{"id":28,"date":"2026-04-28T02:07:59","date_gmt":"2026-04-28T02:07:59","guid":{"rendered":"http:\/\/localhost:8080\/?program=python-for-data-science-and-analytics-3-day"},"modified":"2026-04-28T03:28:54","modified_gmt":"2026-04-28T03:28:54","slug":"data-science-using-python-3-day","status":"publish","type":"program","link":"https:\/\/jegan.my\/?program=data-science-using-python-3-day","title":{"rendered":"Data Science using Python &#8211; 3 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>6. 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>Lab 1: Python and NumPy Practice for Data Work<\/h3>\n<ul>\n<li>Variables, loops, and functions<\/li>\n<li>Lists and dictionaries<\/li>\n<li>NumPy calculations<\/li>\n<li>Preparation for pandas work<\/li>\n<\/ul>\n<h2>Day 2 &#8211; Mastering pandas Series and DataFrames<\/h2>\n<h3>8. 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>9. 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>10. 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>11. 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>12. 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>13. 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>14. Working Beyond Excel with pandas<\/h3>\n<ul>\n<li>Spreadsheet formulas vs pandas operations<\/li>\n<li>Automating repetitive report tasks<\/li>\n<li>Handling larger datasets<\/li>\n<li>Repeatable workflows<\/li>\n<li>Reducing human error<\/li>\n<\/ul>\n<h3>Lab 2: Business Reporting with pandas<\/h3>\n<ul>\n<li>Load CSV business data<\/li>\n<li>Clean and standardize the dataset<\/li>\n<li>Filter, sort, group, and summarize<\/li>\n<li>Create a reusable reporting workflow<\/li>\n<\/ul>\n<h2>Day 3 &#8211; Data Analysis, Visualization, and Business Insight<\/h2>\n<h3>16. 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>17. 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>18. 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>19. 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>20. 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>Lab 3: Applied Data Analysis Project<\/h3>\n<ul>\n<li>Prepare and clean a business dataset<\/li>\n<li>Analyze numeric and text data<\/li>\n<li>Create charts for findings<\/li>\n<li>Present business improvement insights<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>This 3-day practical program helps participants use Python for real-world data analysis, reporting, visualization, and insight generation. It is designed for Excel users who want a more powerful, repeatable, and scalable way to work with data.<\/p>\n","protected":false},"featured_media":0,"template":"","class_list":["post-28","program","type-program","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/jegan.my\/index.php?rest_route=\/wp\/v2\/program\/28","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=28"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}