CS3361 DATA SCIENCE LABORATORY L T P C 0 0 4 lab manual

. a. Use the diabetes data set from UCI and Pima Indians Diabetes data set for performing the following:
Univariate analysis: Frequency, Mean, Median, Mode, Variance, Standard Deviation, Skewness and Kurtosis.

CS3361 DATA SCIENCE LABORATORY L T P C 0 0 4 lab manual 

CS3361 DATA SCIENCE LABORATORY L T P C 0 0 4 2 COURSE OBJECTIVES:

  To understand the python libraries for data science 

 To understand the basic Statistical and Probability measures for data science. 

 To learn descriptive analytics on the benchmark data sets. 

 To apply correlation and regression analytics on standard data sets. 

 To present and interpret data using visualization packages in Python. 

LIST OF EXPERIMENTS: 

1. Download, install and explore the features of NumPy, SciPy, Jupyter, Statsmodels and Pandas packages. 

2. Working with Numpy arrays 

3. Working with Pandas data frames 

4. Reading data from text files, 

Excel and the web and exploring various commands for doing descriptive analytics on the Iris data set. 

5. Use the diabetes data set from UCI and Pima Indians Diabetes data set for performing the following: a. Univariate analysis: Frequency, Mean, Median, Mode, Variance, Standard Deviation, Skewness and Kurtosis. 

b. Bivariate analysis: Linear and logistic regression modeling 

c. Multiple Regression analysis 

d. Also compare the results of the above analysis for the two data sets. 

6. Apply and explore various plotting functions on UCI data sets. 

a. Normal curves 

b. Density and contour plots 

c. Correlation and scatter plots 

d. Histograms 

e. Three dimensional plotting 

7. Visualizing Geographic Data with Basemap List of Equipments:(30 Students per Batch) Tools: Python, Numpy, Scipy, Matplotlib, Pandas, statmodels, seaborn, plotly, bokeh 

Note: Example data sets like: UCI, Iris, Pima Indians Diabetes etc. 

TOTAL: 60 PERIODS 

COURSE OUTCOMES: At the end of this course, the students will be able to: CO1: Make use of the python libraries for data science CO2: Make use of the basic Statistical and Probability measures for data science. CO3: Perform descriptive analytics on the benchmark data sets. CO4: Perform correlation and regression analytics on standard data sets CO5: Present and interpret data using visualization packages in Python

CS3351 DIGITAL PRINCIPLES AND COMPUTER ORGANIZATION Study Material

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CS3351 DIGITAL PRINCIPLESAND COMPUTER ORGANIZATION L T P C 3 0 2 4

COURSE OBJECTIVES:

· To analyze and design combinational circuits.

· To analyze and design sequential circuits

· To understand the basic structure and operation of a digital computer.

· To study the design of data path unit, control unit for processor and to familiarize with the hazards.

· To understand the concept of various memories and I/O interfacing.

UNIT I COMBINATIONAL LOGIC  

Combinational Circuits – Karnaugh Map - Analysis and Design Procedures – Binary Adder – Subtractor – Decimal Adder - Magnitude Comparator – Decoder – Encoder – Multiplexers - Demultiplexers

UNIT II SYNCHRONOUS SEQUENTIAL LOGIC

 Introduction to Sequential Circuits – Flip-Flops – operation and excitation tables, Triggering of FF, Analysis and design of clocked sequential circuits – Design – Moore/Mealy models, state minimization, state assignment, circuit implementation - Registers – Counters.

UNIT III COMPUTER FUNDAMENTALS  

Functional Units of a Digital Computer: Von Neumann Architecture – Operation and Operands of Computer Hardware Instruction – Instruction Set Architecture (ISA): Memory Location, Address and Operation – Instruction and Instruction Sequencing – Addressing Modes, Encoding of Machine Instruction – Interaction between Assembly and High Level Language.

UNIT IV PROCESSOR 9

Instruction Execution – Building a Data Path – Designing a Control Unit – Hardwired Control, Microprogrammed Control – Pipelining – Data Hazard – Control Hazards.

UNIT V MEMORY AND I/O 9 Memory Concepts and Hierarchy – Memory Management – Cache Memories: Mapping and Replacement Techniques – Virtual Memory – DMA – I/O – Accessing I/O: Parallel and Serial Interface – Interrupt I/O – Interconnection Standards: USB, SATA

 45 PERIODS

PRACTICAL EXERCISES: 30 PERIODS

1. Verification of Boolean theorems using logic gates.

2. Design and implementation of combinational circuits using gates for arbitrary functions.

3. Implementation of 4-bit binary adder/subtractor circuits.

4. Implementation of code converters.

5. Implementation of BCD adder, encoder and decoder circuits

6. Implementation of functions using Multiplexers.

7. Implementation of the synchronous counters

8. Implementation of a Universal Shift register.

 9. Simulator based study of Computer Architecture

COURSE OUTCOMES: At the end of this course, the students will be able to:

CO1 : Design various combinational digital circuits using logic gates

CO2 : Design sequential circuits and analyze the design procedures

CO3 : State the fundamentals of computer systems and analyze the execution of an instruction

CO4 : Analyze different types of control design and identify hazards

CO5 : Identify the characteristics of various memory systems and I/O communication

TOTAL:75 PERIODS

TEXT BOOKS

1. M. Morris Mano, Michael D. Ciletti, “Digital Design : With an Introduction to the Verilog HDL, VHDL, and System Verilog”, Sixth Edition, Pearson Education, 2018.

2. David A. Patterson, John L. Hennessy, “Computer Organization and Design, The Hardware/Software Interface”, Sixth Edition, Morgan Kaufmann/Elsevier, 2020.

REFERENCES

1. Carl Hamacher, Zvonko Vranesic, Safwat Zaky, Naraig Manjikian, “Computer Organization and Embedded Systems”, Sixth Edition, Tata McGraw-Hill, 2012.

2. William Stallings, “Computer Organization and Architecture – Designing for Performance”, Tenth Edition, Pearson Education, 2016.

3. M. Morris Mano, “Digital Logic and Computer Design”, Pearson Education, 2016

CS3361 Exploring various commands for doing descriptive analytics on the Iris data set.

CS3362 DATA SCIENCE LABORATORY L T P C 0 0 4

 CS3362 DATA SCIENCE LABORATORY L T P C 0 0 4 2 COURSE OBJECTIVES:

 · To understand the python libraries for data science 

· To understand the basic Statistical and Probability measures for data science. 

· To learn descriptive analytics on the benchmark data sets. 

· To apply correlation and regression analytics on standard data sets. 

· To present and interpret data using visualization packages in Python. 

LIST OF EXPERIMENTS: 

1. Download, install and explore the features of NumPy, SciPy, Jupyter, Statsmodels and Pandas packages. 

2. Working with Numpy arrays 

3. Working with Pandas data frames 

4. Reading data from text files, Excel and the web and exploring various commands for doing descriptive analytics on the Iris data set. 

5. Use the diabetes data set from UCI and Pima Indians Diabetes data set for performing the following:

a. Univariate analysis: Frequency, Mean, Median, Mode, Variance, Standard Deviation, Skewness and Kurtosis.

 b. Bivariate analysis: Linear and logistic regression modeling

c. Multiple Regression analysis

d. Also compare the results of the above analysis for the two data sets. 

6. Apply and explore various plotting functions on UCI data sets.

a. Normal curves

b. Density and contour plots

c. Correlation and scatter plots

d. Histograms

e. Three dimensional plotting 

7. Visualizing Geographic Data with Basemap List of Equipments:(30 Students per Batch) Tools: Python, Numpy, Scipy, Matplotlib, Pandas, statmodels, seaborn, plotly, bokeh 

Note: Example data sets like: UCI, Iris, Pima Indians Diabetes etc. 

TOTAL: 60 PERIODS 

COURSE OUTCOMES: At the end of this course, the students will be able to: CO1: Make use of the python libraries for data science CO2: Make use of the basic Statistical and Probability measures for data science. CO3: Perform descriptive analytics on the benchmark data sets. CO4: Perform correlation and regression analytics on standard data sets CO5: Present and interpret data using visualization packages in Python

  

Ex 4c. Exploring various commands for doing descriptive analytics on the Iris data set. 

Aim

To explore various commands for doing descriptive analytics on the Iris data set. 

Procedure

To understand idea behind Descriptive Statistics.

Load the packages we will need and also the `iris` dataset.

load_iris()  loads in an object containing the iris dataset, which I stored in `iris_obj`. 

Basic statistics.

This number is the number of rows in the dataset, and can be obtained via `count()`.

Mean for every numeric column

Median for every numeric column

variance is a measure of dispersion, roughly the “average” squared distance of a data point from the mean.

The standard deviation is the square root of the variance and interpreted as the “average” distance a data point is from the mean.

The maximum and minimum values.

Program Code

import pandas as pd

from pandas import DataFrame

from sklearn.datasets import load_iris   

# sklearn.datasetsincludes common example datasets

# A function to load in the iris dataset

iris_obj = load_iris()   

# Dataset preview

iris_obj.data   

iris = DataFrame(iris_obj.data, columns=iris_obj.feature_names,index=pd.Index([i for i in range(iris_obj.data.shape[0])])).join(DataFrame(iris_obj.target, columns=pd.Index(["species"]), index=pd.Index([i for i in range(iris_obj.target.shape[0])])))

iris # prints iris data

 

Commands

iris_obj.feature_names 

iris.count()

iris.mean()

iris.median() 

iris.var()

iris.std()

iris.max()

iris.min()

iris.describe()

 

Result

Exploring various commands for doing descriptive analytics on the Iris data set successfully executed.

Working with Numpy arrays CS3361 DATA SCIENCE LABORATORY L T P C 0 0 4 lab manual

  CS3361 DATA SCIENCE LABORATORY L T P C 0 0 4 lab manual 

CS3361 DATA SCIENCE LABORATORY L T P C 0 0 4 2 COURSE OBJECTIVES:

  To understand the python libraries for data science 

 To understand the basic Statistical and Probability measures for data science. 

 To learn descriptive analytics on the benchmark data sets. 

 To apply correlation and regression analytics on standard data sets. 

 To present and interpret data using visualization packages in Python. 

LIST OF EXPERIMENTS: 

2. Working with Numpy arrays 

CS3361 DATA SCIENCE LABORATORY

 CS3361 DATA SCIENCE LABORATORY L T P C 0 0 4 lab manual 

CS3361 DATA SCIENCE LABORATORY L T P C 0 0 4 2 COURSE OBJECTIVES:

  To understand the python libraries for data science 

 To understand the basic Statistical and Probability measures for data science. 

 To learn descriptive analytics on the benchmark data sets. 

 To apply correlation and regression analytics on standard data sets. 

 To present and interpret data using visualization packages in Python. 

LIST OF EXPERIMENTS: 

1. Download, install and explore the features of NumPy, SciPy, Jupyter, Statsmodels and Pandas packages. 

2. Working with Numpy arrays 

3. Working with Pandas data frames 

4. Reading data from text files, 

Excel and the web and exploring various commands for doing descriptive analytics on the Iris data set. 

5. Use the diabetes data set from UCI and Pima Indians Diabetes data set for performing the following: a. Univariate analysis: Frequency, Mean, Median, Mode, Variance, Standard Deviation, Skewness and Kurtosis. 

b. Bivariate analysis: Linear and logistic regression modeling 

c. Multiple Regression analysis 

d. Also compare the results of the above analysis for the two data sets. 

6. Apply and explore various plotting functions on UCI data sets. 

a. Normal curves 

b. Density and contour plots 

c. Correlation and scatter plots 

d. Histograms 

e. Three dimensional plotting 

7. Visualizing Geographic Data with Basemap List of Equipments:(30 Students per Batch) Tools: Python, Numpy, Scipy, Matplotlib, Pandas, statmodels, seaborn, plotly, bokeh 

Note: Example data sets like: UCI, Iris, Pima Indians Diabetes etc. 

TOTAL: 60 PERIODS 

COURSE OUTCOMES: At the end of this course, the students will be able to: CO1: Make use of the python libraries for data science CO2: Make use of the basic Statistical and Probability measures for data science. CO3: Perform descriptive analytics on the benchmark data sets. CO4: Perform correlation and regression analytics on standard data sets CO5: Present and interpret data using visualization packages in Python