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Learn Basic Statistics**\n\n- **Probability Distributions:** Continuous and Discrete\n- **Basic Probability:** Independent and Dependent Events, Marginal Probability, Conditional Probability, Joint Probability\n- **Measures of Central Tendency:** Mean, Median, Mode\n- **Variance, Standard Deviation \u0026 Standard Error**\n\n### **3. Learn Exploratory Data Analysis (EDA)**\n\n- Identification of variables and data types\n- Analyzing the basic metrics\n- Non-Graphical \u0026 Graphical Univariate Analysis\n- Bivariate Analysis\n- Variable transformations, Missing value/Outlier Treatment\n- Correlation Analysis/Dimensionality Reduction\n\n### **4. Learn Supervised \u0026 Unsupervised Model**\n\n### **Supervised Models:**\n\n- Linear/Polynomial/Logistic regression\n- Classification trees\n- Ensemble models like Bagging and Random Forest\n- Supervised Vector Machines\n\n### **Unsupervised Models:**\n\n- Clustering\n- Association Rule Learning\n\n### **5. Learn Deep Learning Models**\n\n- **Supervised:** ANN/CNN/RNN\n- **Unsupervised:** SOMs/Boltzmann Machines/AutoEncoders\n\n### **6. Understand Big Data Technologies**\n\n- Big Data Overview and Eco-System\n- Hadoop/NoSQL/Data Lakes\n- TensorFlow/Docker/Kubernetes\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgrand-27-master%2Fdata-science-course","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgrand-27-master%2Fdata-science-course","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgrand-27-master%2Fdata-science-course/lists"}