Data Analytics Mastery: Turning Insights into Action
June 18 @ 9:00 am - June 20 @ 5:00 pm
Course Overview
This 3-day intensive training program provides participants with a practical foundation in data analytics, focusing on transforming raw data into actionable insights. The course covers data collection, cleaning, analysis, visualization, and predictive modeling using tools such as Excel, SQL, Python, and Tableau/Power BI.
Participants will engage in hands-on activities, case studies, and a mini-project to develop real-world skills and confidently apply data analytics techniques in their roles.
Course Objectives
By the end of this course, participants will:
- Understand the data analytics lifecycle and its business applications.
- Perform data cleaning, transformation, and analysis using popular tools.
- Create compelling visualizations and interactive dashboards.
- Build and evaluate predictive models for forecasting and decision-making.
- Translate analytical insights into business strategies.
Learning Outcomes
Participants will:
- Technical Skills
- Work with Excel, SQL, Python (Pandas, Matplotlib), and Tableau/Power BI.
- Conduct data analysis and create dashboards.
- Perform regression modeling and trend analysis.
- Analytical Thinking
- Develop a structured approach to problem-solving using data.
- Gain expertise in statistical interpretation and identifying patterns.
- Practical Application
- Apply concepts to a mini-project based on a real-world dataset.
- Present insights through storytelling techniques and data visualizations.
Target Participants
- Business analysts, managers, and decision-makers.
- Professionals transitioning into data analytics roles.
- Entrepreneurs and startups aiming to make data-driven decisions.
- Students or recent graduates looking to enhance their analytics skills.
Prerequisites: Basic familiarity with Excel and statistics is recommended.
Course Duration
- 3 Days (Full-Time)
- Daily Schedule: 9:00 AM – 5:00 PM (8 hours/day)
Training Methodology
- Interactive Lectures – Concepts explained with examples.
- Hands-On Labs – Tool-based exercises to build technical expertise.
- Case Studies – Business scenarios to practice analysis and decision-making.
- Mini-Project – Final project to consolidate learning and skills.
- Group Discussions – Peer collaboration and idea sharing.
Day 1: Fundamentals of Data Analytics
Session 1: Introduction to Data Analytics
- What is Data Analytics?
- Types: Descriptive, Diagnostic, Predictive, Prescriptive Analytics.
- Applications in Business and Industries.
Session 2: Data Acquisition and Cleaning
- Data Types and Sources.
- Collecting and Importing Data (Excel, CSV, SQL).
- Data Cleaning Techniques: Missing Values, Outliers, and Standardization.
Session 3: Exploratory Data Analysis (EDA)
- Statistical Measures: Mean, Median, Mode, Variance.
- Visualization Techniques: Bar Charts, Histograms, Scatter Plots, Boxplots.
- Tools: Excel, Python (Pandas, Matplotlib).
Lab Activity:
- Practice data cleaning and analysis with a sample dataset.
Day 2: Data Analysis and Modeling
Session 4: Statistical and Predictive Modeling
- Correlation and Regression Analysis.
- Hypothesis Testing and A/B Testing.
- Introduction to Machine Learning (Decision Trees, Clustering).
Session 5: Hands-On Tools for Analysis
- Excel Advanced Functions (Pivot Tables, Solver).
- SQL Queries for Data Retrieval.
- Python Libraries (Pandas, NumPy, Seaborn).
Session 6: Data Visualization and Storytelling
- Visualization Principles: Simplicity and Clarity.
- Tools: Tableau, Power BI Basics.
- Building Dashboards and Reports.
Lab Activity:
- Create dashboards using Tableau/Power BI with visual insights.
Day 3: Advanced Analytics and Project Work
Session 7: Advanced Analytics Techniques
- Introduction to Big Data Tools (Hadoop, Spark).
- Real-Time Analytics and Data Pipelines.
- AI and NLP Basics for Sentiment Analysis.
Session 8: Insights to Action
- Converting Findings into Business Strategies.
- Presenting Insights to Stakeholders.
- Change Management for Data-Driven Decision-Making.
Mini-Capstone Project:
- Analyze a business problem using a dataset.
- Perform EDA, build a predictive model, and create a visualization report.
- Present findings to the class for peer and instructor feedback.
