Data Analytics Foundations

Prerequisite

Basic computing skills (Excel usage, internet research, file management)

Course Description

Master the art of data analytics with tools like Google Sheets and SQL. Turn raw data into actionable insights for business growth.

What you will learn

• - Explore, clean, and analyze data
• - Perform data visualization and reporting
• - Use SQL for database management
• - Learn basics of machine learning and predictive analysis

Course Outline

• Week 1 Introduction to Data Analytics

  • Understanding the role of data analytics in decision-making processes.
  • Introduction to data-driven insights and their impact on business strategies.
  • Overview of popular data analytics tools and their applications.

• Week 2 Data Exploration and Visualization

  • Exploring data using Google Sheets:
  • Sorting, filtering, and summarizing data.
  • Using pivot tables for data analysis.
  • Advanced data visualization techniques in Google Sheets.

• Week 3 Introduction to Data and Databases

  • Understanding relational databases and SQL:
  • Creating and querying databases.
  • Basic SQL commands for data retrieval and manipulation.
  • Hands-on practice with SQL using SQLite or MySQL.

• Week 4 Data Cleaning and Preprocessing

  • Techniques for data cleaning and preprocessing in Google Sheets:
  • Identifying and handling missing data.
  • Removing duplicates and outliers.
  • Transforming and formatting data for analysis.

• Week 5 Exploratory Data Analysis (EDA)

  • Performing EDA using Google Sheets:
  • Analyzing data distributions and relationships.
  • Calculating descriptive statistics.
  • Identifying patterns and insights from an exploratory analysis.

• Week 6 Statistical Fundamentals for Data Analysis

  • Understanding statistical concepts for data analysis:
  • Probability distributions and hypothesis testing.
  • Correlation and regression analysis.
  • Applying statistical techniques to real-world datasets.

• Week 7 Introduction to Machine Learning

  • Overview of machine learning concepts and algorithms: Supervised vs
  • unsupervised learning. Classification and regression models. Hands-on exercises with Google Sheets add-ons for basic machine learning tasks.

• Week 8 Data Mining and Pattern Recognition

  • Introduction to data mining techniques:
  • Association rule mining and frequent itemsets.
  • Clustering algorithms for pattern recognition.
  • Implementing data mining algorithms in Google Sheets.

• Week 9 Predictive Analytics

  • Building predictive models using Google Sheets:
  • Regression analysis for forecasting.
  • Evaluating model performance and accuracy.
  • Applying predictive analytics to business scenarios

• Week 10 Time Series Analysis

  • Analyzing time series data with Google Sheets:
  • Time series decomposition and trend analysis.
  • Forecasting methods and techniques.
  • Hands-on practice with time series analysis using Google Sheets functions.

• Week 11 Text Analytics and Natural Language Processing (NLP)

  • Introduction to text analytics and NLP:
  • Text preprocessing techniques (tokenization, stemming, etc.).
  • Sentiment analysis and topic modeling.
  • Implementing NLP algorithms in Google Sheets using custom scripts.

• Week 12 Case Studies and Capstone Project

  • Advanced Google Sheets Usage:
  • Advanced data manipulation techniques (array formulas, conditional formatting, etc.).
  • Integrating Google Sheets with external data sources (APIs, web scraping, etc.).

Grading Criteria

Particulars	Marks (%)
Quizzes	20
Class Participation/ Attendance	15
Projects	25
Final Projects	40
Total	100