Introduction

Data science has emerged as a key field in driving data-driven decision-making across industries. It integrates multiple disciplines such as statistics, computer science, and domain-specific knowledge to uncover insights from structured and unstructured data. This course introduces participants to the foundational concepts, tools, and techniques of data science. With hands-on experience, learners will develop the skills needed to tackle real-world data problems, clean and analyze data, and visualize results. By the end of the course, participants will be equipped to start applying data science techniques in various business contexts.

Objectives

By the end of this course, participants will:

• Understand the core principles and lifecycle of data science projects.
• Learn how to clean, process, and manipulate data using Python and popular libraries like Pandas and NumPy.
• Gain foundational knowledge of statistical analysis and its role in data science.
• Develop skills in data visualization to communicate findings effectively.
• Understand machine learning basics and apply simple algorithms to real datasets.
• Explore the data science workflow, from data acquisition to decision-making.
• Build a foundation to transition into more advanced data science topics.

Who Should Attend?

This course is ideal for:

• Aspiring data scientists and analysts seeking to break into the field.
• Professionals with a background in business, engineering, or other fields looking to expand their knowledge of data science.
• Anyone interested in learning the basics of data analysis and how it applies to real-world problems.
• Developers or IT professionals interested in gaining a deeper understanding of data science tools and techniques.

Day 1: Introduction to Data Science and Data Science Workflow

Morning Session: What is Data Science?

• Defining data science: Scope, relevance, and applications in various industries
• Key components of data science: Data collection, cleaning, analysis, and visualization
• The role of a data scientist in an organization
• The data science workflow: Problem definition, data collection, data cleaning, modeling, evaluation, and deployment
• Tools and technologies in data science: Python, R, Jupyter Notebooks, and more

Afternoon Session: Setting Up Your Data Science Environment

• Installing and configuring Python for data science: Anaconda, Jupyter Notebooks, and libraries
• Introduction to data science libraries: NumPy, Pandas, and Matplotlib
• Hands-on: Exploring basic Python programming and working with Jupyter Notebooks
• Understanding data formats: CSV, Excel, JSON, and databases
• Hands-on: Loading and viewing datasets with Pandas

Day 2: Data Exploration and Cleaning

Morning Session: Data Wrangling with Pandas

• Data structures in Pandas: Series, DataFrames, and how they are used for analysis
• Techniques for exploring datasets: Descriptive statistics, summary statistics, and visualizations
• Handling missing data: Strategies for cleaning and imputing missing values
• Data transformations: Aggregation, grouping, and pivoting data
• Hands-on: Cleaning and exploring a dataset with Pandas

Afternoon Session: Data Preprocessing and Feature Engineering

• Introduction to feature engineering: Creating new variables from existing data
• Scaling and normalizing data: Standardization and Min-Max scaling
• Encoding categorical variables: One-hot encoding and label encoding
• Hands-on: Preprocessing a dataset for machine learning
• Introduction to handling time-series and text data

Day 3: Introduction to Statistical Analysis

Morning Session: Fundamentals of Statistics in Data Science

• Descriptive statistics: Mean, median, mode, standard deviation, and variance
• Inferential statistics: Hypothesis testing, confidence intervals, and p-values
• Correlation and causality: Understanding relationships between variables
• The importance of sampling and probability distributions
• Hands-on: Performing statistical analysis on datasets

Afternoon Session: Data Visualization

• Introduction to data visualization: Principles and best practices
• Tools for data visualization: Matplotlib, Seaborn, and Plotly
• Visualizing distributions: Histograms, box plots, and scatter plots
• Visualizing relationships: Heatmaps, pair plots, and correlation matrices
• Hands-on: Creating and customizing visualizations using Matplotlib and Seaborn

Day 4: Introduction to Machine Learning

Morning Session: Introduction to Machine Learning

• What is machine learning? Key concepts and types of machine learning (supervised vs. unsupervised)
• The machine learning pipeline: Data preprocessing, model selection, training, evaluation, and testing
• Overview of common algorithms: Linear regression, classification, clustering, and decision trees
• How to choose the right algorithm for a problem
• Hands-on: Applying simple machine learning algorithms to a dataset using scikit-learn

Afternoon Session: Supervised Learning Techniques

• Supervised learning: Regression vs. classification problems
• Training and evaluating a linear regression model
• Training and evaluating a classification model: Logistic regression, decision trees
• Model evaluation metrics: Accuracy, precision, recall, F1-score, and ROC curve
• Hands-on: Building a classification model and evaluating its performance

Day 5: Real-World Data Science Applications and Final Project

Morning Session: Applying Data Science to Real-World Problems

• End-to-end data science project: From problem definition to solution deployment
• Case studies: Data science applications in finance, healthcare, marketing, and sports
• Challenges in data science: Bias, ethics, privacy, and data quality
• Data science in the industry: Current trends and the future of data science
• Hands-on: Working through a case study or a problem-solving exercise

Afternoon Session: Final Project and Course Wrap-Up

• Final project: Participants will choose a dataset and apply the techniques learned during the course to explore, clean, analyze, and visualize the data.
• Presenting the results: How to communicate insights through clear visualizations and reports
• Course wrap-up: Review of key concepts, future learning paths, and additional resources for further study
• Certification of completion awarded to participants who successfully complete the course and final project

Materials and Tools:

• Required tools: Python, Jupyter Notebooks, and libraries (NumPy, Pandas, Matplotlib, scikit-learn, Seaborn)
• Access to real-world datasets (e.g., Kaggle datasets, UCI machine learning repository)
• Templates for data analysis and reporting

Conclusion and Final Assessment

• Recap of key concepts: Data science workflow, data preprocessing, statistical analysis, machine learning, and data visualization
• Final project presentations and peer feedback
• Certification of completion for those who successfully complete the course and demonstrate practical application of data science techniques