MCS 226 Solved Assignment 2024-25
Data Science and Big Data
MCS 226 Solved
Assignment 2024-25 : All assignments are in PDF format which would be send
on email/WhatsApp (9958676204) just after payment.
Assignment Code: ASST/ MCS 226 /2024-25
Marks: 100
Attempt all the questions:
Q.1 Describe data
science. What uses does it have? In the context of data analysis, define the
terms descriptive, exploratory, and predictive.
Data science is
a multidisciplinary field that utilizes various techniques, algorithms, and
systems to extract insights and knowledge from structured and unstructured
data. It encompasses aspects of statistics, mathematics, computer science, and
domain expertise to analyze complex datasets and derive actionable insights.
Data science involves the entire data lifecycle, including data collection,
cleaning, preprocessing, analysis, interpretation, visualization, and
communication of results.
Business
Intelligence: Data science helps businesses gain insights into customer
behavior, market trends, and operational efficiency, enabling data-driven
decision-making.
Predictive
Analytics: By analyzing historical data, data scientists can build predictive
models to forecast future trends, such as sales, demand, or customer churn,
aiding in strategic planning and risk management.
Machine
Learning: Data science employs machine learning algorithms to develop models
that can automatically learn and improve from data, enabling tasks like image
recognition, natural language processing, recommendation systems, and fraud
detection.
Healthcare: In
healthcare, data science is used for patient diagnosis and treatment
optimization, drug discovery, personalized medicine, and epidemiological
studies.
Finance: Data
science plays a crucial role in financial markets for algorithmic trading, risk
assessment, fraud detection, credit scoring, and portfolio management.
Internet of
Things (IoT): With the proliferation of IoT devices generating vast amounts of
data, data science helps in extracting valuable insights for various
applications, such as smart cities, industrial automation, and wearable
technology.
Social Media Analysis: Data science techniques are employed to analyze social media data for sentiment analysis, trend identification, and targeted advertising.
Descriptive
Analysis: Descriptive analysis involves summarizing and presenting key
characteristics of a dataset. It focuses on understanding the data's main
features, such as central tendency, dispersion, distribution, and frequency.
Descriptive statistics, graphs, and charts are commonly used techniques in this
phase to provide insights into the dataset's structure and patterns.
Exploratory
Analysis: Exploratory analysis aims to uncover hidden patterns, relationships,
or trends within the data. It involves visualizing data through various
statistical and graphical techniques to identify outliers, correlations,
clusters, and anomalies. Exploratory analysis is often the first step in the
data analysis process, helping data scientists form hypotheses and guide
further investigation.
Predictive
Analysis: Predictive analysis focuses on building models that can make
predictions or forecasts based on historical data. It involves using
statistical and machine learning techniques to develop predictive models that
can estimate future outcomes or trends. Predictive analysis leverages the
insights gained from descriptive and exploratory analysis to build accurate models
capable of making informed predictions.
Q.2 A class has 25
students. Create a data set of marks of the students in Mathematics out of a
maximum of 50 marks. Discuss and draw, which chart will be best for Visualization
& Interpretation. Justify your reasons in support of your answer.
Q.3 What is the
purpose of using Apache SPARK, HIVE and HBASE, explain with supporting example.
Q.4 Create a sample
data of the marks of 20 students in five different subjects using MSExcel. Discuss
the different chart and graphing library packages supported by R programming
language. Write programs using R programming language to create four different
plots using this data.
Q.5 What is PageRank?
Discuss the basic principle of flow model in PageRank. Explain different
mechanisms of finding pagerank?
Q.6 Discuss different
data structures in R. Write program using R for the following tasks: (i)
Computation of income tax of a vector of size 10, consisting of the total annual
income of 10 different persons. The tax computation should be 10%, if annual
income is below 5 lakhs and 20% if it is above 5 lakhs. (ii) Matrix addition, subtraction
and multiplication (iii) Finding inverse of a matrix
Q.7 Discuss the need
for Statistical Hypothesis Testing with the help of an example. Explain types
of Errors in Hypothesis Testing.
Q.8 Discuss the
Classification, Clustering and Association Rules with different examples.
Explain, where we can use Random Forest Algorithm? Use R programming language
to discuss Random Forest Algorithm.
Q.9 What is NoSQL
database? Discuss how does a Column Database and Document database Work? List
and briefly discuss Graph database examples.
Q.10 Explain the
process and issues of the following: Advertising on web, Recommendation system,
Mining of social networks.
MCS 226 Solved
Assignment 2024-25 : All assignments are in PDF format which would be send
on email/WhatsApp (9958676204) just after payment.
MCS 226 Solved
Assignment 2024-25, MCS 226 Solved Assignment 2024-25, MCS 226 Solved Assignment
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they will also checkout their assignment marks & result. All this is often
available in a web mode. After submitting the assignment, you'll check you MCS
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