名無しのコスメ好きさん on LIPS 「Data Analytics Syllabus ..」

Data Analytics Syllabus Module 1: Introduction to Data Analytics What is data analytics? Role of a data analyst Types of analytics: Descriptive, Diagnostic, Predictive, Prescriptive Data-driven decision making Module 2: Data Collection & Data Sources Types of data: Structured vs. unstructured Data sources: Databases, APIs, web scraping, surveys Data warehousing and data lakes Introduction to ETL (Extract, Transform, Load) processes Module 3: Data Cleaning & Preprocessing Importance of clean data Handling missing or inconsistent data Data transformation and normalization Tools: Excel, Python (Pandas), R Module 4: Exploratory Data Analysis (EDA) Descriptive statistics: Mean, median, mode, standard deviation Data profiling Identifying trends and patterns Visualization tools: Matplotlib, Seaborn, Tableau, Power BI Module 5: Data Visualization & Communication Best practices for visual storytelling Creating reports and dashboards Tools: Tableau, Power BI, Google Data Studio Module 6: Statistical Analysis Probability and distributions Hypothesis testing Correlation vs. causation Linear and logistic regression Module 7: SQL for Data Analytics Basics of databases Writing SQL queries Joins, filters, and aggregations Data manipulation and extraction Basic syntax and data structures Data analysis libraries (Pandas, NumPy, Matplotlib) Reading, analysis, and visualization of data Introduction to scripting and automation Module 9: Predictive Analytics (Advanced/Optional) Introduction to machine learning Supervised and unsupervised learning Model construction and assessment Algorithms: Decision trees, clustering, regression https://www.sevenmentor.com/data-analytics-courses-in-pune.php https://www.iteducationcentre.com/data-analytics-courses-in-pune.php