Becoming a Data Wizard From Basics to Brilliance

Learn sql syntax and how to retrieve data from relational databases using MySQL, while understanding entity integrity, domain integrity, referential integrity, normalization, and installing MySQL for hands-on practice.

Explore rdbms fundamentals with a focus on data integrity and database normalization, including ddl and dml commands, constraints, and referential integrity.

Explore data definition language (ddl) and data control language (dcl) to shape database schemas, enforce constraints, and manage create, alter, drop, and rename operations with dml workflows, normalization, referential integrity.

Master data manipulation language (DML) concepts for managing and retrieving data with select, insert, update, and delete, including declarative versus procedural approaches and practical where conditions.

Master transaction control language with commits, rollbacks, and savepoints, then learn data control language for grants and revokes, and study domain and integrity constraints like not null and check.

Learn how primary keys and foreign keys enforce data integrity, with composite keys and constraints, and explore union, union all, and intersect.

Explore sql filtering with the where clause and operators such as and, or, not, in, between, exists, and null, then use having, group by, and order by for aggregations.

Group data effectively using group by and order by, with having, limit, and offset, while applying aggregation functions like count, min, max, and avg, and filtering with like.

Master SQL joins by using inner, left, right, full outer, and cross joins to combine tables, handle nulls, and apply aliases for clearer queries.

Becoming a data wizard explores sql rank functions, including rank, dense_rank, and row_number, with over and partition by power, plus how views and triggers organize and store queries for analysis.

Explore SQL triggers and stored procedures, including before and after events, row-level and statement-level triggers, salary changes, subqueries, nested queries, and index creation.

Explore the IMDb movie reviews dataset through a capstone SQL project, connecting SQLite databases, performing joins, nested queries, and analytics on directors, actors, genres, and yearly trends.

Explore descriptive statistics, data types including nominal, ordinal, and binary, and measures of central tendency and dispersion, including mean, median, mode, range, variance, and standard deviation for business analytics.

Explore the central limit theorem, sample means, and Gaussian distributions from population data, with Python in Google Colab using lab notebooks.

Explore distributions to link uniform and Gaussian patterns with z-score, p-value, and hypothesis testing. Learn data cleaning, preprocessing, correlations, and exploratory data analysis basics, including covariance, heatmaps, and confidence intervals.

Master pdf and cdf concepts, histograms, box plots, and outlier detection; learn univariate, bivariate, and multivariate analysis, correlation, imputation, and hypothesis testing.

Learn time series forecasting fundamentals, including stationary and non-stationary data, trend and seasonality, rolling statistics, transformations, and arima models for sales and price prediction.

Explore probability theory and statistics fundamentals, including the central limit theorem, central tendencies, Gaussian and other distributions, hypothesis testing, p-values, outliers, and variance, with time series and ARIMA insights.

Becoming a data wizard: capstone analyzes UK road accidents between 2005 and 2014 using time-series and exploratory data analysis, leveraging Kaggle datasets and cleaning to model trends with highway authorities.

Forecast uk road accidents using time series models such as lstm, neural networks, arima, and prophet, with data preprocessing, missing value handling, and model evaluation for two-year projections.

Learn how to navigate Excel, manage workbooks, and apply formatting, formulas, and charts. Explore data handling from CSV files to charts and visualizations, with tips on cells, axes, and formatting.

Explore how to use Excel formulas to analyze sales data with count, max, min, sum, average, and product across cell ranges, using practical examples.

Learn to create and format tables, apply pivot tables, and analyze data with patterns, sequences, filtering, and styling in Excel.

Explore Excel lookup and reference formulae, mastering VLOOKUP, LOOKUP, INDEX, and MATCH, with offsets, INDIRECT, and logical operators such as ISBLANK and ISERROR to analyze data.

Explore logical formations and lookup in Excel, mastering and, or, and not operators, truth tables, and functions like if and iferror to evaluate conditions.

Explore Excel text formulas like left, right, mid, len, lower, upper, find, replace, and substitute, and statistical functions such as count, average, average ifs, count ifs, max, min, and rank.

Explore Excel date and time formulae, including date serial numbers, today and now, date value, weekday, networkdays, and adding or subtracting days, hours, and minutes.

Learn practical Excel sorting and filtering to organize data, summarize with pivot tables, remove duplicates, and visualize trends with multiple chart types and dynamic target lines.

Customize the Excel ribbon and enable the developer tab to add form controls and build dynamic charts with example data and revenue trends.

Derive insights with pivot tables by learning how to create, filter, and summarize data, visualize revenue by country and product, and build interactive charts and dashboards.

Install Power BI Desktop, publish dashboards, and connect to various data sources to visualize revenue, sales, and budgets using visuals like column charts, stacked bars, donuts, and ribbons.

Explore Power BI maps to analyze sales by region and state with map plots and bubble charts, using tooltips and legend-driven colors.

Build and analyze Power BI tables and matrix visuals, apply filters and formatting, drill down through hierarchies, and create KPI dashboards with data cards, charts, and subtotals.

Explore building and customizing Power BI slicers, including region and city hierarchies, single or multiple select, and date or numeric sliders to drive interactive dashboards.

Master Power Query basics to transform raw data into optimized tables for Power BI dashboards, using joins, merges, appends, and common transformations like group by and split column.

Learn to harness Power Query by appending and merging datasets, transforming columns, and applying filters, sorts, pivots, and statistics to master practical data preparation tasks.

Explore Power Query operations to transform data by adding columns, extracting year, month, and day from dates, and building conditional, index, and custom columns.

Create a sample data dashboard featuring pipe and donut charts, stacked bars, kpi cards, and slicers to explore department, salary, and relationships between tables through data modeling.

Learn how to build production and sales dashboards from real datasets, perform data transformation and modeling, and analyze forecasting accuracy to generate actionable insights and recommendations.