Data Science with GenAI Training

RexGalaxy Academy's Data Science with GenAI Training is a practical, project-based program designed for learners who want to build strong data analysis, machine learning, dashboarding and AI-assisted analytics skills for modern data roles.

This course keeps the broad classic data science structure intact: Python programming, statistics, Excel analytics, SQL, data cleaning, exploratory data analysis, visualization, business intelligence, machine learning, model evaluation, dashboards, storytelling and portfolio projects. Along with the normal data science roadmap, learners also practice GenAI-enabled workflows such as prompt-based data exploration, AI-assisted code explanation, automated report drafting, insight summarization, data storytelling support, model interpretation prompts and productivity-focused analytics workflows.

The training is built around real datasets and practical case studies. Students work with spreadsheets, SQL data, Python notebooks, charts, dashboards, machine learning models and AI-assisted documentation. The focus is not only on tools, but on understanding data problems, asking better business questions, cleaning data correctly, creating meaningful insights and presenting outcomes professionally.

Students should be able to:

• Analyze datasets using Python, Excel, SQL, statistics and visualization libraries.

• Clean, transform and explore structured data for business reporting and decision-making.

• Build dashboards, charts, KPI reports and insight summaries for real-world use cases.

• Apply machine learning fundamentals for supervised and unsupervised learning problems.

• Use GenAI tools responsibly for code explanation, insight drafting, prompt-based analysis, report writing and faster project documentation.

• Build portfolio-ready data science projects with notebooks, dashboards, reports, model outputs and interview-ready explanations.

Start with data science fundamentals, analytics thinking, project workflow and responsible GenAI-assisted learning.

• Understand what data science is and how it connects business problems, data collection, cleaning, analysis, modelling and decision-making.

• Learn the difference between data analyst, data scientist, BI analyst, machine learning beginner and AI-enabled analytics roles.

• Study the complete data project lifecycle from problem definition and dataset review to reporting, presentation and recommendations.

• Set up Python, Jupyter Notebook, VS Code, Excel, SQL environment, GitHub and AI-assisted productivity tools.

• Learn how to understand columns, identify target variables, ask business questions and define success metrics.

• Practice responsible GenAI usage for concept revision, code explanation, prompt-based exploration, report drafting and documentation support.

• Understand AI limitations including hallucinated answers, wrong formulas, incorrect code, data privacy risks and the need for human validation.

• Build project discipline with folders, notebooks, datasets, outputs, charts, reports, screenshots and README documentation.

• Outcome: understand the data science roadmap and use GenAI as a support tool while keeping fundamentals strong.

Build Python programming skills required for data analysis, automation, notebooks and machine learning workflows.

• Learn Python basics including variables, data types, operators, conditions, loops, functions and clean coding style.

• Work with lists, tuples, dictionaries, sets, strings and common data manipulation patterns used in analytics.

• Understand functions, parameters, return values, reusable logic and helper functions for data tasks.

• Practice file handling basics, CSV awareness, reading data, writing outputs and organizing scripts or notebooks.

• Learn error handling with try-except, debugging habits, print checks and reading error messages clearly.

• Use NumPy and Pandas as the foundation for data science programming.

• Practice notebook workflow including markdown notes, code cells, outputs, charts and explanation writing.

• Use GenAI tools to explain Python errors, suggest practice problems, refactor functions and generate documentation drafts.

• Outcome: write Python code confidently for data cleaning, analysis, visualization and machine learning preparation.

Learn the statistical foundation needed to interpret data, measure patterns and support data-driven decisions.

• Understand descriptive statistics such as mean, median, mode, range, variance, standard deviation and percentile interpretation.

• Learn probability basics including events, sample space, conditional probability awareness and real-world uncertainty.

• Understand distributions, normal distribution awareness, skewness, outliers and how data shape affects analysis.

• Practice correlation, covariance awareness, relationship interpretation and avoiding false conclusions from weak patterns.

• Learn sampling, population, bias, sample size awareness and how poor sampling affects business insights.

• Understand hypothesis testing awareness, p-values concept, confidence intervals and business decision interpretation.

• Use statistics in Python and Excel to summarize data, compare groups and identify patterns.

• Use GenAI to explain statistical concepts, but validate calculations with actual formulas and tools.

• Outcome: interpret data with stronger statistical reasoning and avoid misleading conclusions.

Strengthen core data handling skills using Excel, SQL and structured datasets for practical analytics work.

• Use Excel for sorting, filtering, formulas, conditional formatting, pivot tables, charts and quick business summaries.

• Learn spreadsheet data cleaning including duplicate removal, missing values, text functions, date formatting and validation checks.

• Understand SQL fundamentals including tables, rows, columns, primary keys, filtering, sorting and aggregation.

• Write SQL queries using SELECT, WHERE, ORDER BY, GROUP BY, HAVING, JOIN awareness and basic subqueries.

• Connect SQL thinking with business reporting such as sales reports, customer summaries, product performance and attendance data.

• Learn data import-export workflows using CSV, Excel files, database extracts and notebook-based reading.

• Practice quality checks such as missing values, duplicates, inconsistent categories, wrong data types and outliers.

• Use GenAI for query explanation, formula ideas and report wording while verifying results manually.

• Outcome: handle structured data confidently before moving deeper into Python analytics.

Use Pandas to clean, transform and explore datasets for meaningful analysis and project work.

• Load datasets using Pandas from CSV, Excel and other structured formats with proper encoding and column checks.

• Inspect data using head, tail, shape, info, describe, value counts and missing value summaries.

• Clean data by handling null values, duplicates, inconsistent categories, incorrect types, spaces, date formats and outliers.

• Transform data using filtering, sorting, grouping, merging, mapping, replacing and feature creation.

• Perform exploratory data analysis to identify trends, relationships, distributions, anomalies and business insights.

• Create summary tables using groupby, pivot tables, cross-tab style views and calculated columns.

• Document EDA findings in notebooks with markdown explanations, charts, observations and next-step questions.

• Use GenAI for EDA checklist ideas, code explanation, insight wording and hypothesis generation while validating all outputs.

• Outcome: convert messy raw data into clean, analysis-ready datasets with strong EDA documentation.

Create charts, dashboards and visual stories that communicate insights clearly to business audiences.

• Learn visualization principles including chart selection, color discipline, labels, titles, scale, readability and avoiding clutter.

• Use Matplotlib and Seaborn for line charts, bar charts, histograms, scatter plots, box plots, heatmaps and distribution views.

• Use charts to compare trends, categories, relationships, outliers, rankings and performance metrics.

• Build business storytelling with context, key insight, supporting evidence, recommendation and conclusion.

• Create KPI summaries using totals, averages, growth rates, conversion rates and performance indicators.

• Understand dashboard planning with audience, metrics, filters, layout, refresh needs and action-focused design.

• Use GenAI to draft insight summaries, chart captions, dashboard narratives and presentation talking points.

• Validate AI-generated insights against actual data, charts and calculations before including them.

• Outcome: communicate data insights through clean visuals and professional report language.

Build dashboard thinking with BI tools, KPI reporting, interactive visuals and business-ready analytics deliverables.

• Understand BI purpose, dashboard users, KPIs, filters, slicers, drill-downs and decision-focused reporting.

• Prepare data for dashboarding through cleaning, modelling, relationship awareness and metric definition.

• Build dashboard pages with cards, charts, tables, filters, trend lines and category comparisons.

• Design reports for sales, marketing, HR, finance, operations, education and customer analytics.

• Learn dashboard layout discipline including visual hierarchy, consistent formatting, spacing, labels and actionable summaries.

• Understand basic data modelling concepts such as dimension tables, fact tables and relationships awareness.

• Create calculated measures awareness for totals, averages, growth, ratios and performance indicators.

• Use GenAI to brainstorm dashboard metrics, write executive summaries, explain chart findings and prepare presentation notes.

• Outcome: build professional BI-style dashboards and explain business insights clearly.

Learn machine learning concepts, model workflow, feature preparation and supervised learning basics.

• Understand machine learning purpose, supervised learning, unsupervised learning, features, labels, training data and prediction goals.

• Learn ML workflow including problem framing, data cleaning, train-test split, feature selection, model training, evaluation and interpretation.

• Practice regression concepts for predicting continuous values such as price, sales, salary or demand.

• Practice classification concepts for predicting categories such as churn, approval, pass/fail or customer segment.

• Use scikit-learn basics for model building, preprocessing, training, prediction and evaluation.

• Understand overfitting, underfitting, bias-variance awareness and why model evaluation matters.

• Compare baseline models and improved models using metrics and business interpretation.

• Use GenAI for ML concept explanation, feature idea brainstorming and model report drafting while validating outputs carefully.

• Outcome: understand core machine learning workflows and build beginner-level predictive models.

Improve ML models through better features, metrics, validation and project documentation.

• Understand evaluation metrics such as accuracy, precision, recall, F1-score, confusion matrix, MAE, MSE, RMSE and R-squared.

• Choose metrics based on business problem, risk, imbalance and decision consequences.

• Perform feature engineering such as encoding categories, scaling values, date features, text cleanup and derived columns.

• Handle missing values, outliers and data leakage awareness before training models.

• Compare algorithms such as linear regression, logistic regression, decision tree, random forest awareness and clustering basics.

• Tune models at a beginner level with parameter awareness, cross-validation concept and performance comparison.

• Document model assumptions, data limitations, important features, evaluation results and business recommendations.

• Use GenAI to create model explanation drafts, metric interpretation notes, feature brainstorming and interview revision questions.

• Outcome: create more complete machine learning projects with clear evaluation and professional explanation.

Use GenAI tools for analytics productivity, prompt-based exploration, report writing and responsible workflows.

• Understand GenAI basics, LLM capabilities, prompt structure, context, constraints, examples and expected output formatting.

• Write prompts for data questions, column explanation, cleaning checklist, EDA plan, visualization ideas and report summaries.

• Use AI to explain Python errors, SQL queries, statistics concepts, ML metrics and dashboard findings.

• Create AI-assisted data storytelling with executive summaries, insight bullets, recommendation drafts and presentation scripts.

• Generate synthetic sample data for practice while understanding privacy and real-data limitations.

• Use AI for documentation such as README files, project summaries, model cards, data dictionaries and portfolio descriptions.

• Learn validation habits: check formulas, rerun code, compare outputs, inspect charts and never trust AI blindly.

• Avoid sharing confidential data, private files, credentials or sensitive business records with AI tools.

• Outcome: use GenAI as a responsible productivity partner while maintaining human judgement.

Build portfolio-ready projects that combine data cleaning, analysis, visualization, ML and GenAI-assisted reporting.

• Plan projects by defining problem statement, dataset source, target audience, success metrics and final deliverables.

• Build business analytics projects such as sales performance, customer analysis, marketing campaign or ecommerce dashboard.

• Build machine learning projects such as churn prediction, price prediction, loan approval, student performance or demand forecasting.

• Include data cleaning, EDA, charts, feature engineering, model training, evaluation, insights and final recommendations.

• Prepare dashboards or visual reports with KPIs, filters, trends, comparisons and summary notes.

• Use GenAI to support documentation, insight wording, dashboard story, model explanation and presentation script.

• Create GitHub-ready project folders with datasets, notebooks, images, README, requirements file and final report.

• Practice explaining workflow, challenges, decisions, model results and business value in interview format.

• Outcome: complete portfolio-worthy projects that demonstrate practical data science and GenAI-enhanced analytics skills.

Prepare for data roles with resume projects, interview practice, portfolio review and professional presentation skills.

• Understand data career paths including data analyst, junior data scientist, BI analyst, ML beginner and AI-enabled analytics roles.

• Prepare resume bullet points for Python, SQL, Excel, dashboarding, statistics, ML, GenAI workflows and project outcomes.

• Build a portfolio with GitHub repositories, dashboards, notebooks, reports, screenshots and clear project explanations.

• Practice interview questions on Python, SQL, statistics, EDA, visualization, ML workflow, model evaluation and GenAI usage.

• Prepare project storytelling using problem, data, cleaning, analysis, model, insight, recommendation and impact format.

• Review common mistakes such as vague project explanation, weak metric understanding, copied code and unclear business value.

• Practice mock interviews, project walkthroughs, dashboard presentation and technical explanation sessions.

• Use GenAI for mock questions, answer improvement, resume refinement, project summary drafting and revision planning.

• Outcome: present yourself confidently for data science, data analyst and GenAI-enabled analytics opportunities.