Data Analysis with Python Training

RexGalaxy Academy's Data Analysis with Python Training is a practical, project-based program designed for learners who want to analyze data, clean datasets, create visual reports and build business insights using Python. The course is ideal for students, beginners, working professionals, Excel users, reporting professionals and anyone who wants to move into data analyst or analytics-related roles.

This training focuses on the full data analysis workflow: understanding business questions, collecting data, cleaning messy datasets, transforming data, exploring patterns, visualizing insights, writing summaries and presenting findings clearly. Learners work with Python, NumPy, Pandas, Matplotlib, Seaborn, Excel-style data, SQL basics and real-world datasets.

The course is built around practical analytics thinking. Students do not only learn commands; they learn how to ask better questions, identify missing values, detect duplicates, summarize data, compare categories, create charts, explain trends and prepare reports that help decision-making. Each module includes hands-on exercises and examples from sales, marketing, finance, HR, ecommerce, education and operations datasets.

Students should be able to:

• Use Python confidently for data handling, cleaning, transformation and analysis.

• Work with NumPy and Pandas to inspect, filter, group, merge and summarize datasets.

• Perform exploratory data analysis to identify trends, outliers, patterns and business insights.

• Create meaningful charts using Matplotlib and Seaborn for visual storytelling.

• Use SQL basics and Excel-style thinking to strengthen practical analytics workflows.

• Build portfolio-ready data analysis projects with notebooks, charts, reports and interview-ready explanations.

Start with the foundation of data analysis and understand how analysts convert raw data into useful insights.

• Understand what data analysis is and how it supports business decisions, reporting and problem solving.

• Learn the role of a data analyst and how it differs from data scientist, BI analyst and business analyst roles.

• Understand the data analysis lifecycle: problem statement, data collection, cleaning, exploration, visualization and reporting.

• Learn the difference between raw data, clean data, structured data, unstructured data, metrics and KPIs.

• Practice asking useful business questions before starting analysis.

• Understand common analytics domains such as sales, marketing, finance, HR, ecommerce, education and operations.

• Learn how to identify columns, records, categories, numerical fields, date fields and target metrics.

• Understand data quality issues such as missing values, duplicates, inconsistent naming and wrong formats.

• Practice reading a dataset and creating a basic analysis plan.

• Outcome: build a clear analytics mindset before using Python tools deeply.

Build Python programming skills required for data analysis and automation tasks.

• Learn Python syntax, variables, data types, operators, input-output and comments.

• Understand conditions, loops, functions and reusable logic for repeated analysis tasks.

• Work with strings, lists, tuples, dictionaries and sets used in practical data handling.

• Practice indexing, slicing, iteration and basic data manipulation patterns.

• Learn error handling basics and how to read Python error messages confidently.

• Understand modules, packages and importing libraries for analysis work.

• Practice writing small programs for calculations, filtering values and summarizing simple lists.

• Learn clean notebook habits including markdown notes, meaningful variable names and organized outputs.

• Use Python to solve beginner analytics exercises with business-style examples.

• Outcome: write Python code confidently enough to move into data libraries and projects.

Learn how to work with files, notebooks and common data formats used by analysts.

• Understand Jupyter Notebook workflow including code cells, markdown cells, outputs and charts.

• Learn how notebooks are used for analysis, explanation, documentation and portfolio projects.

• Work with CSV, Excel and text data awareness for real-world datasets.

• Practice reading files, checking file paths, handling encoding issues and saving outputs.

• Understand common data types such as integers, floats, strings, booleans, dates and categories.

• Learn type conversion and why wrong data types affect calculations and filtering.

• Practice creating small datasets manually and loading external datasets.

• Understand data dictionaries and why column descriptions are important before analysis.

• Learn how to organize project folders with data, notebooks, images and reports.

• Outcome: handle analysis files and notebooks in a professional, project-ready way.

Use NumPy to understand numerical operations and array-based data handling.

• Understand what NumPy is and why it is useful for numerical computing in Python.

• Learn arrays, dimensions, shapes, indexing, slicing and vectorized operations.

• Perform mathematical operations such as addition, subtraction, multiplication, division and aggregation.

• Use functions for mean, median awareness, min, max, sum, standard deviation and basic statistics.

• Understand broadcasting concepts at a beginner level for efficient calculations.

• Practice filtering arrays using conditions and boolean masks.

• Learn how NumPy supports Pandas, machine learning and scientific computing workflows.

• Practice examples using sales numbers, marks, expenses and performance metrics.

• Understand when NumPy is useful directly and when Pandas is more suitable.

• Outcome: build a numerical foundation for data analysis and Pandas workflows.

Learn Pandas, the most important Python library for practical data analysis.

• Understand Series and DataFrame structures and how they represent tabular data.

• Load data from CSV and Excel files into Pandas DataFrames.

• Inspect datasets using head, tail, shape, columns, info, describe and sample.

• Select rows and columns using labels, indexes, conditions and multiple filters.

• Sort data, rename columns, change data types and create calculated columns.

• Use value counts, unique values and basic summaries to understand categories.

• Group data using groupby to calculate totals, averages, counts and performance metrics.

• Merge, join and concatenate datasets for multi-table analysis awareness.

• Export cleaned or summarized data back to CSV or Excel format.

• Outcome: use Pandas confidently to inspect, filter, summarize and transform datasets.

Clean and prepare messy datasets so they become reliable for analysis and reporting.

• Identify missing values, duplicate rows, inconsistent categories and incorrect formats.

• Handle null values using removal, replacement, filling and business-rule-based decisions.

• Remove duplicates and understand when duplicate records may or may not be valid.

• Clean text data by trimming spaces, changing case, replacing values and standardizing labels.

• Convert date columns, extract year/month/day and create time-based features.

• Detect outliers using summaries, charts and business understanding.

• Create new columns such as profit, discount percentage, age group, month name and status flags.

• Prepare datasets for EDA by selecting useful columns and removing unnecessary noise.

• Document cleaning steps clearly so analysis remains understandable and repeatable.

• Outcome: convert raw messy data into clean, analysis-ready datasets.

Perform exploratory data analysis to discover patterns, trends and meaningful insights.

• Understand EDA purpose and how it helps analysts understand data before final reporting.

• Create summary statistics for numerical and categorical columns.

• Analyze trends over time using date-based grouping and time period comparisons.

• Compare categories such as region, product, department, campaign, gender or customer segment.

• Identify top-performing and low-performing groups using sorting and aggregation.

• Study relationships between variables using correlation awareness and grouped comparisons.

• Detect outliers, unusual values, missing patterns and possible data quality issues.

• Write observation notes that explain what the analysis shows in business language.

• Practice EDA on sales, HR, marketing, finance, education and ecommerce datasets.

• Outcome: find useful insights and prepare clear analysis findings from real datasets.

Create charts and visual stories using Matplotlib and Seaborn.

• Understand why visualization is important for communicating analysis results clearly.

• Learn chart selection for comparisons, trends, distributions, relationships and proportions.

• Create bar charts, line charts, histograms, scatter plots, box plots and heatmaps.

• Use Matplotlib for chart customization including titles, labels, colors, size and legends.

• Use Seaborn for cleaner statistical visualizations and faster chart creation.

• Visualize sales trends, category performance, customer behavior and distribution patterns.

• Learn how to avoid chart clutter, misleading scales and unreadable visual reports.

• Add chart interpretation notes to explain insights below visuals.

• Prepare charts for reports, dashboards, presentations and portfolio notebooks.

• Outcome: create meaningful visualizations that support business storytelling.

Learn statistics basics required to interpret data accurately and avoid weak conclusions.

• Understand mean, median, mode, minimum, maximum, range and percentile concepts.

• Learn variance and standard deviation awareness for understanding spread in data.

• Understand distributions, skewness, outliers and why data shape matters.

• Learn probability basics and uncertainty awareness in business data.

• Understand correlation and why correlation does not always mean causation.

• Compare groups using averages, counts, percentages and change over time.

• Learn sampling, bias and why poor data collection affects analysis quality.

• Use Python and Pandas to calculate statistics from real datasets.

• Practice interpreting statistics in simple business language instead of only formulas.

• Outcome: use statistical thinking to make analysis more accurate and trustworthy.

Connect Python analysis with SQL, Excel-style thinking and practical business reporting.

• Understand why analysts often use Python, SQL and Excel together in real jobs.

• Learn SQL basics including SELECT, WHERE, ORDER BY, GROUP BY and aggregation awareness.

• Understand tables, rows, columns, primary keys and joins at a beginner level.

• Practice translating SQL-style questions into Pandas operations and vice versa.

• Use Excel-style thinking such as filters, pivot tables, formulas and summary reports.

• Create KPI reports such as total sales, average order value, conversion rate and monthly growth.

• Build business summaries with clear headings, charts, observations and recommendations.

• Learn reporting structure: objective, data used, key metrics, insights, conclusion and next steps.

• Practice creating analysis reports for sales, HR, finance and marketing scenarios.

• Outcome: connect Python analysis with common workplace reporting and SQL workflows.

Build real-world projects that show complete data analysis workflow from raw data to insight.

• Plan projects by defining problem statement, dataset source, target audience and expected output.

• Build a sales analysis project with revenue trends, category performance, regional insights and recommendations.

• Build an HR analysis project with employee counts, attrition awareness, department comparison and workforce metrics.

• Build a marketing or ecommerce project with campaign performance, customer segments and conversion insights.

• Include data cleaning, EDA, charts, summary tables and final observations in every project.

• Create notebook documentation with markdown explanations and step-by-step analysis flow.

• Export charts, cleaned data and summary reports for portfolio presentation.

• Learn how to explain project decisions, data limitations and business recommendations.

• Review projects using a checklist for clarity, accuracy, visuals, structure and insight quality.

• Outcome: complete portfolio-ready data analysis projects using Python.

Prepare for data analyst roles with portfolio, resume, interview practice and presentation skills.

• Understand entry-level data analyst expectations and common job requirements.

• Prepare resume bullet points for Python, Pandas, NumPy, EDA, visualization, SQL basics and reporting projects.

• Create GitHub repositories with notebooks, datasets, images, README files and clear project summaries.

• Practice explaining projects using problem, data, cleaning, analysis, insights and recommendation format.

• Prepare interview questions on Python, Pandas, statistics, SQL basics, visualization and data cleaning.

• Learn how to present charts and insights confidently to non-technical audiences.

• Review common mistakes such as unclear notebooks, weak conclusions, missing README and copied analysis.

• Create a final portfolio checklist with project links, screenshots, resume points and interview notes.

• Practice mock project walkthroughs and analytics storytelling.

• Outcome: present yourself confidently for data analysis internships, jobs and analytics projects.