSION MODELS 4.6. CLASSIFICATION MODELS 4.7. UNSUPERVISED LEARNING 4.8. ASSOCIATION RULE LEARNING 4.9. MODEL SELECTION