Introduction to Data Analysis
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Follow on Instagram for UpdatesIntroduction to Data Analysis Syllabus — Unit-wise Content
The official GTU curriculum for Introduction to Data Analysis (subject code DI04000071) as prescribed for Chem. Green · Semester 4 divides the course into 5 units, covering 52 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (DI04000071)Unit 1 Introduction to Data Analysis
- ● Sources and nature of data
- classification of data (structured
- ● Characteristics of data
- ● Introduction to Big Data platform
- ● Need of data Analysis ● Evolution of analytic scalability ● Analytical process ● Analysis vs. reporting ● Modern data analysis tools ● Applications of data analysis. ● Key roles for successful analysis ● Various phases of data analytics lifecycle – discovery
- data preparation
- model planning
- model building
- communicating results, and operationalization
Unit 2 Python libraries for Data Analysis and Data extraction
- Toolkits Using Python ● NumPy - Difference between array and list
- N dimension array - 1D array
- Zeros matrix
- Ones matrix
- Identity matrix
- Working with random Program Name: Diploma in Engineering Level: Diploma Branch: Computer Engineering / Computer Science and Engineering Subject Code : DI04000071 Subject Name: Introduction to Data Analysis number
- Stacking - Vertical stacking
- horizontal stacking
- Working with RGB Image
- image as a numpy array. ● Pandas - Working with Dataframes Read csv and xlsx file
- Analyze the basic dataset characteristics
- Perform different merge and sort operations with multiple dataframes
- Handle missing values in Dataframe
- Analyze the DataFrame with loc and iloc
- add or remove an attribute from the DataFrame
- Working With Data ● Reading Files ● Scraping the Web – Purpose
- Legality and Ethical Considerations
- Overview of popular libraries (Beautiful Soup
- Selenium) ● BeautifulSoup (Purpose and Use Cases
- Parsing HTML
- Working with HTML tags
- Accessing tag attributes
- Simple HTML Parsing Example
- Extracting Data from Web Pages)
- ● Requests (Basics of sending GET/POST requests
- accessing response content, and authenticating with Requests) ● Cleaning and Munging ● Rescaling ● Data Normalization and Transformation ● Dimensionality Reduction
Unit 3 Statistical Analysis
- ● Regression modeling ● Multivariate analysis ● Apply basics of descriptive statistics including measures of central tendency such as mean
- median, and mode Different correlation techniques: ● Pearson’s Correlation Coefficient
- ● Methods of Least Squares
- ● scatterplots and other graphical techniques to identify the correlation between variables
- Program Name: Diploma in Engineering Level: Diploma Branch: Computer Engineering / Computer Science and Engineering Subject Code : DI04000071 Subject Name: Introduction to Data Analysis ● Different probability distributions such as Normal
- Bernoulli Definition and Importance
Unit 4 Data Visualization
- ● Introduction to Data Visualization ● Importance of Data Visualization ● Basic Data Visualization with matplotlib ● Customizing matplotlib plots ● Data visualization with Seaborn ● Interactive visualization with Plotly ● Time series data visualization ● Advance plots: Yiolin plots
- Box plots
Unit 5 Generative AI and Big Data Visualization with Power BI
- ● Recent trends in data analysis
- predictive vs generative AI ● Generative AI in Analytics: synthetic data
- automated dashboards
- natural language queries
- storytelling with data
- AI copilots in BI ● Big Data: o Visualizing Big Data o Pre-attentive Attributes o Challenges of Big Data Visualization ● Power BI: o Data transformation & summarization o Dashboards: bar, line
- pie charts o AI visuals: Q&A
- decomposition tree
- key influencers o Visualizing large datasets & big data considerations ● Generative AI Challenges & Solutions: bias
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