Advanced Data Analysis Training Service | in Dammam - Riyadh - Jeddah - Makkah

1. Introduction to Advanced Data Analysis

• Data analysis evolution including (descriptive, diagnostic, predictive, prescriptive analytics, AI integration)

• Business analytics framework including (problem definition, data collection, analysis, insights, action, measurement)

• Data-driven decision making including (evidence-based, reducing bias, measuring impact, continuous improvement)

• Analytics tools ecosystem including (Excel advanced features, Python libraries, R statistical packages, Power BI, SQL, Tableau)

• Data quality dimensions per ISO/IEC 25012 including (accuracy, completeness, consistency, timeliness, validity, accessibility)

• Analytics project lifecycle including (business understanding, data preparation, modeling, evaluation, deployment, monitoring)

• Course structure and learning approach including (theory, hands-on practice, real datasets, projects, certification preparation)

2. Advanced Microsoft Excel for Data Analysis

2.1 Advanced Excel Functions and Formulas

• Logical functions including (IF, IFS, SWITCH, AND, OR, NOT, nested logic, error handling IFERROR)

• Lookup and reference functions including (VLOOKUP, HLOOKUP, INDEX, MATCH, XLOOKUP, INDIRECT, dynamic arrays)

• Text functions including (CONCATENATE, TEXTJOIN, LEFT, RIGHT, MID, FIND, SUBSTITUTE, text parsing)

• Date and time functions including (DATEDIF, NETWORKDAYS, EOMONTH, WORKDAY, time calculations, fiscal calendars)

• Array formulas including (dynamic arrays, FILTER, SORT, UNIQUE, SEQUENCE, spill ranges, calculation efficiency)

2.2 PivotTables and PivotCharts Advanced Techniques

• PivotTable advanced features including (calculated fields, calculated items, grouping, slicers, timelines, Show Values As)

• Data modeling including (relationships, Power Pivot, data model, DAX basics, hierarchies, KPI creation)

• PivotChart customization including (chart types, formatting, drill-down, interactive elements, combination charts)

• Dashboard creation including (layout design, visual hierarchy, interactivity, parameter controls, user experience)

2.3 What-If Analysis and Optimization

• Scenario Manager including (defining scenarios, comparing outcomes, scenario summary, sensitivity analysis)

• Goal Seek including (reverse calculation, target setting, breakeven analysis, optimization constraints)

• Data Tables including (one-variable, two-variable, sensitivity analysis, Monte Carlo simulation preparation)

• Solver add-in including (linear programming, constraint optimization, objective function, decision variables, applications)

2.4 Advanced Data Visualization in Excel

• Chart types and selection including (scatter plots, waterfall, funnel, treemap, sunburst, box and whisker, appropriate usage)

• Conditional formatting advanced including (data bars, color scales, icon sets, custom formulas, highlighting patterns)

• Sparklines including (line, column, win/loss, trend visualization, inline charts, variance display)

• Dynamic charts including (named ranges, OFFSET function, interactive elements, automatic updates, user controls)

3. Statistical Analysis Fundamentals

3.1 Descriptive Statistics and Data Distribution

• Measures of central tendency including (mean, median, mode, trimmed mean, weighted average, appropriate selection)

• Measures of dispersion including (range, variance, standard deviation, coefficient of variation, interquartile range)

• Distribution shapes including (normal, skewed, bimodal, kurtosis, symmetry, outlier detection)

• Data visualization for distribution including (histograms, box plots, violin plots, density plots, Q-Q plots)

• Five-number summary including (minimum, Q1, median, Q3, maximum, box plot construction)

3.2 Probability and Probability Distributions

• Probability fundamentals including (classical, empirical, subjective, conditional probability, Bayes' theorem)

• Discrete distributions including (binomial, Poisson, geometric, hypergeometric, probability mass functions)

• Continuous distributions including (normal, exponential, uniform, lognormal, probability density functions)

• Central Limit Theorem including (sampling distributions, standard error, implications for inference)

• Normal distribution applications including (Z-scores, percentiles, confidence intervals, empirical rule 68-95-99.7)

3.3 Hypothesis Testing

• Hypothesis testing framework including (null hypothesis, alternative hypothesis, significance level alpha, p-value, decision rules)

• Type I and Type II errors including (alpha and beta, power analysis, sample size determination, error trade-offs)

• T-tests including (one-sample, independent samples, paired samples, assumptions, normality testing, effect size)

• ANOVA (Analysis of Variance) including (one-way, two-way, F-statistic, post-hoc tests Tukey, assumptions)

• Chi-square tests including (goodness of fit, independence test, contingency tables, expected frequencies)

• Non-parametric tests including (Mann-Whitney U, Wilcoxon signed-rank, Kruskal-Wallis, when to use)

3.4 Correlation and Statistical Relationships

• Correlation analysis including (Pearson, Spearman, Kendall, correlation coefficient interpretation, causation versus correlation)

• Scatter plots and correlation matrices including (visualization, multicollinearity detection, heatmaps)

• Covariance including (calculation, interpretation, relationship to correlation, portfolio applications)

• Partial correlation including (controlling for variables, confounding factors, relationship isolation)

4. Regression Analysis and Predictive Modeling

4.1 Simple and Multiple Linear Regression

• Simple linear regression including (least squares method, slope and intercept, fitted line, equation interpretation)

• Regression assumptions including (linearity, independence, homoscedasticity, normality of residuals, LINE acronym)

• R-squared and adjusted R-squared including (coefficient of determination, model fit, overfitting, parsimony)

• Multiple linear regression including (multiple predictors, partial regression coefficients, multicollinearity VIF, feature selection)

• Regression diagnostics including (residual plots, Cook's distance, leverage, influential points, assumption validation)

4.2 Model Building and Variable Selection

• Feature selection methods including (forward selection, backward elimination, stepwise regression, all subsets)

• Regularization techniques including (Ridge regression L2, Lasso L1, Elastic Net, coefficient shrinkage, feature elimination)

• Model comparison including (AIC Akaike, BIC Bayesian, cross-validation, holdout validation, train-test split)

• Interaction terms including (multiplication of predictors, non-linear relationships, polynomial regression)

4.3 Logistic Regression for Classification

• Binary logistic regression including (odds ratio, log-odds, logit transformation, probability prediction, threshold selection)

• Model evaluation metrics including (confusion matrix, accuracy, precision, recall, F1-score, ROC curve, AUC)

• Multinomial logistic regression including (multiple categories, reference category, interpretation)

• Logistic regression applications including (credit scoring, churn prediction, disease diagnosis, marketing response)

4.4 Time Series Analysis and Forecasting

• Time series components including (trend, seasonality, cyclical, irregular, decomposition methods)

• Moving averages including (simple MA, weighted MA, exponential smoothing, smoothing parameter selection)

• Autoregressive models including (AR, MA, ARMA, ARIMA, stationarity, differencing, ACF and PACF)

• Seasonal decomposition including (STL, seasonal indices, deseasonalization, seasonal ARIMA)

• Forecasting methods including (Holt-Winters, Prophet, evaluation metrics MAE MSE MAPE, forecast intervals)

5. Python for Data Analysis

5.1 Python Fundamentals and Environment Setup

• Python installation including (Anaconda distribution, Jupyter Notebook, Google Colab, IDE selection)

• Python basics including (data types, variables, operators, control structures, functions, libraries)

• NumPy fundamentals including (arrays, vectorization, mathematical operations, broadcasting, indexing)

• Pandas data structures including (Series, DataFrame, indexing, selecting, filtering, sorting)

5.2 Data Manipulation with Pandas

• Data import/export including (CSV, Excel, SQL, JSON, APIs, read_csv, to_excel, connection strings)

• Data cleaning including (handling missing values dropna fillna, duplicates, data type conversion, string operations)

• Data transformation including (apply functions, map, applymap, lambda functions, method chaining)

• Grouping and aggregation including (groupby, aggregation functions, pivot tables, crosstab)

• Merging and joining including (merge, concat, join, inner/outer/left/right, keys)

5.3 Data Visualization with Matplotlib and Seaborn

• Matplotlib fundamentals including (figure and axes, line plots, scatter plots, customization, subplots)

• Seaborn statistical plots including (distribution plots, categorical plots, regression plots, heatmaps, pair plots)

• Advanced visualization including (facet grids, multi-panel figures, annotations, styling, color palettes)

• Interactive visualization including (Plotly, Bokeh, interactive elements, dashboards, web integration)

5.4 Statistical Analysis in Python

• SciPy statistics module including (distributions, hypothesis tests, correlation, statistical functions)

• Statsmodels including (regression models OLS, time series ARIMA, diagnostics, summary statistics)

• Machine learning with Scikit-learn including (preprocessing, model selection, evaluation, pipelines)

6. Introduction to Machine Learning

6.1 Machine Learning Fundamentals

• Machine learning types including (supervised learning, unsupervised learning, reinforcement learning, applications)

• Supervised learning including (regression, classification, labeled data, training and testing, prediction)

• Unsupervised learning including (clustering, dimensionality reduction, pattern discovery, unlabeled data)

• Machine learning workflow including (data preparation, feature engineering, model training, evaluation, tuning, deployment)

6.2 Classification Algorithms

• Decision trees including (splitting criteria Gini/entropy, pruning, tree depth, interpretation, CART algorithm)

• Random forests including (ensemble method, bagging, feature importance, out-of-bag error, hyperparameters)

• K-Nearest Neighbors (KNN) including (distance metrics, k selection, classification and regression, curse of dimensionality)

• Support Vector Machines (SVM) including (hyperplane, kernel trick, margin maximization, classification and regression)

• Naive Bayes including (probabilistic classifier, conditional independence, text classification, spam filtering)

6.3 Clustering Algorithms

• K-Means clustering including (centroid-based, elbow method, silhouette score, initialization, convergence)

• Hierarchical clustering including (agglomerative, divisive, dendrograms, linkage methods, cutting trees)

• DBSCAN (Density-Based) including (density reachability, epsilon and minPts, handling noise, arbitrary shapes)

• Cluster evaluation including (silhouette coefficient, Davies-Bouldin index, within-cluster variance, interpretation)

6.4 Model Evaluation and Validation

• Train-test split including (data partitioning 70-30 or 80-20, random sampling, stratification)

• Cross-validation including (k-fold, leave-one-out, stratified k-fold, repeated cross-validation, variance reduction)

• Performance metrics including (regression MSE RMSE MAE R², classification accuracy precision recall F1 AUC)

• Overfitting and underfitting including (bias-variance tradeoff, model complexity, regularization, learning curves)

• Hyperparameter tuning including (grid search, random search, Bayesian optimization, nested cross-validation)

7. R Programming for Statistical Analysis

7.1 R Fundamentals and RStudio

• R installation and RStudio interface including (console, script editor, environment, plots, packages)

• R basics including (vectors, matrices, data frames, lists, factors, functions, control structures)

• Data import/export including (read.csv, readxl, haven, foreign, database connections)

• Tidyverse ecosystem including (dplyr, ggplot2, tidyr, readr, tibble, pipe operator %>%)

7.2 Data Manipulation with dplyr and tidyr

• dplyr verbs including (select, filter, mutate, arrange, summarize, group_by, piped operations)

• Data reshaping with tidyr including (pivot_longer, pivot_wider, separate, unite, tidy data principles)

• String manipulation with stringr including (pattern matching, extraction, replacement, regular expressions)

• Date handling with lubridate including (parsing dates, date arithmetic, periods, durations, intervals)

7.3 Data Visualization with ggplot2

• Grammar of graphics including (data, aesthetics, geometries, facets, statistics, coordinates, themes)

• Geometric objects including (geom_point, geom_line, geom_bar, geom_histogram, geom_boxplot, combinations)

• Aesthetics mapping including (color, size, shape, alpha, position, scale transformations)

• Faceting including (facet_wrap, facet_grid, multi-panel plots, free scales)

• Customization including (themes, labels, legends, annotations, scales, color palettes)

7.4 Statistical Modeling in R

• Linear models including (lm function, formula interface, summary output, diagnostic plots)

• Generalized linear models including (glm, family specifications, link functions, logistic regression)

• Model comparison including (anova, AIC, BIC, likelihood ratio tests)

• Advanced modeling packages including (caret for machine learning, forecast for time series, survival analysis)

8. SQL for Data Analysis

8.1 SQL Fundamentals and Database Concepts

• Relational database concepts including (tables, rows, columns, primary keys, foreign keys, relationships, normalization)

• SQL syntax including (SELECT, FROM, WHERE, keywords, case sensitivity, statement termination)

• Data types including (INTEGER, VARCHAR, DATE, DECIMAL, BOOLEAN, type casting)

• Database management systems including (MySQL, PostgreSQL, SQL Server, Oracle, SQLite, cloud databases)

8.2 Data Retrieval and Filtering

• SELECT statement including (column selection, *, aliases AS, DISTINCT, calculated columns)

• WHERE clause including (filtering conditions, comparison operators, logical operators AND OR NOT, IN, BETWEEN)

• Pattern matching including (LIKE, wildcards % _, regular expressions, case sensitivity)

• ORDER BY including (sorting ASC/DESC, multiple columns, NULL handling)

• LIMIT and OFFSET including (result limiting, pagination, TOP in SQL Server)

8.3 Aggregation and Grouping

• Aggregate functions including (COUNT, SUM, AVG, MIN, MAX, STDDEV, distinct counts)

• GROUP BY clause including (grouping columns, aggregate calculations, multiple groups, rollup)

• HAVING clause including (filtering aggregated results, difference from WHERE, complex conditions)

• Window functions including (ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, PARTITION BY, running totals)

8.4 Joins and Subqueries

• JOIN types including (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN, CROSS JOIN, self joins)

• Join conditions including (ON clause, multiple conditions, composite keys, join performance)

• Subqueries including (scalar subqueries, correlated subqueries, IN/EXISTS, subquery in SELECT/FROM/WHERE)

• Common Table Expressions (CTE) including (WITH clause, recursive CTEs, query readability, complex queries)

• UNION operations including (UNION, UNION ALL, combining results, column matching)

9. Business Intelligence with Power BI

9.1 Power BI Desktop Fundamentals

• Power BI ecosystem including (Desktop, Service, Mobile, gateway, licensing, collaboration)

• Data import including (Excel, CSV, SQL databases, web sources, APIs, folder connections, parameters)

• Power Query Editor including (data transformation, cleansing, appending, merging, M language basics)

• Data modeling including (relationships, cardinality, cross-filter direction, star schema, snowflake schema)

9.2 DAX (Data Analysis Expressions)

• DAX fundamentals including (calculated columns, measures, calculated tables, context, evaluation)

• Basic DAX functions including (SUM, AVERAGE, COUNT, DISTINCTCOUNT, MIN, MAX, date functions)

• CALCULATE function including (context modification, filter arguments, filter functions, evaluation context)

• Time intelligence including (TOTALYTD, SAMEPERIODLASTYEAR, DATEADD, PARALLELPERIOD, fiscal calendars)

• Advanced DAX including (iterator functions SUMX, AVERAGEX, variables VAR, EARLIER, filter context)

9.3 Data Visualization and Dashboard Design

• Visual types including (bar/column charts, line charts, scatter plots, maps, tables, matrices, cards, gauges)

• Interactive features including (slicers, filters, drill-down, cross-filtering, bookmarks, buttons)

• Dashboard design principles including (layout, visual hierarchy, color theory, storytelling, user experience)

• Custom visuals including (marketplace, custom visuals import, R visuals, Python visuals, development)

9.4 Power BI Service and Collaboration

• Publishing to Power BI Service including (workspace, apps, sharing, permissions, row-level security)

• Scheduled refresh including (data gateway, refresh schedule, incremental refresh, monitoring)

• Collaboration features including (comments, subscriptions, alerts, sharing dashboards, embedding)

• Power BI mobile including (mobile-optimized reports, phone layouts, on-the-go access)

10. Exploratory Data Analysis (EDA) and Feature Engineering

10.1 Exploratory Data Analysis Techniques

• EDA objectives including (understanding data, detecting patterns, spotting anomalies, testing assumptions, generating hypotheses)

• Univariate analysis including (distribution analysis, summary statistics, outlier detection, missing values assessment)

• Bivariate analysis including (correlation, scatter plots, cross-tabulation, relationship discovery)

• Multivariate analysis including (correlation matrices, parallel coordinates, heatmaps, dimensionality reduction)

10.2 Data Cleaning and Preprocessing

• Missing data handling including (deletion, mean/median imputation, forward/backward fill, predictive imputation, MICE)

• Outlier treatment including (detection methods IQR Z-score, capping, transformation, deletion, investigation)

• Data transformation including (normalization, standardization, log transformation, Box-Cox, power transforms)

• Encoding categorical variables including (one-hot encoding, label encoding, ordinal encoding, target encoding, dummy variables)

10.3 Feature Engineering

• Feature creation including (derived features, interaction terms, polynomial features, domain knowledge application)

• Feature scaling including (min-max scaling, standardization, robust scaling, when to apply)

• Feature selection including (correlation-based, recursive feature elimination RFE, L1 regularization, feature importance)

• Dimensionality reduction including (PCA Principal Component Analysis, t-SNE, factor analysis, variance explained)

10.4 Handling Imbalanced Data

• Imbalanced data problems including (class imbalance, minority class, model bias, evaluation challenges)

• Resampling techniques including (oversampling SMOTE, undersampling, combination methods, ADASYN)

• Cost-sensitive learning including (class weights, misclassification costs, threshold adjustment)

• Evaluation metrics for imbalance including (precision-recall, F1-score, balanced accuracy, Matthews correlation)

11. Advanced Analytics Applications

11.1 Customer Analytics

• Customer segmentation including (RFM analysis Recency Frequency Monetary, clustering, persona development)

• Customer Lifetime Value (CLV) including (calculation methods, predictive modeling, strategic applications)

• Churn prediction including (logistic regression, survival analysis, feature importance, retention strategies)

• Market basket analysis including (association rules, Apriori algorithm, support/confidence/lift, cross-selling)

11.2 Financial Analytics

• Risk analytics including (credit scoring, default prediction, portfolio risk, Value at Risk VaR, stress testing)

• Financial forecasting including (revenue prediction, expense modeling, cash flow analysis, scenario planning)

• Time series in finance including (stock price analysis, volatility modeling GARCH, returns analysis)

• Fraud detection including (anomaly detection, classification models, transaction monitoring, feature engineering)

11.3 Operations and Supply Chain Analytics

• Demand forecasting including (time series methods, regression, machine learning, forecast accuracy metrics)

• Inventory optimization including (EOQ Economic Order Quantity, safety stock, ABC analysis, optimization models)

• Process optimization including (bottleneck analysis, simulation, queuing theory, linear programming)

• Predictive maintenance including (failure prediction, sensor data analysis, remaining useful life, cost-benefit)

11.4 Marketing Analytics

• Campaign effectiveness including (A/B testing, lift analysis, attribution modeling, ROI calculation)

• Customer response modeling including (propensity scores, logistic regression, targeting optimization)

• Marketing mix modeling including (multiple regression, elasticity, diminishing returns, budget allocation)

• Sentiment analysis including (text mining, natural language processing basics, classification, topic modeling)

12. Data Storytelling and Communication

12.1 Principles of Data Visualization

• Visual perception including (preattentive attributes, Gestalt principles, color theory, cognitive load)

• Chart selection including (comparison, composition, distribution, relationship, choosing appropriate visualizations)

• Data-ink ratio including (Edward Tufte principles, minimalism, removing chartjunk, clarity)

• Misleading visualizations including (truncated axes, cherry-picking, 3D distortion, inappropriate charts, ethical considerations)

12.2 Dashboard Design Best Practices

• Dashboard purpose including (operational, analytical, strategic dashboards, audience consideration)

• Layout and structure including (F-pattern, Z-pattern, visual hierarchy, whitespace, grouping)

• Interactivity including (filters, drill-down, parameters, tooltips, navigation, user control)

• Performance optimization including (data reduction, aggregation, query optimization, caching, refresh strategy)

12.3 Presenting Analytical Findings

• Storytelling with data including (narrative structure, context, insights, recommendations, call to action)

• Executive summary including (key findings, business impact, recommendations, concise presentation)

• Tailoring to audience including (technical versus non-technical, detail level, language, visualization complexity)

• Presentation delivery including (speaking skills, answering questions, handling objections, visual aids)

12.4 Reporting and Documentation

• Analysis documentation including (methodology, assumptions, data sources, limitations, reproducibility)

• Report structure including (executive summary, introduction, methodology, results, conclusions, appendices)

• Automated reporting including (scheduled reports, parameterized reports, R Markdown, Jupyter Notebooks)

• Version control including (Git basics, collaboration, tracking changes, documentation standards)