Monday 26 February 2024

Python Coding challenge - Day 138 | What is the output of the following Python Code?

 


The above code deletes elements from index 2 to index 3 (not including index 4) in the list num and then prints the updated list. Let's break it down:

num = [10, 20, 30, 40, 50]

This line initializes a list named num with the elements 10, 20, 30, 40, and 50.

del(num[2:4])

This line uses the del statement to delete elements from index 2 up to (but not including) index 4 in the list. So, it removes the elements at index 2 and 3 (30 and 40) from the list.

After this operation, the list num becomes [10, 20, 50].

print(num)

Finally, the code prints the updated list, which is [10, 20, 50].

So, the output of the code will be:

[10, 20, 50]

IBM Data Analytics with Excel and R Professional Certificate

 


What you'll learn

Master the most up-to-date practical skills and knowledge data analysts use in their daily roles

Learn how to perform data analysis, including data preparation, statistical analysis, and predictive modeling using R, R Studio, and Jupyter

Utilize Excel spreadsheets to perform a variety of data analysis tasks like data wrangling, using pivot tables, data mining, & creating charts

Communicate your data findings using various data visualization techniques including, charts, plots & interactive dashboards with Cognos and R Shiny

Join Free: IBM Data Analytics with Excel and R Professional Certificate

Professional Certificate - 9 course series

Prepare for the in-demand field of data analytics. In this program, you’ll learn high valued skills like Excel, Cognos Analytics, and R programming language to get job-ready in less than 3 months.

Data analytics is a strategy-based science where data is analyzed to find trends, answer questions, shape business processes, and aid decision-making. This Professional Certificate focuses on data analysis using Microsoft Excel and R programming language. If you’re interested in using Python, please explore the IBM Data Analyst PC. 

This program will teach you the foundational data skills employers are seeking for entry level data analytics roles and will provide a portfolio of projects and a Professional Certificate from IBM to showcase your expertise to potential employers.

You’ll learn the latest skills and tools used by professional data analysts and upon successful completion of this program, you will be able to work with Excel spreadsheets, Jupyter Notebooks, and R Studio to analyze data and create visualizations. You will also use the R programming language to complete the entire data analysis process,  including data preparation, statistical analysis, data visualization, predictive modeling and creating interactive dashboards. Lastly, you’ll learn how to communicate your data findings and prepare a summary report.

This program is ACE® and FIBAA recommended—when you complete, you can earn up to 15 college credits and 4 ECTS credits.

Applied Learning Project

You will complete hands-on labs to build your portfolio and  gain practical experience with Excel, Cognos Analytics, SQL, and the R programing language and related libraries for data science, including Tidyverse, Tidymodels, R Shiny, ggplot2, Leaflet, and rvest.

Projects include:

Analyzing fleet vehicle inventory data using pivot tables.

Using key performance indicator (KPI) data from car sales to create an interactive dashboard.

Identifying patterns in countries’ COVID-19 testing data rates using R.

Using SQL with the RODBC R package to analyze foreign grain markets.

Creating linear and polynomial regression models and comparing them with weather station data to predict precipitation.

Using the R Shiny package to create a dashboard that examines trends in census data.

Using hypothesis testing and predictive modeling skills to build an interactive dashboard with the R Shiny package and a dynamic Leaflet map widget to investigate how weather affects bike-sharing demand.

Predict Sales Revenue with scikit-learn

 


What you'll learn

Build simple linear regression models in Python

Apply scikit-learn and statsmodels to regression problems

Employ explorartory data analysis (EDA) with seaborn and pandas

Explain linear regression to both technical and non-technical audiences

Join Free: Predict Sales Revenue with scikit-learn

About this Guided Project

In this 2-hour long project-based course, you will build and evaluate a simple linear regression model using Python. You will employ the scikit-learn module for calculating the linear regression, while using pandas for data management, and seaborn for plotting. You will be working with the very popular Advertising data set to predict sales revenue based on advertising spending through mediums such as TV, radio, and newspaper. 

By the end of this course, you will be able to:

- Explain the core ideas of linear regression to technical and non-technical audiences
- Build a simple linear regression model in Python with scikit-learn
- Employ Exploratory Data Analysis (EDA) to small data sets with seaborn and pandas
- Evaluate a simple linear regression model using appropriate metrics

This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Jupyter and Python 3.7 with all the necessary libraries pre-installed.

Notes:

- You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want.
- This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Generative AI: Enhance your Data Analytics Career

 


What you'll learn

Describe how you can use Generative AI tools and techniques in the context of data analytics across industries

Implement various data analytic processes such as data preparation, analysis, visualization and storytelling using Generative AI tools

Evaluate real-world case studies showcasing the successful application of Generative AI in deriving meaningful insights 

 Analyze the ethical considerations and challenges associated with using Generative AI in data analytics

Join Free: Generative AI: Enhance your Data Analytics Career

There are 3 modules in this course

This comprehensive course unravels the potential of generative AI in data analytics. The course will provide an in-depth knowledge of the fundamental concepts, models, tools, and generative AI applications regarding the data analytics landscape. 

In this course, you will examine real-world applications and use generative AI to gain data insights using techniques such as prompts, visualization, storytelling, querying and so on. In addition, you will understand the ethical implications, considerations, and challenges of using generative AI in data analytics across different industries.

You will acquire practical experience through hands-on labs where you will leverage generative AI models and tools such as ChatGPT, ChatCSV, Mostly.AI, SQLthroughAI and more.

Finally, you will apply the concepts learned throughout the course to a data analytics project. Also, you will have an opportunity to test your knowledge with practice and graded quizzes and earn a certificate. 

This course is suitable for both practicing data analysts as well as learners aspiring to start a career in data analytics. It requires some basic knowledge of data analytics, prompt engineering, Python programming and generative artificial intelligence.

Data Analyst Career Guide and Interview Preparation

 


What you'll learn

Describe the role of a data analyst and some career path options as well as the prospective opportunities in the field.

Explain how to build a foundation for a job search, including researching job listings, writing a resume, and making a portfolio of work.

Summarize what a candidate can expect during a typical job interview cycle, different types of interviews, and how to prepare for interviews.

Explain how to give an effective interview, including techniques for answering questions and how to make a professional personal presentation.

Join Free: Data Analyst Career Guide and Interview Preparation

There are 4 modules in this course

Data analytics professionals are in high demand around the world, and the trend shows no sign of slowing. There are lots of great jobs available, but lots of great candidates too. How can you get the edge in such a competitive field?

This course will prepare you to enter the job market as a great candidate for a data analyst position. It provides practical techniques for creating essential job-seeking materials such as a resume and a portfolio, as well as auxiliary tools like a cover letter and an elevator pitch. You will learn how to find and assess prospective job positions, apply to them, and lay the groundwork for interviewing. 

The course doesn’t stop there, however. You will also get inside tips and steps you can use to perform professionally and effectively at interviews. You will learn how to approach a take-home challenges and get to practice completing them. Additionally, it provides information about the regular functions and tasks of data analysts, as well as the opportunities of the profession and some options for career development.

You will get guidance from a number of experts in the data industry through the course. They will discuss their own career paths and talk about what they have learned about networking, interviewing, solving coding problems, and fielding other questions you may encounter as a candidate. Let seasoned data analysis professionals share their experience to help you get ahead and land the job you want.

Machine Learning With Big Data

 


Build your subject-matter expertise

This course is part of the Big Data Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

Join Free: Machine Learning With Big Data

There are 7 modules in this course

Want to make sense of the volumes of data you have collected?  Need to incorporate data-driven decisions into your process?  This course provides an overview of machine learning techniques to explore, analyze, and leverage data.  You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems.

At the end of the course, you will be able to:

Design an approach to leverage data using the steps in the machine learning process.
Apply machine learning techniques to explore and prepare data for modeling.
Identify the type of machine learning problem in order to apply the appropriate set of techniques.
Construct models that learn from data using widely available open source tools.
Analyze big data problems using scalable machine learning algorithms on Spark.

Sunday 25 February 2024

Python Coding challenge - Day 137 | What is the output of the following Python Code?

 


a = [1, 2, 3, 4]

b = [1, 2, 5]

print(a < b)

Two lists, a and b, are defined.

a is [1, 2, 3, 4]

b is [1, 2, 5]

The code uses the less-than (<) operator to compare the two lists a and b. This comparison is performed element-wise.

The first elements of both lists are equal (1 == 1).

The second elements are equal (2 == 2).

The third elements are different (3 in a and 5 in b).

The less-than comparison stops at the first differing element. Since 3 is less than 5, the entire comparison evaluates to True.

The result of the comparison is printed using print(a < b), and it will output True.

So, the output of the code is:

True

This is because, in lexicographical order, the list a is considered less than the list b due to the first differing element at index 2.

Saturday 24 February 2024

3D contour plot using Python

 


import numpy as np

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# Create a meshgrid of x and y values
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)

# Define a function to calculate the z values (height) based on x and y
def f(x, y):
    return np.sin(np.sqrt(x**2 + y**2))

# Calculate the z values for the meshgrid
Z = f(X, Y)

# Create a three-dimensional contour plot
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111, projection='3d')
contour = ax.contour3D(X, Y, Z, 50, cmap='viridis')

# Add labels and a colorbar
ax.set_xlabel('X-axis')
ax.set_ylabel('Y-axis')
ax.set_zlabel('Z-axis')
fig.colorbar(contour, ax=ax, label='Z values')

# Show the plot
plt.show()

#clcoding.com

Friday 23 February 2024

Lists data structures in Python

 


Example 1: Creating a List

In [2]:
[1, 2, 3, 4, 5]
['apple', 'banana', 'orange']
[1, 'hello', 3.14, True]
[]

Example 2: Accessing Elements in a List

In [3]:
apple
5
[2, 3, 4]
['apple', 'banana']

Example 3: Modifying Elements in a List

In [4]:
['apple', 'grape', 'orange']
['apple', 'grape', 'orange', 'kiwi']
['apple', 'grape', 'orange', 'kiwi', 'mango']

Example 4: Removing Elements from a List

In [4]:
['apple', 'grape', 'kiwi', 'mango', 'pineapple']
Popped fruit: grape
['apple', 'kiwi', 'mango', 'pineapple']

Example 5: List Operations

In [5]:
4
True
[1, 2, 3, 4, 5, 6, 7, 8]

Example 6: List Iteration

In [6]:
Fruit: apple
Fruit: kiwi
Fruit: mango
Fruit: pineapple
Index: 0, Fruit: apple
Index: 1, Fruit: kiwi
Index: 2, Fruit: mango
Index: 3, Fruit: pineapple

Popular Posts

Categories

AI (23) Android (24) AngularJS (1) Assembly Language (2) aws (16) Azure (7) BI (10) book (3) Books (92) C (77) C# (12) C++ (82) Course (60) Coursera (167) coursewra (1) Cybersecurity (22) data management (9) Data Science (68) Data Strucures (6) Deep Learning (9) Django (6) Downloads (3) edx (2) Engineering (14) Excel (13) Factorial (1) Finance (5) flutter (1) FPL (17) Google (17) Hadoop (3) HTML&CSS (46) IBM (19) IoT (1) IS (25) Java (92) Leet Code (4) Machine Learning (37) Meta (18) MICHIGAN (4) microsoft (3) Pandas (3) PHP (20) Projects (29) Python (693) Python Coding Challenge (135) Questions (2) R (70) React (6) Scripting (1) security (3) Selenium Webdriver (2) Software (17) SQL (38) UX Research (1) web application (8)

Followers

Person climbing a staircase. Learn Data Science from Scratch: online program with 21 courses