This course focuses on practical and widely used data analytics techniques to extract meaningful insights from data. Learners will explore the end-to-end data analysis process, including data preparation, exploration, modeling, and interpretation. Emphasis is placed on using real-world data sets and tools such as Excel, Python, or R to apply statistical, predictive, and visual analysis techniques. By the end of the course, students will be able to apply analytical methods to solve business, scientific, and operational problems through data-driven decision-making.
Topics Covered:
- Introduction to the Data Analysis Process
(Data lifecycle, goals, and applications) - Data Cleaning and Preprocessing
(Handling missing values, outliers, and data normalization) - Exploratory Data Analysis (EDA)
(Descriptive statistics, distributions, visualizations) - Statistical Techniques for Analytics
(Correlation, hypothesis testing, confidence intervals) - Predictive Analytics
(Linear regression, logistic regression, decision trees) - Clustering and Classification Techniques
(K-means, hierarchical clustering, k-NN, Naive Bayes) - Time Series Analysis Basics
(Trend analysis, seasonality, forecasting techniques) - Data Visualization Techniques
(Using charts, dashboards, and storytelling with data) - Introduction to Tools
(Excel, Python with pandas/matplotlib/seaborn, or R) - Case Studies and Capstone Project
(Applying techniques to solve real-world problems)
Who Is This Course For?
This course is ideal for:
- Aspiring data analysts, business analysts, and junior data scientists
- Professionals looking to upskill and apply data analysis in their roles
- Students in business, economics, computer science, or related fields
- Individuals with basic knowledge of statistics or programming (optional but helpful)
- Anyone seeking to make data-driven decisions in their personal or professional life