## GUJARAT TECHNOLOGICAL UNIVERSITY DIPLOMA IN ENGINEERING - SEMESTER 4 - EXAMINATION - SUMMER 2026 | Subject Code: DI04000071 | Subject Code: DI04000071 | Subject Code: DI04000071 | Date: 21/05/2026 | |-----------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------| | Subject Name: Introduction to Data Analysis | Subject Name: Introduction to Data Analysis | Subject Name: Introduction to Data Analysis | Total Marks: 70 | | Time: 10:30 AM TO 01:00 PM Instructions : | Time: 10:30 AM TO 01:00 PM Instructions : | Time: 10:30 AM TO 01:00 PM Instructions : | | | 1. Attempt all questions. 2. MakeSuitable assumptions wherever necessary 3. Figures to the right indicate full marks. | 1. Attempt all questions. 2. MakeSuitable assumptions wherever necessary 3. Figures to the right indicate full marks. | 1. Attempt all questions. 2. MakeSuitable assumptions wherever necessary 3. Figures to the right indicate full marks. | | | 4. Use of simple calculators and non-programmable scientific calculators are permitted. | 4. Use of simple calculators and non-programmable scientific calculators are permitted. | 4. Use of simple calculators and non-programmable scientific calculators are permitted. | | | 5. English version is authentic. | 5. English version is authentic. | 5. English version is authentic. | | | Q.1 | (a) | Define Data Analysis. List any four applications of data analysis. | 03 | | GLYPH GLYPH ૧ | ( અ ) | ડGLYPHટા િવGLYPHલ ેષ ણ GLYPHય ા GLYPHય ા િય ત કરો. ડ GLYPHટા િવ GLYPHલ ેષ ણ ન ા કોઈ પ ણ ચ ા ર ઉ પ ય ોગ ો લ ખ ો. | ૦૩ | | | (b) | Explain the difference between structured and unstructured data with examples. | 04 | | | ( બ ) | ઉદાહરણ સાથે structured અને unstructured ડGLYPHટા વ)ચેનો તફાવત સમ,વો. | ૦૪ | | | (c) | Explain the various phases of the Data Analytics Lifecycle. | 07 | | | ( ક ) | ડGLYPHટા એના.લ/ટ0સ લાઇફ સાઇકલના તબ3ા સમ,વો. | ૦૭ | | | | અથવા | | | | | OR | | | | (c) | Explain the concept of Big Data. What are the characteristics of Big Data (5 V's)? | 07 | | | ( ક ) | Big Data ને સમ,વો. Big Data ની િવશેષતાઓ (5 V's) Aું છે? | ૦૭ | | Q.2 | (a) | What is NumPy? State the difference between a NumPy array and a Python list. | 03 | | GLYPH GLYPH ૨ | ( અ ) | NumPy Aું છે? NumPy array અને Python list વ)ચેનો તફાવત જણાવો. | ૦૩ | | | (b) | Write a Python code to create a 2D NumPy array of shape (3,3) with random numbers. Also demonstrate vertical and horizontal stacking of two arrays. | 04 | | | ( બ ) | Random Numbers સાથે (3,3) આકાર નો 2D NumPy array બનાવવા માટGLYPH Python code લખો. બે arrays ના vertical અને horizontal stacking પણ દશાWવો. | ૦૪ | | | (c) | Explain Pandas DataFrame with an example. Write Python code to: (i) Read a CSV file, (ii) Handle missing values, (iii) Find top 3 rows using nlargest(). | 07 | | | ( ક ) | ઉદાહરણ સાથે Pandas DataFrame સમ,વો. Python code લખો: (i) CSV ફાઈલ | ૦૭ | | | | વાંચો, (ii) missing values handle કરો, (iii) nlargest() વડGLYPH top 3 rows મેળવો. અથવા | | | | | OR | | | Q.2 | (a) | What is Web Scraping? Mention its purpose. | 03 | | GLYPH GLYPH ૨ | ( અ ) | Web Scraping Aું છે ? તેનો હGLYPH` ુ જણાવો. | ૦૩ | | | (b) | Explain Data Normalization and Data Transformation with suitable examples. | 04 | | | ( બ ) | ઉ.ચત ઉદાહરણ સાથે Data Normalization અને Data Transformation સમ,વો. | ૦૪ | |---------------------------------------------------------------------------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------| | | (c) | Explain BeautifulSoup library for web scraping. Write a Python program that sends a GET request to a website and extracts all paragraph tags using BeautifulSoup. | 07 | | | ( ક ) | Web scraping માટGLYPH BeautifulSoup library સમ,વો. Python program લખો c website પર GET request મોકલે અને BeautifulSoup વડGLYPH paragraph tags extract | ૦૭ | | | | કરGLYPH. | | | Q.3 | (a) | Define Mean, Median and Mode with examples. | 03 | | GLYPH GLYPH ૩ | ( અ ) | ઉદાહરણ સાથે Mean, Median અને Mode GLYPHયાGLYPHયાિયત કરો. | ૦૩ | | | (b) | Explain Pearson's Correlation Coefficient. What does a value of +1, 0, and -1 indicate? | 04 | | | ( બ ) | Pearson's Correlation Coefficient સમ,વો. +1, 0 અને -1 ની value Aું m ૂ ચવે છે? | ૦૪ | | | (c) | Describe Normal, Poisson, Exponential, and Bernoulli probability distributions with their definitions and importance. | 07 | | | ( ક ) | Normal, Poisson, Exponential અને Bernoulli probability distributions ની GLYPHયાGLYPHયા | ૦૭ | | | | અને મહoવ વણWવો. | | | | | અથવા OR | | | Q.3 | (a) | What is the Method of Least Squares? State its use. | 03 | | GLYPH GLYPH ૩ | ( અ ) | Least Squares Method Aું છે? તેનો ઉપયોગ જણાવો. | ૦૩ | | | (b) | Write a short note on scatterplots and state its use in identifying correlation between variables. | 04 | | | ( બ ) | qકGLYPHટરrલ ોsસ પ ર t ૂ ંકu નvધ લખો અને variables વ)ચેના correlation ને ઓળખવામાં | ૦૪ | | | | તેનો ઉપયોગ જણાવો. | | | | (c) | Explain Descriptive Statistics in detail. Write a Python program to calculate Mean, Median, Mode, and Pearson's Correlation Coefficient for a given dataset. | 07 | | | ( ક ) | Descriptive Statistics િવગતવાર સમ,વો. Python program લખો c dataset માટGLYPH | ૦૭ | | | | Mean, Median, Mode અને Pearson's Correlation Coefficient ગણે. | | | Q.4 | (a) | What is Data Visualization? State its importance. | 03 | | GLYPH GLYPH ૪ | ( અ ) | Data Visualization Aું છે? તેxું મહoવ જણાવો. | ૦૩ | | | (b) | Differentiate between Matplotlib and Seaborn libraries for data visualization. | 04 | | | ( બ ) | Data visualization માટGLYPH Matplotlib અને Seaborn libraries વ)ચે તફાવત કરો. | ૦૪ | | | (c) | Write a Python program using Matplotlib to plot a line chart, bar chart, and scatter plot for sample data. | 07 | | | ( ક ) | Matplotlib વાપરuને sample data માટGLYPH line chart, bar chart અને scatter plot દોરવા | ૦૭ | માટGLYPH<c=2,font=/QPBAAA+ShrutiRegular> Python program લખો. ## અથવા | Q.4 GLYPH GLYPH | (a) | What is Plotly? State any two advantages of using Plotly for visualization. | 03 | |-------------------------------------------------------------------------------------------|-------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------| | ૪ | ( અ ) | Plotly Aું છે? Visualization માટGLYPH Plotly ઉપયોગ કરવાના કોઈ પણ બે ફાયદા જણાવો. | ૦૩ | | | (b) | Explain Time Series Data Visualization with a suitable example. | 04 | | | ( બ ) | ઉ.ચત ઉદાહરણ સાથે Time Series Data Visualization સમ,વો. | ૦૪ | | | (c) | Write a Python program using Seaborn to: | 07 | | | | 1. Create a bar plot showing the average income across regions using the 'tips' dataset. 2. Create a box plot to visualize the distribution of income across regions. | | | | ( ક ) | Seaborn નો ઉપયોગ કરuને Python yોzામ લખો: | ૦૭ | | | | ૧. 'tips' dataset નો ઉપયોગ કરuને િવિવધ yદGLYPHશોમાં સરGLYPHરાશ આવક (income) | | | | | દશાWવતો bar plot બનાવો. | | | | | ૨. િવિવધ yદGLYPH શોમા ં આ વકx ું િવતરણ (distribution) દશાWવવા માટGLYPH box plot બનાવો. | | | Q.5 | (a) | What is Generative AI? How is it different from Predictive AI? | 03 | | GLYPH GLYPH ૫ | ( અ ) | Generative AI Aું છે? તે Predictive AI થી કGLYPHવી રuતે અલગ છે? | ૦૩ | | | (b) | Explain any four challenges of Big Data Visualization. | 04 | | | ( બ ) | Big Data Visualization ના કોઈ પણ ચાર પડકારો સમ,વો. | ૦૪ | | | (c) | Explain Power BI and its key features. | 07 | | | ( ક ) | Power BI અને તેના ુGLYPHય features સમ,વો. | ૦૭ | | | | અથવા | | | | | OR | | | Q.5 | (a) | List any three uses of Generative AI in Analytics. | 03 | | GLYPH GLYPH ૫ | ( અ ) | એન.લ/ટ0સ માં Generative AI ના કોઈપણ €ણ ઉપયોગોની યાદu આપો. | ૦૩ | | | (b) | Explain bias, fairness, and privacy challenges of Generative AI. | 04 | | | ( બ ) | Generative AI ના bias, fairness અને privacy challenges સમ,વો. | ૦૪ | | | (c) | Explain how to visualize large datasets using Power BI. Describe the role of AI copilots in Business Intelligence. | 07 | | | ( ક ) | Power BI વાપરuને large datasets visualize કGLYPHવી રuતે કરવા તે સમ,વો. Business | ૦૭ | | | | Intelligence માં AI copilots ની  ૂિમકા વણWવો. | | *****