Training Course

Overview

Data Science Fundamentals is a comprehensive professional training course designed to provide participants with a strong foundation in the principles, methods, tools, and workflows used to transform data into actionable knowledge and evidence-based decisions. The course introduces the complete data science lifecycle, from problem definition and data acquisition through data preparation, exploratory analysis, statistical reasoning, visualization, predictive modelling, model evaluation, and communication of results. Participants develop a practical understanding of how data science supports business intelligence, operational improvement, forecasting, risk management, customer analytics, scientific research, and strategic decision-making across modern organizations.

The course provides a structured introduction to essential data science concepts, including data types, data structures, analytical thinking, data collection, data quality, data cleaning, feature preparation, exploratory data analysis, descriptive statistics, probability, statistical inference, and data visualization. Participants are introduced to practical tools and technologies such as Python, Jupyter Notebook, pandas, NumPy, Matplotlib, Seaborn, and scikit-learn, while also learning transferable analytical concepts that apply across different data science environments. Best practices for reproducible analysis, documentation, validation, version control, and responsible data use are incorporated throughout the program.

Data Science Fundamentals emphasizes hands-on learning through realistic datasets, analytical exercises, case studies, and applied business scenarios. Participants learn how to investigate patterns and relationships in data, identify meaningful variables, formulate analytical hypotheses, build basic regression and classification models, evaluate predictive performance, and interpret analytical findings. The course introduces established frameworks and practices such as the data science lifecycle, CRISP-DM, exploratory data analysis, train-test splitting, cross-validation, model evaluation metrics, feature engineering, and analytical storytelling to help participants build reliable and explainable data science workflows.

The final stage of the course integrates data preparation, exploratory analytics, statistical analysis, machine learning, visualization, and professional communication into an end-to-end data science workflow. Participants examine model limitations, overfitting, bias, data leakage, reproducibility, ethical considerations, privacy, and responsible AI and analytics practices. Through a comprehensive capstone project, participants apply the full data science lifecycle to a real-world problem, develop an analytical solution, evaluate its reliability, communicate findings to stakeholders, and formulate practical recommendations based on evidence.

Course Duration

10 Days (80 Hours)

Target Participants

·         Aspiring data scientists and data analysts

·         Business analysts and business intelligence professionals

·         Data and analytics professionals seeking formal data science foundations

·         IT professionals transitioning into data science and analytics

·         Researchers and quantitative professionals working with datasets

·         Finance, marketing, operations, HR, and customer analytics professionals

·         Managers and supervisors responsible for data-driven decision-making

·         Professionals working with Python, spreadsheets, databases, or business intelligence tools

·         Professionals preparing for more advanced machine learning and data science training

·         Executives and decision-makers seeking a practical understanding of data science capabilities

Course Objectives

By the end of the training, participants will be able to:

·         Explain fundamental data science concepts, terminology, workflows, and applications

·         Understand the data science lifecycle and apply the CRISP-DM framework to analytical projects

·         Identify suitable data sources, analytical questions, variables, and data requirements

·         Prepare, clean, transform, and validate datasets for reliable analysis

·         Use Python, Jupyter Notebook, pandas, and NumPy for practical data science workflows

·         Conduct exploratory data analysis and identify patterns, trends, relationships, and anomalies

·         Apply descriptive statistics, probability concepts, and basic statistical inference

·         Create effective data visualizations using Matplotlib and Seaborn

·         Build and interpret introductory regression and classification models

·         Apply appropriate model evaluation techniques and performance metrics

·         Understand overfitting, underfitting, data leakage, bias, variance, and model validation

·         Apply basic feature engineering and analytical dataset development techniques

·         Communicate data science findings through clear visualizations, reports, and data storytelling

·         Apply principles of reproducibility, responsible data use, privacy, and ethical analytics

·         Develop and present an end-to-end data science solution through a practical capstone project

Course Content

Day 1: Foundations of Data Science, Analytical Thinking, and the Data Science Lifecycle

Module 1: Foundations of Data Science, Analytical Thinking, and the Data Science Lifecycle

1.      Data Science Concepts, Definitions, Evolution, and Applications

2.      Data Science, Data Analytics, Business Intelligence, and Machine Learning

3.      Data Science Roles, Teams, Responsibilities, and Professional Competencies

4.      The Data Science Lifecycle and CRISP-DM Framework

5.      Problem Definition, Business Understanding, and Analytical Question Formulation

6.      Data Types, Variables, Observations, Features, Labels, and Data Structures

7.      Structured, Semi-Structured, and Unstructured Data Sources

8.      Data Collection, Sampling, Data Access, and Analytical Requirements

9.      Data Science Tools, Environments, Jupyter Notebook, Python, and Development Workflows

10.  Practical Exercise: Defining a Real-World Data Science Problem and Developing an Initial Analytical Project Plan

Day 2: Python, Data Preparation, and Data Quality

Module 2: Python, Data Preparation, and Data Quality

1.      Python Fundamentals for Data Science

2.      Jupyter Notebook, Python Scripts, Packages, and Analytical Workspaces

3.      Variables, Data Types, Operators, Functions, and Control Structures

4.      Lists, Tuples, Dictionaries, Sets, and Data Structures

5.      NumPy Arrays and Numerical Data Processing

6.      pandas Series, DataFrames, Indexing, and Data Selection

7.      Importing Data from CSV, Excel, JSON, and Database Sources

8.      Data Profiling, Missing Values, Duplicates, Outliers, and Inconsistencies

9.      Data Cleaning, Transformation, Encoding, Scaling, and Validation

10.  Practical Exercise: Preparing and Validating a Real-World Dataset Using Python and pandas

Day 3: Exploratory Data Analysis and Statistical Foundations

Module 3: Exploratory Data Analysis and Statistical Foundations

1.      Exploratory Data Analysis Concepts, Objectives, and Workflow

2.      Descriptive Statistics and Measures of Central Tendency

3.      Measures of Dispersion, Variability, and Distribution

4.      Percentiles, Quartiles, Frequency Distributions, and Summary Statistics

5.      Probability Concepts and Random Variables

6.      Probability Distributions and Practical Analytical Applications

7.      Sampling Methods, Sampling Bias, and Representativeness

8.      Correlation, Covariance, Relationships, and Association

9.      Identifying Trends, Patterns, Anomalies, and Data Quality Issues

10.  Case Study and Exercise: Conducting an Exploratory Data Analysis and Developing Initial Data Insights

Day 4: Data Visualization and Analytical Communication

Module 4: Data Visualization and Analytical Communication

1.      Principles of Effective Data Visualization

2.      Choosing Appropriate Charts for Analytical Questions

3.      Matplotlib Fundamentals and Professional Plot Construction

4.      Seaborn for Statistical and Exploratory Visualization

5.      Bar Charts, Histograms, Box Plots, and Distribution Analysis

6.      Scatter Plots, Line Charts, Heatmaps, and Relationship Analysis

7.      Visualizing Categorical, Numerical, Time-Series, and Multivariate Data

8.      Color, Scale, Annotation, Labels, and Visual Accessibility

9.      Data Storytelling, Insight Communication, and Analytical Reporting

10.  Practical Exercise: Developing a Professional Analytical Visualization Report from a Real-World Dataset

Day 5: Statistical Inference and Hypothesis Testing

Module 5: Statistical Inference and Hypothesis Testing

1.      Statistical Inference and the Role of Uncertainty in Data Science

2.      Population, Sample, Parameters, Statistics, and Sampling Distributions

3.      Confidence Intervals and Estimation

4.      Null and Alternative Hypotheses

5.      P-Values, Significance Levels, and Statistical Decision-Making

6.      Type I and Type II Errors and Statistical Power

7.      T-Tests, Proportion Tests, and Practical Comparison Methods

8.      Chi-Square Tests and Categorical Data Analysis

9.      Practical Interpretation of Statistical Results and Effect Size

10.  Case Study: Evaluating a Business Hypothesis Using Statistical Inference and Communicating the Findings

Day 6: Regression Analysis and Predictive Modelling Foundations

Module 6: Regression Analysis and Predictive Modelling Foundations

1.      Predictive Analytics Concepts and Regression Modelling

2.      Simple Linear Regression and Model Structure

3.      Multiple Linear Regression and Predictor Variables

4.      Regression Coefficients, Intercepts, Predictions, and Interpretation

5.      Model Fit, Residuals, R-Squared, and Adjusted R-Squared

6.      Regression Assumptions and Diagnostic Analysis

7.      Feature Selection, Transformation, and Basic Feature Engineering

8.      Train-Test Splitting and Introduction to Cross-Validation

9.      Regression Evaluation Metrics Including MAE, MSE, RMSE, and MAPE

10.  Practical Exercise: Building, Evaluating, and Interpreting a Regression Model with scikit-learn

Day 7: Classification, Machine Learning, and Model Evaluation

Module 7: Classification, Machine Learning, and Model Evaluation

1.      Classification Concepts and Supervised Machine Learning

2.      Logistic Regression and Binary Classification

3.      Decision Trees and Rule-Based Prediction

4.      Random Forests and Ensemble Learning Fundamentals

5.      Training, Validation, and Testing Machine Learning Models

6.      Confusion Matrix and Classification Performance Measures

7.      Accuracy, Precision, Recall, F1-Score, and Specificity

8.      ROC Curves, AUC, Threshold Selection, and Probability-Based Classification

9.      Overfitting, Underfitting, Bias, Variance, and Model Generalization

10.  Case Study and Exercise: Developing and Evaluating a Customer or Risk Classification Model

Day 8: Feature Engineering, Model Optimization, and Advanced Analytical Workflows

Module 8: Feature Engineering, Model Optimization, and Advanced Analytical Workflows

1.      Feature Engineering Principles and Analytical Feature Development

2.      Numerical, Categorical, Date-Time, and Text Feature Preparation

3.      Feature Scaling, Transformation, Encoding, and Selection

4.      Pipelines and Reproducible Machine Learning Workflows

5.      Cross-Validation and Robust Model Evaluation

6.      Hyperparameter Concepts and Model Optimization

7.      Grid Search, Random Search, and Practical Parameter Tuning

8.      Ensemble Methods and Comparative Model Evaluation

9.      Data Leakage, Model Bias, Interpretability, and Analytical Risk

10.  Practical Exercise: Building a Reproducible Machine Learning Pipeline and Comparing Multiple Models

Day 9: Responsible Data Science, Reproducibility, Deployment, and Professional Practice

Module 9: Responsible Data Science, Reproducibility, Deployment, and Professional Practice

1.      Responsible Data Science Principles and Professional Standards

2.      Data Privacy, Confidentiality, Security, and Responsible Data Handling

3.      Bias, Fairness, Transparency, and Ethical Analytical Decision-Making

4.      Explainability, Interpretability, and Communicating Model Limitations

5.      Reproducible Data Science and Analytical Documentation

6.      Version Control, Project Organization, Dependencies, and Environment Management

7.      Model Serialization, APIs, and Introduction to Analytical Deployment

8.      Monitoring, Model Performance, Data Drift, and Lifecycle Management

9.      Communicating Data Science Results to Technical and Non-Technical Stakeholders

10.  Case Study: Designing a Responsible, Reproducible, and Stakeholder-Ready Data Science Workflow

Day 10: Integrated Data Science Capstone and Professional Application

Module 10: Integrated Data Science Capstone and Professional Application

1.      End-to-End Data Science Project Planning and Problem Definition

2.      Business Understanding, Data Requirements, and Analytical Success Criteria

3.      Data Acquisition, Preparation, Quality Assessment, and Feature Development

4.      Exploratory Data Analysis, Statistical Investigation, and Visualization

5.      Model Selection, Training, Validation, and Performance Evaluation

6.      Model Interpretation, Limitations, Risk, and Responsible Use

7.      Analytical Storytelling, Visualization, and Stakeholder Communication

8.      Recommendations, Decision Support, and Business Value Assessment

9.      Integrated Data Science Capstone: Developing an End-to-End Analytical Solution

10.  Capstone Presentation, Peer Review, Evaluation, and 90-Day Data Science Implementation Action Plan