Become a Certified Data Scientist with Keyperformix learnings

Introduction to Data Science: Overview of data science and its applications Data science workflow and methodology Types of data and data sources Role of a data scientist Python Programming for Data Science: Python fundamentals (data types, control flow, functions) Introduction to essential libraries (NumPy, Pandas) Data manipulation and cleaning with Pandas Data visualization with Matplotlib and Seaborn

Statistical Concepts: Descriptive statistics (mean, median, standard deviation) Probability and distributions Hypothesis testing Correlation and regression analysis Exploratory Data Analysis (EDA): Data profiling and visualization Identifying patterns and anomalies Feature engineering and selection Data storytelling and presentation

Introduction to Machine Learning: Types of machine learning (supervised, unsupervised, reinforcement) Machine learning workflow Model evaluation metrics Bias-variance tradeoff Supervised Learning: Linear regression and logistic regression Decision trees and random forests Support vector machines (SVM) K-nearest neighbors (KNN) Unsupervised Learning: Clustering algorithms (K-means, hierarchical clustering) Dimensionality reduction (PCA, t-SNE) Association rule mining.

Model Evaluation and Selection: Cross-validation techniques Hyperparameter tuning Ensemble methods (bagging, boosting) Feature Engineering and Selection: Techniques for creating new features. Methods for selecting the most relevant features. Dealing with imbalanced datasets.