Data Analytics (Beginner to Advanced) - Powered by SkillCamper

(Introduction to Data Analytics) Week 1: Introduction to Data Analytics What is Data Analytics? Applications across industries Data Analytics lifecycle ( Data Types and Data Collection) Structured vs Unstructured Data Data collection tools and APIs (Data Cleaning and Preparation) Handling missing data Basic preprocessing techniques (Normalization, Encoding) (Exploratory Data Analysis) Data visualization basics using Excel and Google Sheets Summary statistics, correlation Week 2 : Introduction to Python for Data Science ( Python Programming Basics) Data types, variables, loops, conditionals Functions and Libraries Defining functions, importing libraries Week 3 : (Working with NumPy and Pandas) Arrays, DataFrames, data manipulation Basic Visualization Using Matplotlib and Seaborn Week 4 : (Comprehensive Data Science Bootcamp) Python and Data Wrangling SQL Queries and Joins Supervised and Unsupervised ML Neural Networks with Keras (intro level) Capstone: Real-world data science problem Statistics and Probability for Data Science Week 5 : (Statistics) Descriptive and Inferential Statistics Probability Distributions Hypothesis Testing Bayesian Statistics Week 6: (Data Visualization with Tableau and Power BI) Dashboard Design Visual Best Practices Connecting Data Sources Interactivity and DAX functions Time Series Analysis and Forecasting Time Series Components ARIMA/SARIMA Models Facebook Prophet Forecast Accuracy Metrics Week 7 :. (Data Engineering Fundamentals) Data Pipelines & ETL Data Warehousing Concepts Airflow and Orchestration Working with SQL and NoSQL Week 8 : (AI for Business Leaders (Additional for Working professionals) Introduction to AI Strategy Identifying AI Opportunities ROI Measurement Legal, Ethical & Social Issue