DATA ANALYSIS & SCIENCE: FROM INSIGHT TO IMPACT

COURSE OVERVIEW

This course offers a hands-on, end-to-end journey through the world of **Data Analysis and Data Science**. Designed for beginners and intermediate learners, the course blends statistical thinking, programming, and real-world application to extract insights from data and turn them into actionable outcomes.

Learners will explore data manipulation, visualization, statistical analysis, and machine learning using tools like Python, Pandas, NumPy, Matplotlib, and Scikit-learn. Emphasis is placed on solving real-world problems, working with structured and unstructured datasets, and communicating findings effectively.

Whether you're pursuing a data science career, enhancing your analytical skills, or making data-driven decisions, this course is your foundation.

LEARNING OBJECTIVES

By the end of this course, students will be able to:

* Understand the data science lifecycle: data collection, cleaning, exploration, modeling, and communication

* Perform data cleaning and transformation with Python and Pandas

* Visualize data trends and patterns using Matplotlib and Seaborn

* Conduct statistical analysis and hypothesis testing

* Build and evaluate predictive models using machine learning algorithms

* Work with real-world datasets (CSV, Excel, APIs, databases)

* Communicate insights through dashboards and reports

* Understand ethical data use and bias in algorithms

COURSE MODULES

Module 1: Introduction to Data Science

What is data science and how is it used?

Overview of tools and technologies

Setting up your Python environment (Jupyter, Anaconda, VS Code)

Module 2: Data Analysis with Python

Python basics for data science

Data structures and loops

Working with NumPy and Pandas

Data importing, cleaning, and wrangling

Module 3: Exploratory Data Analysis (EDA)

Descriptive statistics

Data visualization with Matplotlib and Seaborn

Identifying trends, outliers, and correlations

Module 4: Statistical Thinking

Probability distributions

Hypothesis testing (t-tests, chi-square)

Confidence intervals A/B testing basics

Module 5: Introduction to Machine Learning

Supervised vs. unsupervised learning

Linear regression, decision trees, k-NN

Model evaluation (accuracy, precision, recall, ROC)

Module 6: Advanced Topics (Optional)

Natural Language Processing (NLP) basics

Time series forecasting

Clustering (k-means)

Module 7: Data Communication

Creating reports and dashboards

Telling stories with data

Introduction to tools like Power BI or Tableau (optional)

Module 8: Ethics and Real-World Applications

Bias, fairness, and transparency in data science

Case studies: healthcare, marketing, finance, education

CAPSTONE PROJECT

Students will complete a data science project involving:

Data sourcing and cleaning

Exploratory analysis

Model building (optional)

Presentation of findings in a report or dashboard

Example projects: predicting housing prices, analyzing customer churn, sentiment analysis of social media data, etc.

TARGET AUDIENCE

Aspiring data analysts or data scientists

Professionals in business, marketing, finance, or engineering

Students in STEM fields Anyone looking to make data-driven decisions

COURSE FORMAT

Duration: 8–12 weeks (adjustable)

Delivery: Online (self-paced or instructor-led), in-person, or hybrid

Format: Lectures, coding labs, assignments, quizzes, and a final project

Tools: Jupyter Notebook, Python, GitHub, Excel (optional)

OUTCOMES

Upon completing the course, learners will:

 Be able to perform exploratory data analysis and build predictive models

 Understand and apply core statistical and machine learning concepts

 Confidently work with real-world datasets using industry-standard tools

 Have a portfolio-ready project to showcase

TECHNOLOGIES AND TOOLS COVERED

 Area                   Tools & Libraries        

Programming             Python (Jupyter, VS Code) 

Data Wrangling          Pandas, NumPy             

Visualization           Matplotlib, Seaborn       

Machine Learning        Scikit-learn              

Statistics              SciPy, statsmodels        

Optional Dashboarding   Tableau, Power BI, Plotly