In today's data-driven world, organizations are increasingly relying on data science to gain a competitive edge. "Data Science for Competitive Advantage" training is essential as it equips individuals with the necessary skills and knowledge to leverage data effectively. This training covers a wide range of topics, including data collection, cleaning, analysis, visualization, and machine learning techniques. By participating in this training, individuals can learn how to extract valuable insights from large datasets, identify trends and patterns, build predictive models, and make data-driven decisions that can improve business performance, optimize operations, and drive innovation.
Upon the successful completion of this course, each participant will be able to:
This course is designed for Business Analysts, Data Analysts, Marketing Professionals, Product Managers, Business Intelligence Analysts, Decision-Makers (Executives, Managers) & anyone interested in leveraging data for strategic advantage.
DAY 1
Registration, Welcome & Introduction
Pre-Test
Introduction to Data Science & Business Applications
√ Defining data science, its key components (statistics, computer science, domain expertise)
√ The data science lifecycle: from data collection to model deployment
√ Global statistics and trends
√ Customer segmentation & targeting
√ Fraud detection
√ Risk assessment
√ Personalized recommendations
√ Market research & trend analysis
√ Supply chain optimization
√ Internal data sources (CRM, ERP, databases)
√ External data sources (public datasets, APIs, web scraping)
√ Data collection methods (surveys, interviews, experiments)
√ Identifying and handling missing values, outliers, and inconsistencies
√ Data transformation and feature engineering techniques
Day 2
Data Visualization & Exploratory Data Analysis (EDA)√ Choosing the right chart type for different data types
√ Creating clear and concise visualizations
√ Storytelling with data
√ Tools for data visualization (Tableau, Power BI, Python libraries like matplotlib, seaborn)
√ Summarizing and visualizing data distributions
√ Identifying patterns, trends, and anomalies
√ Hypothesis testing and statistical significance
√ Case study: Conducting EDA on a real-world dataset
Day 3
Statistical Analysis & Machine Learning Fundamentals√ Descriptive statistics (mean, median, mode, standard deviation)
√ Inferential statistics (hypothesis testing, regression analysis)
√ Probability distributions
√ Supervised vs. unsupervised learning
√ Common machine learning algorithms (linear regression, logistic regression, decision trees, clustering)
√ Model evaluation metrics (accuracy, precision, recall, F1-score)
Day 4
Evaluating Predictive Models√ Techniques for improving model performance (hyperparameter tuning, cross-validation)
√ Overfitting and underfitting
√ Ensemble methods (bagging, boosting)
Day 5
Communicating Insights & Best Practices√ Data privacy and security
√ Bias in data and algorithms
√ Responsible AI
√ Version control
√ Reproducibility
√ Documentation
Pre-Test
End of the CourseFacilitated by a highly qualified specialist, who has extensive knowledge and experience; this program will be conducted using extensively interactive methods, encouraging participants to share their own experiences and apply the program material to real-life work situations in order to stimulate group discussions and improve the efficiency of the subject coverage.
Percentages of the total course hour classification are:
At the completion of the course, all participants who successfully accomplished the required contact hours will receive an EdTech Training Participation Certificate as a testimony to their commitment to professional development and further education.