توضیحات
تخفیف 15% ثبت نام زودهنگام تا تاریخ 9 مهرماه میباشد.
• Introduction to Python and applications…. • Python editors and Installing…. • Data Structures in Python… • Lists in Python…. • Arrays in Python…. • Conditional structures and loops in Python…. • Define Functions… • What is Dictionary?… • Object Oriented Programming .… • Various types of Errors… • Basic Optimization Problems Concept and its Implementation in Python
What is Data Science?…. Why Data Science? …. Math and Statistics in Data Science…. Data Science Instruments…. Data Science References…. Why Programming?…. World of Machine Learning…. Applications of Data Science…. Data Science Job Opportunity….
Crisp DM Concepts (Essential for a Data Mining Project): 1. Business Understanding 2. Data Understanding 3. Data Preparation 4. Modeling phase 5. Evaluation phase 6. Deployment phase
Introduction to Data Science modules… Work with Numpy library… Introduction to Datasets… Work with Pandas library… Data Visualization Concepts and Implementation in Python…
Introduction to R and Rstudio R Applications and Sources… R Data Types, Arithmetic & Logical Operators… R Matrix Tutorial… Factor in R: Categorical & Continuous Variables… R Data Frame… Lists in R… Functions in R Programming… Conditional Structures in R… Loops in R…
Fundamental Probability Concepts includes: Principles of Probability, Conditional Probability and independence, Random Variables etc…. Probability Advanced Concepts includes: Co-Distribution, Expected Value, Central Limit Theorem etc…. Basic Statistics and Random Sampling… MLE, MSE Concepts… Confidence Interval Concept… Hypothesis Tests Concepts… What is P-value?… Statistics and Linear Algebra Implementation in Python… Statistics in R…
Data Pre-Processing Concept… Labeled and Unlabeled Data… Work with Numpy and Pandas libraries (Advanced)… Work with Scikitlearn and Scipy Libraries… Data Analysis with Python… Work with Seaborn library… Overfitting and Underfitting Concepts…
Import Data into R… How to Replace Missing Values in R… Data Visualization in R… Statistical Analysis in R…
Introduction to Supervised Learning… Cross Validation Concept… Classification Algorithms: KNN, Decision Tree… Regression Concepts: Linear Regression, Polynomial Regression, Ridge…
Introduction to Unsupervised Learning… Clustering Algorithms: K-means, Hierarchical…
