About corse

Data science has emerged as a crucial discipline that empowers organizations to make informed decisions by analyzing and interpreting complex data sets. This course on Data Science for Decision Making aims to equip participants with the essential skills and knowledge to leverage data effectively. With the rapid advancement of technology and the proliferation of data, professionals across various sectors are increasingly tasked with making data-driven decisions. This training will focus on the practical applications of data science, providing attendees with the tools to transform raw data into actionable insights. Throughout the course, participants will engage with real-world case studies and hands-on exercises that illustrate the power of data in decision-making processes. Emphasizing a blend of theoretical foundations and practical applications, this program is designed to enhance critical thinking and analytical abilities. By the end of the training, attendees will not only understand key data science concepts but also develop a strategic mindset necessary for utilizing data as a valuable asset in their organizations.  

The Objectives

• Understand fundamental data science concepts and techniques.
• Develop skills in data analysis and visualization.
• Gain proficiency in using data science tools and software.
• Learn to apply statistical methods for business decision-making.
• Enhance critical thinking and problem-solving abilities.
• Create actionable insights from data-driven findings.

Training Methodology

The training will employ a combination of lectures, hands-on workshops, group discussions, and real-life case studies. Participants will engage in collaborative learning, allowing them to share insights and experiences. Practical exercises will ensure that attendees can apply theoretical knowledge to concrete situations, reinforcing their understanding of data science principles.

WHO SHOULD ATTEND

This course is designed for professionals across various industries, including managers, analysts, and decision-makers who wish to enhance their data literacy. It is ideal for individuals who are involved in strategic planning, marketing, finance, and operations and seek to integrate data science into their decision-making processes. No prior data science experience is required, making this course accessible to a wide range of participants.

Course Outlines

Day 1: Introduction to Data Science

• Overview of data science and its significance in decision-making.
• Key concepts: data types, data structures, and data sources.
• Introduction to the data science lifecycle.
• Understanding the role of data in business strategy.
• Tools and technologies commonly used in data science.
• Setting expectations for the course.

Day 2: Data Collection and Preparation

• Techniques for data collection and sampling methods.
• Data cleaning: identifying and managing missing values.
• Data transformation and normalization processes.
• Exploring data types and structures in depth.
• Introduction to data wrangling techniques.
• Hands-on exercise: preparing a dataset for analysis.

Day 3: Exploratory Data Analysis (EDA)

• Importance of EDA in the data science process.
• Techniques for visualizing data: charts and graphs.
• Identifying patterns and trends in data.
• Statistical measures: mean, median, mode, and standard deviation.
• Hands-on exercise: conducting EDA on a sample dataset.
• Interpreting results to inform decision-making.

Day 4: Statistical Analysis for Decision Making

• Introduction to inferential statistics and hypothesis testing.
• Understanding confidence intervals and p-values.
• Correlation vs. causation in data analysis.
• Using statistical methods to support business decisions.
• Hands-on exercise: applying statistical tests to real-world scenarios.
• Case study analysis: making decisions based on statistical findings.

Day 5: Predictive Modeling Techniques

• Overview of predictive modeling and its applications.
• Introduction to regression analysis and its types.
• Understanding classification techniques: decision trees and logistic regression.
• Evaluating model performance: metrics and validation techniques.
• Hands-on exercise: building a simple predictive model.
• Discussion on ethical considerations in predictive analytics.

Training Method?

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a copy of the presentation
• Slides and handouts

Training Method?

The course agenda will be as follows:

• Technical Session 30-10.00 am
• Coffee Break 00-10.15 am
• Technical Session 15-12.15 noon
• Coffee Break 15-12.45 pm
• Technical Session 45-02.30 pm
• Course Ends 30 pm