DDuration 2 days – 14 hrs
Overview
This foundational training course introduces participants to the core concepts of data analytics, including data types, data structures, and essential statistical techniques commonly used in Business Intelligence (BI). Learners will gain hands-on exposure to the data analysis process, including data cleaning, exploration, visualization, and interpretation. Ideal for individuals seeking to build a strong analytical foundation to support data-driven decision-making within their organizations.
Objectives
- Understand the fundamentals of data analytics and its importance in business contexts
- Identify various types of data and distinguish between structured and unstructured data
- Apply basic statistical techniques for data analysis
- Perform exploratory data analysis (EDA)
- Interpret data using charts, summaries, and key performance indicators
- Gain familiarity with common tools and environments used in data analytics (e.g., Excel, Power BI, or Python basics)
Audience
- Business analysts
- Aspiring data analysts
- Entry-level professionals in marketing, finance, operations, or IT
- Managers who wish to develop a data-driven mindset
- Anyone interested in understanding the basics of data analytics
Pre- requisites
- Basic computer literacy
- Familiarity with Microsoft Excel (preferred but not required)
- No prior experience in data analytics is necessary
Course Content
Day 1: Introduction to Data Analytics & Data Fundamentals
What is Data Analytics?
- Role of analytics in business
- Analytics types: Descriptive, Diagnostic, Predictive, Prescriptive
Data Types and Structures
- Structured vs. unstructured data
- Qualitative vs. quantitative data
- Nominal, ordinal, interval, ratio scales
The Data Analytics Lifecycle
- Collection, preparation, exploration, modeling, interpretation
Basic Statistics for Analysis
- Mean, median, mode
- Standard deviation and variance
- Correlation vs. causation
Workshop/Activity:
- Analyzing sample datasets in Excel
Day 2: Exploratory Data Analysis (EDA) and Visualization
Data Cleaning and Preparation
- Handling missing or inconsistent data
- Data normalization and transformation
Exploratory Data Analysis (EDA)
- Identifying patterns and outliers
- Using summary statistics
Data Visualization Techniques
- Bar charts, histograms, line charts, pie charts
- Choosing the right chart for your data
Introduction to BI Tools (Demo-based)
- Overview of Excel PivotTables, Power BI, or basic Python (based on delivery)
Capstone Exercise:
- Perform EDA and visualization on a business dataset
- Present key findings
