Course Description

Course Overview

The Data Analytics course provides a comprehensive understanding of the principles and techniques involved in analyzing and interpreting data to derive meaningful insights. This program is suitable for individuals with or without prior programming knowledge, making it accessible to a wide range of learners. The course covers various aspects of data analytics, including data exploration, data visualization, statistical analysis, and data-driven decision-making. Students will learn how to apply data analytics tools and techniques to real-world datasets and develop skills to extract valuable insights from data.These concepts will be explored in-depth through an applied learning approach, which includes sessions facilitated by leading practitioners and real-world industry projects. This practical approach ensures that learners gain hands-on experience and can immediately apply their newly acquired knowledge and skills in their professional roles.

Prerequisites

• Basic understanding of mathematics and statistics
• Familiarity with spreadsheets and data manipulation
• Basic knowledge of programming concepts (preferred but not mandatory)
• Curiosity and eagerness to work with data

Methodology

The course adopts a combination of theoretical lectures, practical exercises, and hands-on labs to provide a well-rounded learning experience. The lectures cover the fundamental concepts and methodologies of data analytics, while the practical exercises and labs allow students to apply the learned techniques to real-world datasets. The course encourages active participation, critical thinking, and problem-solving skills.

Course Outline

Introduction to Data Analytics

Overview of data analytics and its applications

Role of data analytics in decision-making

Ethical considerations in data analytics

Data Exploration and Preparation

Data types and data quality assessment

Data cleaning and handling missing values

Data transformation and feature engineering

Exploratory Data Analysis

Descriptive statistics and data visualization

Data summarization and aggregation techniques

Identifying patterns and outliers in data

Statistical Analysis for Data Analytics

Probability and probability distributions

Statistical inference and hypothesis testing

Correlation and regression analysis

Data Visualization for Insights

Principles of effective data visualization

Visualization techniques and tools (e.g., Tableau, Matplotlib)

Storytelling with data and communicating insights

Predictive Analytics

Introduction to predictive modeling

Supervised learning algorithms (e.g., linear regression, decision trees, logistic regression)

Model evaluation and validation techniques

Advanced Analytics Techniques

Clustering and segmentation analysis

Text mining and sentiment analysis

Association rule mining and market basket analysis

Data-Driven Decision Making

Decision analysis and optimization

Prescriptive analytics and decision support systems

Incorporating analytics into business processes

Outcome

Upon completing the course, students will:

• Understand the fundamental concepts, methodologies, and techniques of data analytics.
• Be proficient in data exploration, data visualization, statistical analysis, and predictive modeling.
• Gain hands-on experience with data analytics tools and software (e.g., Excel, Tableau, Python libraries).
• Be able to extract insights and meaningful patterns from data to support decision-making processes.
• Develop skills in storytelling with data and effective communication of insights.
• Apply data analytics techniques to solve real-world problems and address business challenges.

Labs

The course includes hands-on labs and projects to reinforce the theoretical concepts and provide practical experience. The labs may include:

• Exploratory data analysis on real-world datasets using statistical techniques and visualizations.
• Data cleaning and preprocessing tasks to prepare data for analysis.
• Building predictive models using regression or classification algorithms.
• Creating interactive dashboards and visualizations using data analytics tools like Tableau.
• Conducting sentiment analysis on textual data and extracting meaningful insights.
• Analyzing customer segmentation and patterns through clustering techniques.