Duration: 4 Days

Description

This 4-day course introduces the essential concepts, tools, and techniques used in data analysis. Learners begin with foundational knowledge of data types, sources, and ethical considerations, and progressively build fluency in Python programming for data tasks. The course emphasizes practical skills such as data cleaning, basic statistical analysis, and exploratory data analysis (EDA) using Python and NumPy. It concludes with training in communicating insights through clear summaries, visualizations, and audience-focused reporting. By the end, participants will be able to work with real-world datasets and deliver basic analytical insights confidently.

Audience

This course is designed for aspiring data analysts, early-career professionals, and anyone looking to develop core data analysis skills using Python. It’s ideal for learners transitioning from Excel-based analysis or non-technical roles into more data-driven positions, as well as recent graduates or bootcamp learners preparing for entry-level analytics roles.

Objectives

  • Distinguish between data types, structures, and formats
  • Collect, store, and describe data using ethical and legal best practices
  • Write and execute Python code to manipulate and process data
  • Clean, normalize, and perform simple statistical operations on datasets
  • Apply NumPy and built-in tools for numeric computation
  • Conduct basic exploratory data analysis (EDA)
  • Interpret common data visualizations and generate reports
  • Present findings effectively to technical and non-technical audiences

Prerequisites

Participants should have basic familiarity with Python, including variables, data types, and simple loops or conditionals. No prior experience with data analysis or statistics is required. Comfort with general computer use and working with files is expected.

Course Outline

Module 1: Introduction to Data and Data Analysis Concepts

  • Types and Classifications of Data
  • Data Collection, Storage, and Transformation
  • Data Sources and Basic Collection Methods
  • Data Storage Formats and Systems
  • The Data Lifecycle and Its Impact
  • Roles in the Data Domain
  • Types of Analytics and Practical Use Cases
  • Ethics, Privacy, and Legal Compliance in Data

Module 2: Python Basics for Data Analysis

  • Variables, Arithmetic Operations, and Strings
  • Working with Core Data Structures
  • Functions and Code Reusability
  • Control Flow with Conditionals and Loops
  • Error Handling and Exception Management
  • Using Python Modules and Packages

Module 3: Working with Data and Performing Simple Analyses

  • Reading and Writing Data Files
  • Data Cleaning and Preprocessing
  • Data Normalization and Formatting
  • Aggregations and Descriptive Statistics in Python
  • Numerical Operations with NumPy and Built-In Tools
  • Exploratory Data Analysis (EDA) Basics

Module 4: Communicating Insights and Reporting

  • Understanding and Interpreting Basic Visualizations
  • Summarizing Findings with Clarity
  • Create Reports with Visual Support
  • Presenting Data to Diverse Audiences