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Python for Data Analytics Introduction

Python for Data Analytics

Introduction

Python is widely used in analytics because it can:

  • Automate repetitive work (e.g., cleaning the same monthly file)
  • Handle larger datasets than spreadsheets comfortably manage
  • Connect data preparation, statistics, visualisation, and modelling in one workflow
  • Reproduce results: the code is the record of every step

You do not need to become a software engineer to use Python for practical analysis.

Library

Purpose

pandasTabular data: loading, cleaning, grouping, joining
NumPyFast numerical operations
MatplotlibCore plotting library
seabornStatistical charts built on Matplotlib
SciPy / statsmodelsStatistical tests and models
scikit-learnMachine learning

The examples are designed to run in a notebook such as Jupyter or Google Colab. Colab needs no installation.

What you will learn in this module

  • How to use variables, data types, lists, dictionaries, and conditions
  • How to read and inspect data with pandas
  • How to clean missing, duplicated, and inconsistent values
  • How to group data and calculate useful metrics
  • How to create simple charts and keep notebook work reproducible

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Data Analytics Tutorial

Data Analytics Tutorial

MODULE 1 : Foundations of Data Analytics
lessonIntroduction - Foundations of Data Analytics
lessonWhat is Data Analystics
lessonTypes of Data Analytics
lessonThe Data Analytics Workflow
lessonData Types, Sources, and Structure
lessonTurning a Business Request into an Analytical Question
MODULE 2 : Spreadsheets and Practical Data Handling
lessoniIntroduction - Spreadsheets and Practical Data Handling
lessonUnderstanding Spreadsheet Structure
lessonFormulas and Cell References
lessonEssential Functions for Analysis
lessonSorting, Filtering, and Data Validation
lessonLookups and Pivot Tables
MODULE 3 : SQL for Data Analytics
lessonSQL for Data Analytics - Introduction
lessonDatabases and Your First SQL Query
lessonFiltering, Sorting, and Limiting Results
lessonAggregate Functions and GROUP BY
lessonJoining Related Tables
lessonCASE, Subqueries, and CTEs
MODULE 4 : Python for Data Analytics
lessonPython for Data Analytics Introduction
lessonPython Basics for Analysts
lessonConditions, Loops, and Functions
lessonReading and Inspecting Data with pandas
lessonCleaning and Transforming Data
lessonGrouping, Summaries, and Visualisation
MODULE 5 : Statistics for Data Analytics
lessonStatistics for Data Analytics Introduction
lessonDescriptive Statistics
lessonDistributions, Percentiles, and Outliers
lessonSampling and Uncertainty
lessonHypothesis Testing and Confidence Intervals
lessonCorrelation, Regression, and Causation
MODULE 6 : Data Visualization Reporting and Dashboards
lessonData Visualisation, Reporting, and Dashboards Introduction
lessonChoosing the Right Chart
lessonVisual Design and Avoiding Misleading Charts
lessonKPIs and Dashboard Planning
lessonBuilding Reports in BI Tools
lessonPresenting an Insight and Recommendation
MODULE 7 : Exploratory Data Analysis and Data Cleaning
lessonExploratory Data Analysis and Data Cleaning - Introduction
lessonStarting an Exploratory Data Analysis
lessonMissing Values and Their Meaning
lesson Duplicates, Inconsistent Labels, and Data Types
lesson Outliers and Validation Rules
lessonA Reproducible Cleaning Pipeline
MODULE 8 : Business Product and Marketing Analytics
lessonBusiness, Product, and Marketing Analytics Introduction
lessonSales and Revenue Analytics
lessonCustomer Retention and Churn
lessonMarketing Funnel and Campaign Analytics
lessonProduct Analytics and User Journeys
lessonOperational Analytics and Root-Cause Investigation
MODULE 9 : Advanced Analytics - Forecasting and Machine Learning Basics
lessonAdvanced Analytics, Forecasting, and Machine Learning Basics - Introduction
lessonForecasting and Time-Series Basics
lessonRegression and Classification
lessonClustering and Segmentation
lessonModel Evaluation and Choosing Metrics
lessonOverfitting, Leakage, and Responsible Modelling
MODULE 10 : Data Workflows - Warehousing and Professional Practice
lessonData Workflows, Warehousing, and Professional Practice - Introduction
lessonOperational Databases and Data Warehouses
lessonETL, ELT, and Data Pipelines
lessonAutomation, Scheduling, and Monitoring
lessonDocumentation, Version Control, and Collaboration
lessonPrivacy, Governance, and Ethical Practice
MODULE 11 : Final Lesson: Bringing It All Together
lessonFinal Lesson: Bringing It All Together

Learn via Video Course

Data Analysis and Processing with Python using Pandas - Data Science Course Logo

Data Analysis and Processing with Python using Pandas - Data Science Course

5 hrs

Get in Touch For Details

Data Analytics Tutorial

    MODULE 1 : Foundations of Data Analytics
    lessonIntroduction - Foundations of Data Analytics
    lessonWhat is Data Analystics
    lessonTypes of Data Analytics
    lessonThe Data Analytics Workflow
    lessonData Types, Sources, and Structure
    lessonTurning a Business Request into an Analytical Question
    MODULE 2 : Spreadsheets and Practical Data Handling
    lessoniIntroduction - Spreadsheets and Practical Data Handling
    lessonUnderstanding Spreadsheet Structure
    lessonFormulas and Cell References
    lessonEssential Functions for Analysis
    lessonSorting, Filtering, and Data Validation
    lessonLookups and Pivot Tables
    MODULE 3 : SQL for Data Analytics
    lessonSQL for Data Analytics - Introduction
    lessonDatabases and Your First SQL Query
    lessonFiltering, Sorting, and Limiting Results
    lessonAggregate Functions and GROUP BY
    lessonJoining Related Tables
    lessonCASE, Subqueries, and CTEs
    MODULE 4 : Python for Data Analytics
    lessonPython for Data Analytics Introduction
    lessonPython Basics for Analysts
    lessonConditions, Loops, and Functions
    lessonReading and Inspecting Data with pandas
    lessonCleaning and Transforming Data
    lessonGrouping, Summaries, and Visualisation
    MODULE 5 : Statistics for Data Analytics
    lessonStatistics for Data Analytics Introduction
    lessonDescriptive Statistics
    lessonDistributions, Percentiles, and Outliers
    lessonSampling and Uncertainty
    lessonHypothesis Testing and Confidence Intervals
    lessonCorrelation, Regression, and Causation
    MODULE 6 : Data Visualization Reporting and Dashboards
    lessonData Visualisation, Reporting, and Dashboards Introduction
    lessonChoosing the Right Chart
    lessonVisual Design and Avoiding Misleading Charts
    lessonKPIs and Dashboard Planning
    lessonBuilding Reports in BI Tools
    lessonPresenting an Insight and Recommendation
    MODULE 7 : Exploratory Data Analysis and Data Cleaning
    lessonExploratory Data Analysis and Data Cleaning - Introduction
    lessonStarting an Exploratory Data Analysis
    lessonMissing Values and Their Meaning
    lesson Duplicates, Inconsistent Labels, and Data Types
    lesson Outliers and Validation Rules
    lessonA Reproducible Cleaning Pipeline
    MODULE 8 : Business Product and Marketing Analytics
    lessonBusiness, Product, and Marketing Analytics Introduction
    lessonSales and Revenue Analytics
    lessonCustomer Retention and Churn
    lessonMarketing Funnel and Campaign Analytics
    lessonProduct Analytics and User Journeys
    lessonOperational Analytics and Root-Cause Investigation
    MODULE 9 : Advanced Analytics - Forecasting and Machine Learning Basics
    lessonAdvanced Analytics, Forecasting, and Machine Learning Basics - Introduction
    lessonForecasting and Time-Series Basics
    lessonRegression and Classification
    lessonClustering and Segmentation
    lessonModel Evaluation and Choosing Metrics
    lessonOverfitting, Leakage, and Responsible Modelling
    MODULE 10 : Data Workflows - Warehousing and Professional Practice
    lessonData Workflows, Warehousing, and Professional Practice - Introduction
    lessonOperational Databases and Data Warehouses
    lessonETL, ELT, and Data Pipelines
    lessonAutomation, Scheduling, and Monitoring
    lessonDocumentation, Version Control, and Collaboration
    lessonPrivacy, Governance, and Ethical Practice
    MODULE 11 : Final Lesson: Bringing It All Together
    lessonFinal Lesson: Bringing It All Together

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    HCL GUVI (Grab Your Vernacular Imprint) Geek Network Private Limited is a leading online learning and skills development company, incubated by IIT Madras and IIM Ahmedabad.

    Established in 2014 and acquired by the HCL Group in 2022, HCL GUVI is dedicated to providing effective and high-quality learning and skilling programs that transcend language barriers in technology education. HCL GUVI today is trusted by over 4.8 million users, 20 languages and 2500+ corporate partners.

    app storeplay store
    Refer & Earn

    HCL GUVI (Grab Your Vernacular Imprint) Geek Network Private Limited is a leading online learning and skills development company, incubated by IIT Madras and IIM Ahmedabad.

    Established in 2014 and acquired by the HCL Group in 2022, HCL GUVI is dedicated to providing effective and high-quality learning and skilling programs that transcend language barriers in technology education. HCL GUVI today is trusted by over 4.8 million users, 20 languages and 2500+ corporate partners.

    app storeplay storeRefer & Earn
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