DataCraft Mastering Analytics

Prerequisite

• Basic understanding of data analysis concepts and tools.
• Familiarity with spreadsheet software (e.g., Microsoft Excel, Google Sheets).
• Basic knowledge of SQL is recommended but not required.

Course Description

Data analytics is a critical skill for professionals across various industries, enabling them to derive valuable insights from data to inform decision-making and drive business growth. This course will equip you with the knowledge and tools to master data analytics using Google Sheets, SQL, and other popular data analytics tools.

What you will learn

• By the end of this course, you will have a solid understanding of data analytics concepts and techniques, as well as hands-on experience with popular data analytics tools. You will be able to perform data exploration, visualization, cleaning, preprocessing, and analysis using Google Sheets and SQL.
• You will also learn statistical fundamentals, machine learning concepts, predictive analytics, time series analysis, and text analytics using Google Sheets and custom scripts.

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