Understanding Data Analytics: Overview, importance, and applications

Data Types and Sources: Structured vs. unstructured data, data collection methods

Introduction to Data Analysis Tools: Excel, SQL basics

Data Cleaning Techniques: Handling missing values, outliers, and duplicates

Data Transformation: Normalization, standardization, encoding categorical variables

Data Preparation in Python and R: Pandas, DataFrames, data manipulation

EDA Fundamentals: Summary statistics, data visualization basics

Data Visualization Tools: Matplotlib, Seaborn for Python; ggplot2 for R

EDA Techniques: Histograms, box plots, scatter plots, correlation analysis

Probability Distributions: Normal, Binomial, Poisson distributions

Inferential Statistics: Central Limit Theorem, confidence intervals

Hypothesis Testing: Types of tests, p-values, t-tests, ANOVA

Machine Learning Basics: Supervised vs. unsupervised learning, regression vs. classification

Linear Regression: Concepts, assumptions, model evaluation

Hands-on: Implementing Linear Regression in Python/R

Classification Algorithms: Logistic Regression, Decision Trees, Random Forest

Clustering Algorithms: K-Means Clustering, Hierarchical Clustering

Model Evaluation Metrics: Accuracy, precision, recall, F1-score

Introduction to Big Data: Challenges, characteristics, and importance

Handling Big Data: Introduction to Hadoop, MapReduce basics

Spark Fundamentals: RDDs, transformations, actions