Data Analysis

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Duration  5 days – 35 hrs

 

Overview

 

The Data Analysis training course provides participants with the essential skills and knowledge to effectively analyze data and derive valuable insights. Through a combination of lectures, hands-on exercises, and practical examples, participants will learn how to clean and prepare data, perform exploratory data analysis, conduct statistical analysis, and visualize data using popular tools and techniques.

 

Objectives

 

  • Understand the fundamentals of Data Analysis and its importance in decision-making processes.
  • Learn how to clean, prepare, and validate datasets for analysis.
  • Gain proficiency in using tools such as Excel and Python for data manipulation and analysis.
  • Develop skills in exploratory data analysis (EDA) techniques to uncover patterns and trends in data.
  • Learn basic statistical concepts and hypothesis testing methods for data-driven decision-making.
  • Master the art of data visualization to effectively communicate insights and findings.

 

Audience

 

  • Business Professionals: Managers, executives, and analysts in various industries who need to make data-driven decisions to improve operations, optimize processes, and drive business growth.
  • Data Analysts: Individuals whose primary role involves collecting, cleaning, analyzing, and interpreting data to extract insights and inform decision-making within organizations.
  • Data Scientists: Professionals with advanced skills in statistical analysis, machine learning, and programming who work with large and complex datasets to uncover patterns, build predictive models, and drive innovation.
  • Researchers and Academics: Scientists, researchers, and academics across disciplines such as social sciences, economics, healthcare, and environmental studies who use data analysis to conduct research, validate hypotheses, and publish findings.
  • Students and Educators: Students pursuing degrees in fields related to data science, business analytics, statistics, or any discipline that involves quantitative analysis. Educators who teach courses on data analysis, statistics, or research methods.
  • Marketing and Sales Professionals: Marketers and sales professionals who leverage data analysis to understand customer behavior, target audiences more effectively, optimize marketing campaigns, and improve sales strategies.
  • Government and Public Sector Professionals: Public officials, policymakers, and analysts who use data analysis to inform policy decisions, allocate resources efficiently, and address societal challenges.
  • Healthcare Professionals: Doctors, nurses, researchers, and healthcare administrators who utilize data analysis to improve patient outcomes, manage healthcare resources, and advance medical research.
  • Financial Analysts: Professionals in the finance industry who analyze financial data to assess investment opportunities, manage risk, and make informed decisions in areas such as banking, investment management, and corporate finance.
  • Non-profit Organizations: Professionals working in non-profit organizations and NGOs who use data analysis to measure the impact of their programs, identify areas for improvement, and allocate resources effectively to achieve their mission.

 

Pre- requisites 

  • Basic computer literacy
  • Familiarity with Microsoft Excel (for Excel-based exercises)
  • No prior experience with statistical concepts or programming required

Course Content

 

Day 1: Introduction to Data Analysis and Excel Basic

 

  • What is Data Analysis?
  • Importance of Data Analysis in decision-making
  • Introduction to Microsoft Excel for Data Analysis
  • Basic Excel operations: navigation, entering data, formatting
  • Working with Excel formulas and functions
  • Introduction to basic statistical functions in Excel (mean, median, mode, standard deviation)
  • Data visualization in Excel: creating charts and graphs

 

Day 2: Data Cleaning and Preparation in Excel

 

  • Understanding common data quality issues
  • Techniques for cleaning and preparing data in Excel
  • Handling missing values and duplicates
  • Text manipulation and data formatting techniques
  • Using Excel’s data validation tools
  • Practice exercises: cleaning and preparing sample datasets

 

Day 3: Introduction to Data Analysis with Excel

 

  • Overview of Data Analysis tools in Excel (PivotTables, PivotCharts)
  • Creating PivotTables for data summarization and analysis
  • Analyzing data with PivotCharts
  • Introduction to conditional formatting for data visualization
  • Performing basic data analysis tasks using Excel’s built-in features
  • Hands-on exercises: analyzing sample datasets with Excel tools

 

Day 4: Introduction to Data Analysis with Python and Jupyter Notebooks

 

  • Introduction to Python for Data Analysis
  • Setting up Python environment (Anaconda, Jupyter Notebooks)
  • Basic Python programming concepts (variables, data types, lists)
  • Introduction to libraries: NumPy and Pandas for data manipulation
  • Loading and exploring data using Pandas DataFrames
  • Hands-on exercises: basic data manipulation and analysis with Python and Pandas

 

Day 5: Data Visualization and Conclusion

 

  • Introduction to data visualization libraries in Python (Matplotlib, Seaborn)
  • Creating basic plots and charts with Matplotlib
  • Enhancing visualizations with Seaborn
  • Group project: Analyzing and visualizing a real-world dataset using Python
  • Presentation of group projects
  • Conclusion and next steps in the Data Analysis journey

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  • Warehouse supervisors, team leaders, and managers.
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  • Logistics and distribution supervisors.
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  • Supply chain and operations professionals.
  • Personnel responsible for reviewing warehouse controls and performance.
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  • Basic familiarity with warehouse, logistics, or inventory activities.
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Course Outline

  Day 1: Warehouse Processes, Layout, and Storage Controls Module 1: Warehouse Operations and Process Controls  
  • Warehouse roles within the supply chain.
  • End-to-end flow: receiving, put-away, storage, replenishment, picking, packing, and dispatch.
  • Physical stock movement and corresponding system transactions.
  • Responsibilities, authorization, and segregation of duties.
  • Common operational weaknesses and audit evidence.
  Module 2: Warehouse Layout and Storage Systems  
  • Functional zones, movement paths, and space utilization.
  • Receiving, storage, picking, staging, and dispatch areas.
  • Storage options: floor stacking, shelving, pallet racking, and bin storage.
  • Fixed and dynamic location systems.
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  • Layout bottlenecks and their effects on productivity and inventory control.
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  • Purchase orders, delivery documents, and expected receipts.
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  • Put-away rules and location confirmation.
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  Module 4: Storage, Stock Rotation, and Replenishment  
  • Storage discipline and location integrity.
  • FIFO and FEFO principles, applications, and limitations.
  • Batch, lot, and expiry-date management.
  • Controls for slow-moving, obsolete, damaged, and expired stock.
  • Replenishment of picking locations.
  • Supervision and review of stock rotation compliance.
    Day 2: Order Fulfillment and Inventory Accuracy Module 5: Picking Strategies and Order Fulfillment  
  • Discrete, batch, zone, and wave picking.
  • Selecting picking strategies for different order profiles.
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  • Item master data, units of measure, and location records.
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  • Transaction controls for receipts, transfers, issues, returns, and adjustments.
  • Common causes of physical and system stock mismatches.
  • Barcode scanning and WMS control points.
  • Inventory reconciliation and supporting documentation.
  • Measuring inventory accuracy and interpreting discrepancies.
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  • Cycle counting versus full physical inventory.
  • ABC classification and risk-based count priorities.
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  Module 8: Shrinkage Reduction and Inventory Protection  
  • Sources of shrinkage: loss, damage, theft, misplacement, and recording errors.
  • Access controls and accountability for stock movements.
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  • Exception reporting and unusual transaction patterns.
  • Investigating recurring losses and unexplained adjustments.
  • Preventive and detective controls for shrinkage reduction.
    Day 3: Performance Management, Process Review, and Improvement Module 9: Warehouse Productivity and Inventory KPIs  
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  • Put-away productivity and replenishment performance.
  • Picking productivity and order accuracy.
  • Inventory accuracy and cycle count variance.
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  • Defining KPI formulas, data sources, and reporting boundaries.
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  Module 11: Warehouse Improvement Planning  
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  Module 12: Capstone — Warehouse Operations and Inventory Improvement Plan  
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