{"id":29,"date":"2026-04-28T03:48:53","date_gmt":"2026-04-28T03:48:53","guid":{"rendered":"http:\/\/localhost:8080\/?program=data-science-using-python-5-day"},"modified":"2026-04-28T03:58:54","modified_gmt":"2026-04-28T03:58:54","slug":"data-science-using-python-5-day","status":"publish","type":"program","link":"https:\/\/jegan.my\/?program=data-science-using-python-5-day","title":{"rendered":"Data Science using Python &#8211; 5 Day"},"content":{"rendered":"<h2>Day 1 &#8211; Python Language Foundations for Data Analysis<\/h2>\n<h3>1. Introduction to Python for Data Work<\/h3>\n<ul>\n<li>Python in data analysis and reporting<\/li>\n<li>Python vs spreadsheet workflows<\/li>\n<li>Notebooks, scripts, and environments<\/li>\n<li>Python workflow for data professionals<\/li>\n<li>Setting up the analysis environment<\/li>\n<\/ul>\n<h3>2. Python Syntax, Variables, and Data Types<\/h3>\n<ul>\n<li>Python syntax basics<\/li>\n<li>Variables and naming rules<\/li>\n<li>Numbers, strings, and booleans<\/li>\n<li>Type conversion<\/li>\n<li>Comments and readable code style<\/li>\n<\/ul>\n<h3>3. Control Structures and Program Flow<\/h3>\n<ul>\n<li>if, elif, and else<\/li>\n<li>for loops and while loops<\/li>\n<li>Looping through collections<\/li>\n<li>break and continue<\/li>\n<li>Simple control flow patterns<\/li>\n<\/ul>\n<h3>4. Python Data Structures for Analysis<\/h3>\n<ul>\n<li>Lists and tuples<\/li>\n<li>Dictionaries<\/li>\n<li>Sets and uniqueness checks<\/li>\n<li>Indexing and slicing<\/li>\n<li>Choosing the right structure<\/li>\n<\/ul>\n<h3>5. Functions, Modules, and Reusable Analysis Logic<\/h3>\n<ul>\n<li>Defining and calling functions<\/li>\n<li>Parameters and return values<\/li>\n<li>Scope and variable visibility<\/li>\n<li>Importing modules and libraries<\/li>\n<li>Reusable analysis functions<\/li>\n<\/ul>\n<h3>Lab 1: Python Practice for Data Work<\/h3>\n<ul>\n<li>Variables, loops, and functions<\/li>\n<li>Lists and dictionaries<\/li>\n<li>Simple reusable scripts<\/li>\n<li>Preparation for data collection work<\/li>\n<\/ul>\n<h2>Day 2 &#8211; Data Collection, Integration, and Pipeline Design<\/h2>\n<h3>6. Data Collection Fundamentals<\/h3>\n<ul>\n<li>Structured vs semi-structured data sources<\/li>\n<li>Files, databases, and APIs as data sources<\/li>\n<li>Batch vs scheduled data collection<\/li>\n<li>Centralized data collection concepts<\/li>\n<li>Designing a practical collection workflow<\/li>\n<\/ul>\n<h3>7. Working with SQL Data Sources<\/h3>\n<ul>\n<li>SQL databases in analytics workflows<\/li>\n<li>Connecting Python to SQL data sources<\/li>\n<li>Reading query results into Python<\/li>\n<li>Basic filtering and extraction strategies<\/li>\n<li>Moving database records into analysis pipelines<\/li>\n<\/ul>\n<h3>8. Working with NoSQL Data Sources<\/h3>\n<ul>\n<li>NoSQL concepts for analysts<\/li>\n<li>Document-style data and nested structures<\/li>\n<li>Reading NoSQL-style records into Python<\/li>\n<li>Flattening records for analysis<\/li>\n<li>Choosing SQL vs NoSQL sources<\/li>\n<\/ul>\n<h3>9. Collecting Data from APIs<\/h3>\n<ul>\n<li>API basics for data professionals<\/li>\n<li>Requests, endpoints, and JSON responses<\/li>\n<li>Authentication basics and API keys<\/li>\n<li>Loading API responses into Python<\/li>\n<li>Handling pagination and repeated calls<\/li>\n<\/ul>\n<h3>10. Building a Simple Data Pipeline<\/h3>\n<ul>\n<li>Extract, transform, and load concepts<\/li>\n<li>Bringing multiple sources into one location<\/li>\n<li>Automating collection with Python scripts<\/li>\n<li>Preparing pipeline output for pandas<\/li>\n<li>Monitoring and maintaining simple pipelines<\/li>\n<\/ul>\n<h3>Lab 2: Create a Practical Data Collection Pipeline<\/h3>\n<ul>\n<li>Read data from files and an API<\/li>\n<li>Load data into a common structure<\/li>\n<li>Standardize collected records<\/li>\n<li>Produce a pipeline-ready dataset<\/li>\n<\/ul>\n<h2>Day 3 &#8211; Data Science Foundations with Python<\/h2>\n<h3>11. NumPy Foundations for Numerical Data<\/h3>\n<ul>\n<li>Why NumPy matters<\/li>\n<li>Creating arrays<\/li>\n<li>Vectorized operations<\/li>\n<li>Array indexing and slicing<\/li>\n<li>Basic numerical calculations<\/li>\n<\/ul>\n<h3>12. Introduction to pandas and Why It Matters<\/h3>\n<ul>\n<li>What pandas is<\/li>\n<li>Why pandas beats manual spreadsheet work<\/li>\n<li>Series and DataFrame concepts<\/li>\n<li>Business use cases for pandas<\/li>\n<li>pandas for reporting productivity<\/li>\n<\/ul>\n<h3>13. pandas Series Fundamentals<\/h3>\n<ul>\n<li>Creating a Series<\/li>\n<li>Series indexing and selection<\/li>\n<li>Filtering Series data<\/li>\n<li>Applying functions to a Series<\/li>\n<li>Missing values in a Series<\/li>\n<\/ul>\n<h3>14. DataFrame Creation and Structure<\/h3>\n<ul>\n<li>Creating DataFrames<\/li>\n<li>Rows, columns, index, and data types<\/li>\n<li>Shape, columns, and metadata<\/li>\n<li>Reading CSV and Excel-style data<\/li>\n<li>Saving DataFrames<\/li>\n<\/ul>\n<h3>15. Data Cleaning with pandas<\/h3>\n<ul>\n<li>Nulls, blanks, and inconsistent values<\/li>\n<li>Handling missing data<\/li>\n<li>Duplicates and formatting issues<\/li>\n<li>Renaming and standardizing columns<\/li>\n<li>Data type conversion<\/li>\n<\/ul>\n<h3>Lab 3: Data Science Preparation with pandas<\/h3>\n<ul>\n<li>Load collected data into pandas<\/li>\n<li>Clean and standardize the dataset<\/li>\n<li>Prepare analysis-ready structures<\/li>\n<li>Create reusable preparation steps<\/li>\n<\/ul>\n<h2>Day 4 &#8211; Data Analytics, Exploration, and Insight Generation<\/h2>\n<h3>16. Selecting, Filtering, and Transforming DataFrames<\/h3>\n<ul>\n<li>Column and subset selection<\/li>\n<li>loc and iloc<\/li>\n<li>Conditional filtering<\/li>\n<li>Sorting and resetting indexes<\/li>\n<li>Calculated columns and transformations<\/li>\n<\/ul>\n<h3>17. Aggregation, Grouping, and Reshaping<\/h3>\n<ul>\n<li>groupby summaries<\/li>\n<li>sum, mean, count, min, and max<\/li>\n<li>Value counts and frequencies<\/li>\n<li>Pivot-style analysis<\/li>\n<li>Merging and joining datasets<\/li>\n<\/ul>\n<h3>18. Exploratory Data Analysis for Business Decisions<\/h3>\n<ul>\n<li>Patterns, distributions, and anomalies<\/li>\n<li>Trends, gaps, and outliers<\/li>\n<li>Descriptive statistics<\/li>\n<li>Summaries for deeper analysis<\/li>\n<li>Turning observations into questions<\/li>\n<\/ul>\n<h3>19. Numerical Data Analysis<\/h3>\n<ul>\n<li>Totals, averages, and ratios<\/li>\n<li>Category and segment comparisons<\/li>\n<li>Top and low performers<\/li>\n<li>Practical correlation ideas<\/li>\n<li>Interpreting numeric findings<\/li>\n<\/ul>\n<h3>20. Text Data Analysis and Sentiment Analysis<\/h3>\n<ul>\n<li>Text analysis in business<\/li>\n<li>Cleaning text data<\/li>\n<li>Word frequency and keyword patterns<\/li>\n<li>Basic sentiment analysis workflow<\/li>\n<li>Customer feedback insights<\/li>\n<\/ul>\n<h3>Lab 4: Applied Data Analytics<\/h3>\n<ul>\n<li>Analyze numeric and text data<\/li>\n<li>Compare categories and segments<\/li>\n<li>Extract sentiment and keyword patterns<\/li>\n<li>Present analytical findings<\/li>\n<\/ul>\n<h2>Day 5 &#8211; Publishing Insights, Web Reports, and Dashboards<\/h2>\n<h3>21. Data Visualization with Matplotlib and Seaborn<\/h3>\n<ul>\n<li>Choosing the right chart<\/li>\n<li>Bar, line, scatter, and histogram charts<\/li>\n<li>Seaborn basics<\/li>\n<li>Trends, comparisons, and distributions<\/li>\n<li>Business presentation visuals<\/li>\n<\/ul>\n<h3>22. Turning Analysis into Productivity and Business Improvement<\/h3>\n<ul>\n<li>Improving reporting speed and quality<\/li>\n<li>Finding inefficiencies and bottlenecks<\/li>\n<li>Supporting business decisions<\/li>\n<li>Repeatable analysis for productivity<\/li>\n<li>Data-driven confidence<\/li>\n<\/ul>\n<h3>23. Reporting Output Design<\/h3>\n<ul>\n<li>From analysis to publishable output<\/li>\n<li>Choosing metrics for stakeholders<\/li>\n<li>Structuring business-friendly reports<\/li>\n<li>Combining text, tables, and charts<\/li>\n<li>Designing repeatable reporting outputs<\/li>\n<\/ul>\n<h3>24. Publishing Analytical Results to Web Reports<\/h3>\n<ul>\n<li>Static report concepts<\/li>\n<li>Exporting analysis outputs for the web<\/li>\n<li>HTML-based reporting ideas<\/li>\n<li>Organizing sections and visuals clearly<\/li>\n<li>Sharing report output with business teams<\/li>\n<\/ul>\n<h3>25. Dashboard Fundamentals<\/h3>\n<ul>\n<li>What makes a useful dashboard<\/li>\n<li>KPI and metric presentation<\/li>\n<li>Charts, filters, and drill-down thinking<\/li>\n<li>Operational vs management dashboards<\/li>\n<li>Dashboard usability for decision makers<\/li>\n<\/ul>\n<h3>26. Building a Simple Python-Driven Reporting View<\/h3>\n<ul>\n<li>Preparing pandas outputs for presentation<\/li>\n<li>Generating chart-ready datasets<\/li>\n<li>Creating simple report pages or dashboard views<\/li>\n<li>Refreshing outputs from updated data<\/li>\n<li>Turning analysis into shareable artifacts<\/li>\n<\/ul>\n<h3>27. Final Capstone Project<\/h3>\n<ul>\n<li>Collect data from multiple sources<\/li>\n<li>Prepare the data with Python and pandas<\/li>\n<li>Perform business-focused analysis<\/li>\n<li>Create charts and insights<\/li>\n<li>Publish the results as a report or dashboard-style output<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>This 5-day applied program builds practical Python data science capability across data preparation, pandas, analysis, visualization, data pipelines, and publishing analytical outputs as usable business reports and dashboards.<\/p>\n","protected":false},"featured_media":0,"template":"","class_list":["post-29","program","type-program","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/jegan.my\/index.php?rest_route=\/wp\/v2\/program\/29","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/jegan.my\/index.php?rest_route=\/wp\/v2\/program"}],"about":[{"href":"https:\/\/jegan.my\/index.php?rest_route=\/wp\/v2\/types\/program"}],"wp:attachment":[{"href":"https:\/\/jegan.my\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=29"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}