Data Analytics – III is a comprehensive and application-oriented guide designed to equip students with the essential skills required in today’s data-driven world. The book introduces key concepts of exploratory data analysis (EDA), data visualization, and statistical techniques, and gradually advances to practical tools such as Python and Power BI for real-world data analysis.
With a strong focus on hands-on learning, the book covers working with datasets using libraries like Pandas and NumPy, as well as creating interactive dashboards and visual reports using Power BI. It enables students to clean, transform, analyze, and present data effectively for informed decision-making. Written in a simple and structured manner, this book serves as an ideal resource for developing analytical thinking, problem-solving abilities, and industry-relevant skills required for careers in data analytics and business intelligence.
1. Exploratory Data Analysis
1.1 EDA fundamental
1.2 Exploratory data analysis and data visualization
1.3 Types of Exploratory Data Analysis : Univariate, Bivariate, Multivariate and Time Series Analysis
1.4 Exploratory Data Analysis Tools
1.5 Making sense of data Comparing EDA with classical and Bayesian analysis
1.6 Continuous variables, Discrete variables, Dependency relationships, Multivariate categorical variables
1.7 Temporal data, Spatial data
2. EDA Using Python
2.1 Introduction to Python
2.2 Python Libraries (Types of libraries)
2.3 Working on DataFrame
2.4 Exploratory Data Analysis (EDA) Using Pandas (head(), tail(), isna(), DataFrame.sort_values(),
2.5 DataFrame.truncate(), DataFrame.describe(), drop_dublicates)
2.6 Exploratory data visualization with Pandas library
2.7 Numpy library function for EDA
3. Introduction to Power BI
3.1 Power BI History, Importance
3.2 Power BI Components
3.3 Power BI Architecture
3.4 Power BI Tools
3.5 Power BI Advantages
3.6 Power BI Disadvantages
3.7 Download and Install Power BI Desktop
3.8 Power BI Dashboard
4. Working on Data using Power BI
4.1 Importing data into Power BI from various sources: Excel, CSV, databases, web, etc.
4.2 Cleaning data using Power Query Editor.
4.3 Introduction to Power Query Editor for data cleaning and transformation.
4.4 Techniques for handling missing values, duplicates, and errors.
4.5 Data reshaping: pivoting, unpivoting, splitting columns.
5. Data Visualization using Power BI
5.1 Creating basic visualizations: bar charts, line charts, pie charts, etc.
5.2 Formatting and customizing visuals: colors, fonts, labels.
5.3 Utilizing slicers and filters for interactive analysis.
5.4 Advanced visualizations: treemaps, scatter plots, histograms, etc.
6. Dashboard in Power BI
6.1 Building interactive dashboards: combining visuals, arranging layout.
6.2 Dashboard Creation.
6.3 Dashboard layout and Navigation button.
6.4 Publishing dashboards to Power BI Service.
6.5 Sharing dashboards with colleagues and stakeholders.
6.6 Configuring dashboard settings: access permissions, refresh schedules.
BCA-504 (B) | Semester V
BACHELOR OF COMPUTER APPLICATIONS (BCA)
As per U.G.C. guidelines and also on the basis of the revised syllabus of Kaviyatri Bahinabai Chaudhary North Maharashtra University with effect from Academic Year 2024-25. Also useful for all Universities.
