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Foundational Level (2 Weeks) 4 Lessons    - Kes 1500/=

• Module 1: Introduction to Excel and Data Basics 

• Module 2: Data Cleaning and Manipulation Basics 

• Module 3: Essential Formulas and Functions for Data Analysis 

• Module 4: Data Visualization Basics 

• Module 5: Introduction to PivotTables 

• Module 6: Data Protection and Sharing 

Intermediate (4 weeks) 8 Lessons - Kes 2000/=

Module 1: Advanced Data Cleaning and Manipulation 

Module 2: Advanced Functions for Analysis 

Module 3: Dynamic Data Analysis Using Named Ranges and Tables 

Module 4: Advanced PivotTables and PivotCharts 

Module 5: Data Visualization Techniques 

Module 6: Collaboration and Automation Basics 

Advanced (6 weeks) 12 Lessons - Kes 2000=

Module 1: Power Query for Data Extraction and Transformation 

Module 2: Power Pivot for Complex Data Analysis 

Module 3: Advanced DAX Functions for Data Analysis 

Module 4: Advanced Data Visualization and Dashboarding
Module 5: Inferential Data Analysis

Module 6: Real-World Data Analysis Projects 

Pay Full Course at  30% Discount  (3850)

Foundational Level

Module 1: Introduction to SPSS and Data Management

Module 2: Data Cleaning and Transformation 

Module 3: Exploring Data in SPSS 

Module 4: Basic Statistical Analysis 

Module 5: Data Visualization Techniques 

Intermediate

Intermediate Level

Module 1: Advanced Data Transformation Techniques

Module 2: Comparative Statistical Analysis

Module 3: Categorical Data Analysis

Module 4: Data Reduction Techniques

Module 5: Regression Analysis Basics

Module 6: Data Visualization and Customization

Advanced

Module 1: Advanced Regression Techniques

Module 2: Time Series Analysis

Module 3: Multivariate Data Analysis

Module 4: Advanced Data Automation Using Syntax

Module 5: Statistical Assumption Testing

Module 6: Real-World Data Analysis Projects

Foundational Level

Module 1: Introduction to STATA and Data Management

Module 2: Data Cleaning and Transformation

Module 3: Exploratory Data Analysis (EDA)

Module 4: Working with Datasets

Module 5: Basic Statistical Analysis

Module 6: Reporting and Output Management

Intermediate Level

Module 1: Advanced Data Manipulation

Module 2: Comparative Statistical Analysis

Module 3: Regression Analysis Basics

Module 4: Categorical Data Analysis

Module 5: Data Visualization Techniques

Module 6: Automating Analysis with Do-Files

Advanced Level

Module 1: Advanced Regression Techniques

Module 2: Time Series Analysis

Module 3: Panel Data Analysis

Module 4: Survival Analysis

Module 5: Multivariate Data Analysis

Module 6: Real-World Projects and Reporting

Module 1: Introduction to SQL and Relational Databases

• Understanding relational databases

• Installing and connecting to a SQL environment (MySQL, PostgreSQL, SQLite)

• Database concepts: tables, rows, columns, and relationships

• Basic SQL syntax

Module 2: Basic Data Retrieval with SELECT Statements

• SELECT, FROM, WHERE clauses

• Filtering data using logical operators (AND, OR, NOT)

• Sorting data with ORDER BY

• Aliasing columns and tables

Module 3: Aggregating Data

• Using aggregate functions (COUNT, SUM, AVG, MAX, MIN)

• Grouping data with GROUP BY

• Filtering groups with HAVING

Module 4: Data Filtering and Conditional Logic

• Wildcards and pattern matching with LIKE

• Using IN and BETWEEN operators

• Conditional logic with CASE WHEN statements

Module 5: Joins and Combining Data

• Understanding relationships between tables

• INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN

• Joining multiple tables

Module 6: Basic Subqueries

• Inline views and nested queries

• Using subqueries in SELECT, WHERE, and FROM clauses

Intermediate Level

Module 1: Advanced Joins and Set Operations

• CROSS JOIN and SELF JOIN

• UNION, INTERSECT, and EXCEPT operations

Module 2: Data Manipulation with SQL (DML)

• Inserting data with INSERT

• Updating records with UPDATE

• Deleting records with DELETE

• Transaction control with COMMIT, ROLLBACK, and SAVEPOINT

Module 3: Advanced Subqueries and CTEs (Common Table Expressions)

• Correlated subqueries

• Recursive CTEs

• Window functions (ROW_NUMBER, RANK, NTILE)

Module 4: Data Integrity and Constraints

• Primary keys, foreign keys, and unique constraints

• Handling null values and default constraints

• Check constraints and data validation

Module 5: Performance Optimization Techniques

• Understanding query execution plans

• Indexing strategies

• Optimizing joins and subqueries

Module 6: Functions in SQL

• String functions (CONCAT, SUBSTRING)

• Date and time functions (NOW, DATEADD, DATEDIFF)

• Mathematical and conditional functions

Advanced Level

Module 1: Advanced Window Functions

• Partitioning and ordering with PARTITION BY

• Aggregate functions in windowing

• Moving averages and cumulative sums

Module 2: Data Modeling and Database Design

• Normalization and denormalization

• Designing ER diagrams

• Implementing relationships in SQL databases

Module 3: Advanced Data Analysis Techniques

• Statistical calculations with SQL

• Using pivot tables (CASE for dynamic pivoting)

• Complex data transformations

Module 4: Stored Procedures and Triggers

• Creating and executing stored procedures

• Automating tasks with triggers

• Error handling in stored procedures

Module 5: Security and User Management

• Creating users and assigning roles

• Granting and revoking privileges

• Securing sensitive data

Module 6: Integrating SQL with Data Analytics Tools

• Connecting SQL with Python and R

• Visualizing SQL data in Power BI and Tableau

• Automating report generation

Foundational Level

Module 1: Introduction to Python for Data Analysis

• Installing Python and Jupyter Notebook

• Overview of Python data science libraries (Pandas, NumPy, Matplotlib, Seaborn)

• Basic syntax, variables, and data types

• Input/output operations

Module 2: Data Structures and Control Flow

• Lists, dictionaries, sets, and tuples

• Conditional statements (if-else)

• Loops (for, while)

Module 3: Data Manipulation with Pandas

• DataFrames and Series basics

• Reading and writing CSV, Excel, and JSON files

• Indexing and slicing data

• Filtering and subsetting data

Module 4: Exploratory Data Analysis (EDA)

• Descriptive statistics (describe())

• Handling missing data

• Visualizing data with Matplotlib (basic plots)

• Detecting outliers

Module 5: Data Cleaning and Transformation

• String manipulation

• Date-time handling

• Feature engineering basics

• Grouping and aggregation (groupby)

Module 6: Data Visualization with Seaborn

• Distribution plots (distplot, kdeplot)

• Relational plots (scatterplot, lineplot)

• Categorical plots (boxplot, barplot)

Intermediate Level

Module 1: Advanced Data Manipulation with Pandas

• MultiIndex and hierarchical data

• Pivot tables (pivot_table)

• Advanced grouping and aggregation

Module 2: Statistical Analysis and Hypothesis Testing

• Descriptive and inferential statistics

• Hypothesis testing (ttest_ind, chi2_contingency)

• Correlation and covariance

Module 3: Introduction to Data Wrangling

• Merging, joining, and concatenating DataFrames

• Reshaping data with melt and pivot

• Handling duplicate data

Module 4: Working with APIs and Web Scraping

• Fetching data from APIs (requests library)

• Parsing JSON responses

• Introduction to BeautifulSoup for web scraping

Module 5: Data Time Series Analysis

• Working with time series data

• Resampling and rolling window operations

• Visualizing time series trends

Module 6: Interactive Data Visualization with Plotly

• Creating interactive charts

• Time series plots

• Heatmaps and 3D visualizations

Advanced Level

Module 1: Regression Analysis and Predictive Modeling

• Simple and multiple linear regression

• Model evaluation metrics (MSE, RMSE)

• Polynomial regression

Module 2: Classification Techniques

• Logistic regression

• K-Nearest Neighbors (KNN)

• Support Vector Machines (SVM)

Module 3: Clustering and Dimensionality Reduction

• K-Means clustering

• Hierarchical clustering

• Principal Component Analysis (PCA)

Module 4: Machine Learning Workflow with Scikit-Learn

• Data preprocessing (StandardScaler, LabelEncoder)

• Model selection and hyperparameter tuning

• Cross-validation techniques

Module 5: Natural Language Processing (NLP)

• Text data preprocessing

• Sentiment analysis using TextBlob and NLTK

• Word cloud visualization

Module 6: Advanced Time Series Analysis

• ARIMA and SARIMA models

• Forecasting techniques

• Performance evaluation

Foundational Level

Module 1: Introduction to R and RStudio

• Installing R and RStudio

• Understanding RStudio interface

• Writing and executing basic R code

• R data structures: vectors, lists, matrices, and data frames

Module 2: Data Import and Export

• Reading and writing CSV, Excel, and text files

• Using readr, readxl, and writexl packages

• Importing data from web and APIs

Module 3: Data Manipulation with dplyr and tidyr

• Selecting, filtering, and mutating data (select, filter, mutate)

• Summarizing data (group_by, summarize)

• Reshaping data with gather() and spread()

Module 4: Exploratory Data Analysis (EDA)

• Descriptive statistics (summary())

• Handling missing data (na.omit(), is.na())

• Visualizing data with ggplot2 (basic plots)

Module 5: Data Cleaning and Transformation

• Removing duplicates

• String manipulation (stringr package)

• Handling dates and times (lubridate package)

Module 6: Basic Data Visualization

• Creating bar charts, histograms, and scatter plots

• Customizing plots (titles, legends, colors)

• Introduction to ggplot2

Intermediate Level

Module 1: Advanced Data Manipulation

• Joining datasets (inner_join, left_join, etc.)

• Pivoting data (pivot_longer, pivot_wider)

• Advanced grouping and aggregation

Module 2: Statistical Analysis in R

• Descriptive and inferential statistics

• Correlation analysis

• Hypothesis testing (t.test(), chisq.test())

Module 3: Regression Analysis

• Simple linear regression (lm() function)

• Multiple regression models

• Model diagnostics and interpretation

Module 4: Time Series Analysis Basics

• Creating and plotting time series data

• Decomposing time series

• Moving averages and forecasting

Module 5: Data Visualization with ggplot2

• Advanced plot types (box plots, violin plots)

• Faceting and theming

• Exporting plots

Module 6: Introduction to R Markdown for Reporting

• Creating reproducible reports

• Formatting text and code outputs

• Exporting reports to PDF, HTML, and Word

Advanced Level

Module 1: Machine Learning with R

• Data preprocessing (caret package)

• Model training and evaluation

• Cross-validation techniques

Module 2: Classification Models

• Logistic regression

• Decision trees (rpart)

• Random forest (randomForest)

Module 3: Clustering and Dimensionality Reduction

• K-means clustering

• Hierarchical clustering

• Principal Component Analysis (PCA)

Module 4: Time Series Forecasting

• ARIMA and SARIMA models (forecast package)

• Evaluating forecast accuracy

• Advanced time series visualization

Module 5: Natural Language Processing (NLP)

• Text mining with tm and tidytext

• Word cloud visualization

• Sentiment analysis

Module 6: Advanced Reporting and Dashboarding

• Interactive dashboards with shiny

• Automating reports with rmarkdown

• Integrating R with Excel and APIs

Foundational Level

Module 1: Introduction to Power BI

• Overview of Power BI and its components

• Installing and setting up Power BI Desktop

• Understanding Power BI Service and Power BI Mobile

• Connecting to various data sources

Module 2: Data Importing and Transformation with Power Query

• Loading data from Excel, databases, and web sources

• Cleaning and transforming data

• Removing duplicates and handling missing values

• Merging and appending datasets

Module 3: Data Modeling Basics

• Understanding relationships between tables

• Creating a star schema

• Using calculated columns and measures

• Introduction to DAX (Data Analysis Expressions)

Module 4: Visualizing Data with Power BI

• Creating basic visuals (bar charts, line charts, pie charts)

• Using slicers, filters, and drill-through

• Formatting and customizing visuals

• Best practices for visualization design

Module 5: Creating Interactive Dashboards

• Designing dashboard layouts

• Adding interactivity with slicers and bookmarks

• Dashboard publishing to Power BI Service

• Sharing dashboards

Module 6: Business Insights and Storytelling

• Exploring key business metrics

• Creating KPI cards and trend analysis

• Communicating insights effectively

Intermediate Level

Module 1: Advanced Data Modeling

• Creating complex relationships

• Using calculated tables

• Optimizing data models for performance

Module 2: Advanced DAX Functions

• Time intelligence functions (TOTALYTD, DATEADD)

• Context management (CALCULATE, FILTER)

• Conditional logic with SWITCH

Module 3: Power BI Service and Dataflows

• Publishing reports to Power BI Service

• Creating and managing workspaces

• Introduction to dataflows for reusable data pipelines

Module 4: Advanced Data Visualizations

• Custom visuals from Power BI marketplace

• Using drill-through and drill-down features

• Visual interactions and formatting tips

Module 5: Power BI and Excel Integration

• Connecting Power BI to Excel

• Using PowerPivot and Power Query in Excel

• Exporting Power BI data to Excel

Module 6: Business Use Cases and Scenario Analysis

• Sales performance analysis

• Financial reporting dashboards

• Customer segmentation analysis

Advanced Level

Module 1: Advanced DAX for Business Analysis

• Advanced time intelligence scenarios

• Dynamic ranking and grouping

• Optimization techniques for DAX

Module 2: Advanced Data Modeling and Performance Optimization

• Handling large datasets with composite models

• Performance tuning techniques

• Using aggregations and partitions

Module 3: Row-Level Security (RLS) and Data Governance

• Implementing row-level security in reports

• Managing user roles and access

• Best practices for data governance

Module 4: Power BI Embedded and API Integration

• Embedding Power BI reports in web applications

• Introduction to Power BI REST APIs

• Automating report refreshes

Module 5: Power BI with AI and Machine Learning

• Using AI visuals (Key Influencers, Decomposition Tree)

• Integrating Power BI with Azure Machine Learning

• Predictive analytics dashboards

Module 6: Enterprise Solutions and Deployment

• Power BI deployment pipelines

• Managing and versioning reports

• Building enterprise-level reporting solutions

Foundational Level

Module 1: Introduction to R and RStudio

• Installing R and RStudio

• Understanding RStudio interface

• Basic syntax: variables, data types, and operators

• R packages and libraries (using install.packages() and library())

Module 2: Data Structures in R

• Vectors, lists, matrices, data frames, and factors

• Creating and manipulating data structures

• Subsetting and indexing

Module 3: Data Import and Export

• Reading data from CSV, Excel, and text files (read.csv(), readxl)

• Exporting data (write.csv())

• Connecting to databases using DBI

Module 4: Data Manipulation with dplyr and tidyr

• Filtering, selecting, and arranging data

• Grouping and summarizing with group_by() and summarize()

• Data reshaping (pivot_longer(), pivot_wider())

Module 5: Data Visualization with ggplot2

• Understanding the ggplot2 grammar of graphics

• Creating bar plots, line charts, and histograms

• Customizing plots (themes, labels, legends)

Module 6: Exploratory Data Analysis (EDA)

• Descriptive statistics (mean(), sd(), summary())

• Identifying missing values and outliers

• Correlation analysis

Intermediate Level

Module 1: Advanced Data Manipulation with dplyr and tidyr

• Advanced filtering and conditional operations

• Joins and merging datasets (inner_join(), left_join())

• Handling missing data with replace_na()

Module 2: Statistical Analysis in R

• Descriptive and inferential statistics

• Hypothesis testing (t.test(), chisq.test())

• Analysis of Variance (ANOVA)

Module 3: Time Series Analysis

• Working with time-series data

• Plotting time series

• Decomposing time-series components

Module 4: Machine Learning Basics with caret

• Data partitioning and cross-validation

• Linear regression and decision trees

• Model evaluation metrics

Module 5: Text Data Analysis

• Text preprocessing (tm and textclean packages)

• Sentiment analysis with syuzhet

• Word clouds

Module 6: Interactive Visualizations with Shiny

• Building simple Shiny applications

• Adding interactivity with widgets

• Deploying Shiny apps

Advanced Level

Module 1: Advanced Machine Learning with Tidymodels

• Random forests and gradient boosting models

• Hyperparameter tuning

• Feature importance and selection

Module 2: Deep Learning with Keras and TensorFlow

• Setting up Keras in R

• Building and training neural networks

• Image and text classification

Module 3: Advanced Statistical Modeling

• Generalized linear models (GLMs)

• Mixed-effect models

• Survival analysis

Module 4: Big Data Handling with sparklyr

• Connecting R to Spark

• Distributed data processing

• Handling large datasets

Module 5: Advanced Time Series Forecasting

• ARIMA, SARIMA, and Prophet models

• Model evaluation and selection

• Forecast visualization

Module 6: Automation and Model Deployment

• Creating APIs with Plumber

• Automating workflows with RMarkdown and Shiny dashboards

• Deploying models on cloud platforms

Foundational Level

Module 1: Introduction to Python for Data Science

• Setting up the environment (Jupyter Notebook, Anaconda)

• Python fundamentals (variables, data types, operators, control structures)

• Functions, modules, and libraries

• Introduction to data science and its applications

Module 2: Data Manipulation with Pandas

• Loading datasets (read_csv, read_excel)

• DataFrame basics: selecting, filtering, and slicing data

• Handling missing data

• Basic data aggregation and grouping

Module 3: Data Visualization with Matplotlib and Seaborn

• Creating line plots, bar charts, and histograms

• Customizing plots (titles, labels, and colors)

• Introduction to Seaborn for advanced visualizations

Module 4: Exploratory Data Analysis (EDA)

• Descriptive statistics (mean, median, std)

• Identifying outliers

• Data distributions and correlations

Module 5: Data Cleaning and Transformation

• Handling duplicates and missing values

• String manipulations

• Feature engineering basics

Module 6: Introduction to Numpy for Numerical Computing

• Understanding Numpy arrays

• Vectorized operations

• Basic matrix operations

Intermediate Level

Module 1: Statistical Analysis for Data Science

• Probability concepts

• Descriptive and inferential statistics

• Hypothesis testing (ttest, chi2)

Module 2: Advanced Data Manipulation with Pandas

• Advanced grouping and aggregation

• Reshaping data with melt() and pivot()

• Handling time-series data

Module 3: Advanced Data Visualization with Plotly

• Interactive visualizations

• Time-series and heatmaps

• Custom dashboards with Plotly Dash

Module 4: Machine Learning Basics with Scikit-Learn

• Supervised learning: regression and classification

• Model training and evaluation

• Cross-validation techniques

Module 5: Feature Selection and Engineering

• One-hot encoding and label encoding

• Scaling and normalization techniques

• Feature importance analysis

Module 6: Introduction to Natural Language Processing (NLP)

• Text preprocessing (tokenization, stemming, lemmatization)

• Sentiment analysis with TextBlob

• Word cloud visualization

Advanced Level

Module 1: Advanced Machine Learning

• Decision Trees, Random Forests, and Gradient Boosting

• Hyperparameter tuning with GridSearchCV

• Model evaluation and performance metrics

Module 2: Deep Learning with TensorFlow and Keras

• Understanding neural networks

• Building simple neural network models

• Image classification basics

Module 3: Time Series Analysis

• Decomposing time series data

• Forecasting models (ARIMA, SARIMA)

• Evaluating forecast performance

Module 4: Big Data Integration with PySpark

• Setting up PySpark environment

• DataFrames in PySpark

• Distributed computing concepts

Module 5: Advanced NLP Techniques

• Word embeddings with Word2Vec

• Text classification with scikit-learn

• Building chatbot models

Module 6: Model Deployment and Automation

• Creating APIs for machine learning models (Flask/FastAPI)

• Deploying models on cloud platforms

• Automating data pipelines with Airflow