Data Science

Data Science

Course Description:

This course provides an introduction to the fundamentals of data analytics for beginners.

Students will learn how to gather, clean, analyze, and visualize data to derive valuable

insights. The course covers basic statistical concepts, data manipulation techniques, and

introduces popular tools used in the field of data analytics.

Prerequisites:

• Basic computer literacy

• Familiarity with spreadsheets (e.g., Microsoft Excel, Google Sheets)

• Basic python programming knowledge

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Course Outline:

Week 1/a: Introduction to Data Analytics

• Overview of Data Analytics and its applications

• Importance of data-driven decision making

• Role of a Data Analyst

• Introduction to data types and sources

Week 1/b: Data Collection and Cleaning

• Data collection methods (surveys, web scraping, APIs, etc.)

• Data formats (CSV, Excel, JSON, etc.)

• Data cleaning techniques (handling missing values, outliers, duplicates)

Week 2: Exploratory Data Analysis (EDA)

• Basic statistics (mean, median, mode, standard deviation, etc.)

• Data visualization using charts and graphs (bar plots, histograms, scatter plots)

• Descriptive statistics and summary metrics

Week 3: Data Manipulation and Analysis

• Introduction to data manipulation libraries (e.g., pandas in Python)

• Filtering, sorting, and aggregating data

• Basic statistical tests (t-tests, chi-square tests)

Week 4: Introduction to Data Visualization

• Principles of effective data visualization

• Tools for data visualization (e.g., Matplotlib, Seaborn)

• Creating informative and compelling visualizations

Week 5: Introduction to Machine Learning and Predictive Analytics

• Overview of machine learning concepts

• Introduction to supervised and unsupervised learning

• Simple predictive modeling using regression and classification