Data Analysis and Business Intelligence with Python & SQL

Explore SQL syntax and data retrieval from relational databases, learn about entity, domain, and referential integrity, normalization, and how to install or download MySQL for hands-on practice.

Explore data definition language concepts, constraints, and referential integrity, learn how normalization and one-to-many relationships shape database design, and apply DDL commands like create, alter, drop, and rename.

Explore data definition language (DDL) concepts in SQL, including creating and altering tables, constraints, primary keys, data types, and referential integrity. Learn practical basics of relational design.

Master data manipulation language concepts, including declarative and procedural approaches, and use select, insert, update, and delete commands to manage and retrieve data from a database.

Explore data control languages and domain constraints, detailing commit, rollback, savepoint, and grant or revoke permissions, plus domain, key, and check constraints to preserve data integrity.

Explore primary keys and foreign keys, including composite keys, and learn how to join tables, enforce constraints, and use set operators such as union, intersect, truncate, and delete in SQL.

This lecture explains filtering data with the SQL where clause using operators like =, >, IN, BETWEEN, and logical and/or, and contrasts with having, group by, and aggregation.

Explore grouping data with group by and order by, using aggregation functions to compute counts, minimum, maximum, and average salaries across departments, with having, limit, and grouping sets.

Master joining multiple tables in data analysis and business intelligence with python & sql, using inner, left, right, full outer, and cross joins, on clauses, and aliases.

Explore sql rank functions, including rank, dense_rank, and row_number, and how partition by and order by affect rankings. Learn to create and manage views and briefly cover triggers.

Explore sql triggers and stored procedures to automate queries, log salary changes with triggers, and manage before or after events on tables; learn subqueries, indexing basics, and procedural reuse.

Explore the capstone project on data analytics of IMDB movie reviews using SQL and Python, building complex queries to analyze directors, actors, and gender trends.

Explore core Python data structures: lists, tuples, dictionaries, and sets, and master string manipulations, including indexing, slicing, searching, and tokenization for data analysis tasks.

Explore python functions, including lambda expressions, map, filter, and reduce, and learn definition, arguments, and scope, enabling efficient data cleaning, preprocessing, and recursive workflows.

Learn Python libraries, perform exploratory data analysis and descriptive analysis with pandas and seaborn, and build a logistic regression model on a Kaggle football dataset, evaluating with AUC, precision, recall.

Install Power BI Desktop, connect to diverse data sources, and build interactive dashboards with visualizations such as column chart, donut chart, and ribbon chart for end-user insights.

Explore map plots in power bi to analyze region-wise and center-wise sales using bubble charts, tooltips, and formatting options across australia and india datasets.

Learn to build and format Power BI tables and matrix visuals to summarize orders data with sales, profit, and quantity, using filters, aggregations, drill-downs, and conditional formatting.

Learn to build and tailor Power BI slicers by region, city, and date hierarchies, including single or multiple select and orientation options, to enhance dashboards.

Explore how Power Query transforms raw data into cleaned, joined, and shape-friendly tables, then load, apply transformations, and prepare data sources for Power BI dashboards.

Explore Power Query operations to clean and combine data sets, including append and merge, sort and filter, transform columns, and perform group by, pivot, and statistical calculations for data analysis.

Explore Power Query operations to transform datasets by adding columns, extracting date components, and creating conditional and custom columns. Learn to clean, format, and analyze data with practical steps.

Build a super store sales data analysis dashboard with interactive visuals such as pipe plots and donut charts, kpi insights, department filters, and robust data relationships across tables.

Analyze Barbie's company sales and production data through forecasting, data transformation, and modeling to build insightful dashboards with KPI, data dictionaries, and actionable recommendations.