Python for Data Analysis: From Basics to Advanced Data Science Techniques

 

Unlock the power of Python to analyze data, uncover insights, and drive decision-making with "Python for Data Analysis: From Basics to Advanced Data Science Techniques" Whether you're new to data analysis or looking to enhance your skills, this book offers a comprehensive journey through the tools, techniques, and concepts that make Python the go-to choice for data professionals.

Inside, you'll discover:

Foundational Python: Start from the basics of Python programming, including setting up your environment, understanding Python syntax, and exploring core concepts.

Mastering Pandas for Data Manipulation: Dive deep into Pandas for data cleaning, preparation, and manipulation, empowering you to handle and explore real-world datasets with ease.

Data Visualization Techniques: Learn to communicate your findings visually with Matplotlib and Seaborn, creating compelling and informative plots that bring your data to life.

Machine Learning Integration: Step into the world of machine learning with Scikit-Learn to apply predictive models to your data, from basic classification to complex regression tasks.

Advanced Data Analysis: Explore advanced topics, including working with big data using Dask, natural language processing (NLP), and an introduction to deep learning with TensorFlow and Keras.

Practical Projects and Case Studies: Apply what you've learned with hands-on projects and case studies that simulate real-world data analysis scenarios, enhancing your problem-solving skills and practical knowledge.

Future of Data Analysis: Look ahead to the emerging trends in data analysis and the ethical considerations of working with data, preparing you for the future of the field.

"Python for Data Analysis: From Basics to Advanced Data Science Techniques" is more than just a book; it's a comprehensive guide to becoming proficient in data analysis using Python. With clear explanations, practical examples, and step-by-step instructions, this book will equip you with the knowledge and skills you need to navigate the data landscape confidently and become an invaluable asset in your organization or field.

Hard Copy: Python for Data Analysis: From Basics to Advanced Data Science Techniques

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Python for Data Science: A Hands-On Introduction

 

A hands-on, real-world introduction to data analysis with the Python programming language, loaded with wide-ranging examples.

Python is an ideal choice for accessing, manipulating, and gaining insights from data of all kinds. Python for Data Science introduces you to the Pythonic world of data analysis with a learn-by-doing approach rooted in practical examples and hands-on activities. You’ll learn how to write Python code to obtain, transform, and analyze data, practicing state-of-the-art data processing techniques for use cases in business management, marketing, and decision support.

You will discover Python’s rich set of built-in data structures for basic operations, as well as its robust ecosystem of open-source libraries for data science, including NumPy, pandas, scikit-learn, matplotlib, and more. Examples show how to load data in various formats, how to streamline, group, and aggregate data sets, and how to create charts, maps, and other visualizations. Later chapters go in-depth with demonstrations of real-world data applications, including using location data to power a taxi service, market basket analysis to identify items commonly purchased together, and machine learning to predict stock prices.

Hard Copy: Python for Data Science: A Hands-On Introduction

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Data Engineering with AWS: Acquire the skills to design and build AWS-based data transformation pipelines like a pro 2nd ed. Edition

 

Looking to revolutionize your data transformation game with AWS? Look no further! From strong foundations to hands-on building of data engineering pipelines, our expert-led manual has got you covered.

Key Features

Delve into robust AWS tools for ingesting, transforming, and consuming data, and for orchestrating pipelines

Stay up to date with a comprehensive revised chapter on Data Governance

Build modern data platforms with a new section covering transactional data lakes and data mesh

Book Description

This book, authored by a seasoned Senior Data Architect with 25 years of experience, aims to help you achieve proficiency in using the AWS ecosystem for data engineering. This revised edition provides updates in every chapter to cover the latest AWS services and features, takes a refreshed look at data governance, and includes a brand-new section on building modern data platforms which covers; implementing a data mesh approach, open-table formats (such as Apache Iceberg), and using DataOps for automation and observability.

You'll begin by reviewing the key concepts and essential AWS tools in a data engineer's toolkit and getting acquainted with modern data management approaches. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how that transformed data is used by various data consumers. You’ll learn how to ensure strong data governance, and about populating data marts and data warehouses along with how a data lakehouse fits into the picture. After that, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. Then, you'll explore how the power of machine learning and artificial intelligence can be used to draw new insights from data. In the final chapters, you'll discover transactional data lakes, data meshes, and how to build a cutting-edge data platform on AWS.

By the end of this AWS book, you'll be able to execute data engineering tasks and implement a data pipeline on AWS like a pro!

What you will learn

Seamlessly ingest streaming data with Amazon Kinesis Data Firehose

Optimize, denormalize, and join datasets with AWS Glue Studio

Use Amazon S3 events to trigger a Lambda process to transform a file

Load data into a Redshift data warehouse and run queries with ease

Visualize and explore data using Amazon QuickSight

Extract sentiment data from a dataset using Amazon Comprehend

Build transactional data lakes using Apache Iceberg with Amazon Athena

Learn how a data mesh approach can be implemented on AWS

Who this book is for

This book is for data engineers, data analysts, and data architects who are new to AWS and looking to extend their skills to the AWS cloud. Anyone new to data engineering who wants to learn about the foundational concepts, while gaining practical experience with common data engineering services on AWS, will also find this book useful. A basic understanding of big data-related topics and Python coding will help you get the most out of this book, but it’s not a prerequisite. Familiarity with the AWS console and core services will also help you follow along.

Table of Contents

An Introduction to Data Engineering

Data Management Architectures for Analytics

The AWS Data Engineer’s Toolkit

Data Governance, Security, and Cataloging

Architecting Data Engineering Pipelines

Ingesting Batch and Streaming Data

Transforming Data to Optimize for Analytics

Identifying and Enabling Data Consumers

A Deeper Dive into Data Marts and Amazon Redshift

Orchestrating the Data Pipeline

Hard Copy: Data Engineering with AWS: Acquire the skills to design and build AWS-based data transformation pipelines like a pro 2nd ed. Edition

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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.

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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.

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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.

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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.

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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.

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10-question multiple-choice quiz on Pandas

 1. What is Pandas?

a. A data visualization library

b. A web development framework

c. A data manipulation library

d. A machine learning framework

2.  What is the primary data structure in Pandas for one-dimensional labeled data?

a. Series

b. DataFrame

c. Array

d. List

3. How do you read a CSV file into a Pandas DataFrame?

a. pd.load_csv()

b. pd.read_csv()

c. pd.read_data()

d. pd.import_csv()

4. How do you select a specific column in a Pandas DataFrame?

a. df.column('ColumnName')

b. df.select('ColumnName')

c. df['ColumnName']

d. df.get('ColumnName')

5. What is the purpose of the head() method in Pandas?

a. It gives the first few rows of the DataFrame

b. It returns the last rows of the DataFrame

c. It displays a summary statistics of the DataFrame

d. It provides information about the columns in the DataFrame

6. How do you handle missing values in a Pandas DataFrame?

a. Use the fillna() method

b. Use the remove_na() method

c. Use the drop_na() method

d. Pandas automatically handles missing values

7. What function is used to group data in Pandas based on one or more columns?

a. groupby()

b. aggregate()

c. sort()

d. combine()

8. How do you merge two DataFrames in Pandas based on a common column?

a. df.merge()

b. df.join()

c. df.concat()

d. df.combine()

9. What does the describe() method in Pandas provide?

a. Descriptive statistics of the DataFrame

b. A list of unique values in each column

c. Information about data types in the DataFrame

d. A summary of missing values in the DataFrame

10. What is the purpose of the to_csv() method in Pandas?

a. It saves the DataFrame to a CSV file

b. It converts the DataFrame to a Series

c. It exports the DataFrame to an Excel file

d. It prints the DataFrame to the console

Answer:

1. c, 

2. a, 

3. b, 

4. c, 

5. a, 

6. a, 

7. a, 

8. a, 

9. a, 

10. a

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Box and Whisker plot using Python

 

#!/usr/bin/env python

# coding: utf-8

# # Box and whisker plot using Python

# # 1. Matplotlib:

# In[1]:

import matplotlib.pyplot as plt

# Sample data

data = [7, 2, 15, 9, 12, 4, 11, 8, 13, 6]

# Create boxplot

plt.boxplot(data)

# Customize labels and title

plt.xlabel("Data")

plt.ylabel("Value")

plt.title("Boxplot with Matplotlib")

plt.show()

# # 2. Pandas:

# In[2]:

import pandas as pd

import matplotlib.pyplot as plt

# Sample DataFrame

data = pd.DataFrame({"values": [7, 2, 15, 9, 12, 4, 11, 8, 13, 6]})

# Create boxplot

data.plot.box()

# Customize labels and title

plt.xlabel("Data")

plt.ylabel("Value")

plt.title("Boxplot with Pandas")

plt.show()

# # 3. Seaborn:

# In[3]:

import seaborn as sns

# Sample data (same as before)

data = [7, 2, 15, 9, 12, 4, 11, 8, 13, 6]

# Create boxplot

sns.boxplot(data=data)

# Customize with hue (category) plot

data = {"category": ["A", "B", "A", "A", "B", "A", "A", "B", "B", "A"], "values": data}

sns.boxplot(x="category", y="values", data=data)

plt.show()

# In[ ]:

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Introduction to Calculus (Free Courses)

 

There are 5 modules in this course

The focus and themes of the Introduction to Calculus course address the most important foundations for applications of mathematics in science, engineering and commerce. The course emphasises the key ideas and historical motivation for calculus, while at the same time striking a balance between theory and application, leading to a mastery of key threshold concepts in foundational mathematics. 

Students taking Introduction to Calculus will: 

• gain familiarity with key ideas of precalculus, including the manipulation of equations and elementary functions (first two weeks), 

• develop fluency with the preliminary methodology of tangents and limits, and the definition of a derivative (third week),

• develop and practice methods of differential calculus with applications (fourth week),

• develop and practice methods of the integral calculus (fifth week).

Join Free: Introduction to Calculus

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The Python Bible 7 in 1: Volumes One To Seven (Beginner, Intermediate, Data Science, Machine Learning, Finance, Neural Networks, Computer Vision)

 

Become A Python Expert From Scratch!

Python's popularity is growing tremendously and it's becoming more and more relevant economically and technologically. The fields of appliaction of the language range from machine learning, over computer networking to business applications.

In this 7 in 1 version you get a full collection of The Python Bible series. From the first volume on, you will be lead on a structured way to the mastery of Python. Besides the basics and the intermediate concepts, you will also learn how to apply it in areas like machine learning, financial analysis and neural networks. At the end you will additionally be introduced to one of the most interesting fields of computer science, which is computer vision After reading this collection, you will not only understand the programming language but you will also be able to work on projects in the stated fields. You will become a true Python expert!

What You Will Learn:

Beginner Level:

• Basics of Programming with Python• Automation of Simple Processes• Programming of Modular Python Applications• Easy Transition to Other Languages (Java, C++ etc.)

Intermediate Level:

• Object-Oriented Programming• Network Programming• Penetration Testing with Python• Regular Expressions• Multithreading• XML Processing• Database Programming• Logging

Data Science:

• Analyzing and Processing Big Data• Statistical Calculations with Python• Visualization of Data• Working with NumPy, Matplotlib and Pandas

Machine Learning:

• Predicting Data with Machine Learning• Building Neural Networks with Tensorflow• Recognizing Handwritten Digits with Neural Networks• Applying Linear Models like Regression• K-Nearest-Neighbors Classification• K-Means Clustering• Support Vector Machines

Finance:

• Financial Analysis with Python• Analyzing and Graphing Stock Data• Plotting Trendlines• Predicting Share Prices with Machine Learning

Neural Networks:

• Generating Poetic Texts with Neural Networks• Predicting Sequential Data (Stocks, Weather etc.)• Processing Audio and Video Data• Recognizing Objects Like Horses, Cars and Trucks on Images• Understanding Recurrent Neural Networks• Understanding Convolutional Neural Networks

Computer Vision:

• Making unreadable texts readable again with thresholding• Extracting essential information out of images and videos• Edge detection• Template matching and feature matching• Movement detection in videos• Professional object recognition with OpenCV

Start Your Journey And Become A Python Expert With The Python Bible!

Hard Copy: The Python Bible 7 in 1: Volumes One To Seven (Beginner, Intermediate, Data Science, Machine Learning, Finance, Neural Networks, Computer Vision)

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Python for Data Analysts and Scientists: Jump start your career in Data Analysis and Data Science Field

 

This is an excellent book for those who want to Jumpstart their career in Data Analytics and Data Scientist field.

My interest in learning Python script faced a challenging question - “Where shall I start from?”. I browsed through numerous online videos and training materials but with little success. After I agreed to pay a reasonable amount, a training course from a well-known e-learning platform gave me introductory knowledge on Python script. Learning the basic Python commands is one thing, whereas applying them to real life problems is another. For many months, the question - “Which Python commands are important in solving real-life problems?” bothered me a lot. It took me several sleepless nights, and a frantic lookout for a concise list of Python commands from an ocean of online information. My hands-on experiences designing Machine Learning models, performing root cause analysis by statistical hypothesis, and providing consultation as a Data Scientist, helped me learn the most crucial Python commands. The birth of this book is from the thoughts of my struggle in mastering and applying the Python script for resolving numerous challenging tasks. This book concisely lists the essential commands, the data visualization technics, and the statistical knowledge. I have mindfully placed the contents of this book for the day-to-day activities of a Data Analyst and a Data Scientist. This book aims to provide a quick starting platform for those who want to dive into the vast field of Machine Learning and Data Analytics. Further, this book will be a quick reference for those already in this field. With the hope of helping beginners and practitioners, and with a silent prayer of goodwill, I walk you through the simple steps to the proficiency in Python. Let us dive in and enjoy the journey into the world of Python.

Hard Copy: Python for Data Analysts and Scientists: Jump start your career in Data Analysis and Data Science Field

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Distance Measures in Data Science with Algorithms

Distance Measures in data science with algorithms

1. Euclidean Distance:

import numpy as np

def euclidean_distance(p1, p2):

    return np.sqrt(np.sum((p1 - p2) ** 2))

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Euclidean distance:", euclidean_distance(point1, point2))

#clcoding.com

Euclidean distance: 2.8284271247461903

2. Manhattan Distance:

import numpy as np

def manhattan_distance(p1, p2):

    return np.sum(np.abs(p1 - p2))

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Manhattan distance:", manhattan_distance(point1, point2))

#clcoding.com

Manhattan distance: 4

3. Cosine Similarity:

from scipy.spatial import distance

def cosine_similarity(p1, p2):

    return 1 - distance.cosine(p1, p2)

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Cosine similarity:", cosine_similarity(point1, point2))

#clcoding.com

Cosine similarity: 0.9838699100999074

4. Minkowski Distance:

import numpy as np

def minkowski_distance(p1, p2, r):

    return np.power(np.sum(np.power(np.abs(p1 - p2), r)), 1/r)

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Minkowski distance:", minkowski_distance(point1, point2, 3))

#clcoding.com

Minkowski distance: 2.5198420997897464

5. Chebyshev Distance:

import numpy as np

def chebyshev_distance(p1, p2):

    return np.max(np.abs(p1 - p2))

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Chebyshev distance:", chebyshev_distance(point1, point2))

#clcoding.com

Chebyshev distance: 2

6. Hamming Distance:

import jellyfish

def hamming_distance(s1, s2):

    return jellyfish.hamming_distance(s1, s2)

# Example usage

string1 = "hello"

string2 = "hallo"

print("Hamming distance:", hamming_distance(string1, string2))

#clcoding.com

Hamming distance: 1

7. Jaccard Similarity:

def jaccard_similarity(s1, s2):

    set1 = set(s1)

    set2 = set(s2)

    intersection = set1.intersection(set2)

    union = set1.union(set2)

    return len(intersection) / len(union)

# Example usage

string1 = "hello"

string2 = "hallo"

print("Jaccard similarity:", jaccard_similarity(string1, string2))

#clcoding.com

Jaccard similarity: 0.6

8. Sørensen-Dice Index:

def sorensen_dice_index(s1, s2):

    set1 = set(s1)

    set2 = set(s2)

    intersection = set1.intersection(set2)

    return (2 * len(intersection)) / (len(set1) + len(set2))

# Example usage

string1 = "hello"

string2 = "hallo"

print("Sørensen-Dice index:", sorensen_dice_index(string1, string2))

#clcoding.com

Sørensen-Dice index: 0.75

9. Haversine Distance:

def haversine_distance(lat1, lon1, lat2, lon2):

    R = 6371.0  # Radius of the earth in km

    dLat = np.deg2rad(lat2 - lat1)

    dLon = np.deg2rad(lon2 - lon1)

    a = np.sin(dLat / 2)**2 + np.cos(np.deg2rad(lat1)) * np.cos(np.deg2rad(lat2)) * np.sin(dLon / 2)**2

    c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a))

    return R * c

# Example usage

print("Haversine distance:", haversine_distance(51.5074, 0.1278, 40.7128, -74.0060))

#clcoding.com

  Input In [14]

    a = np.sin(dLat / 2)**2 + np.cos(np.deg2rad(lat1)) *

                                                         ^

SyntaxError: invalid syntax

10. Mahalanobis Distance:

from scipy.spatial.distance import cdist

def mahalanobis_distance(X, Y):

    return cdist(X.reshape(1,-1), Y.reshape(1,-1), 'mahalanobis', VI=np.cov(X))

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Mahalanobis distance:", mahalanobis_distance(point1, point2))

#clcoding.com

Mahalanobis distance: [[1.41421356]]

11. Pearson Correlation:

from scipy.stats import pearsonr

def pearson_correlation(X, Y):

    return pearsonr(X, Y)[0]

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Pearson correlation:", pearson_correlation(point1, point2))

#clcoding.com

Pearson correlation: 1.0

12. Squared Euclidean Distance:

def squared_euclidean_distance(X, Y):

    return euclidean_distance(X, Y)**2

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Squared Euclidean distance:", squared_euclidean_distance(point1, point2))

#clcoding.com

Squared Euclidean distance: 8.000000000000002

13. Jensen-Shannon Divergence:

def jensen_shannon_divergence(X, Y):

    M = 0.5 * (X + Y)

    return np.sqrt(0.5 * (rel_entr(X, M).sum() + rel_entr(Y, M).sum()))

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Jensen-Shannon divergence:", jensen_shannon_divergence(point1, point2))

#clcoding.com

Jensen-Shannon divergence: 0.6569041853099059

14. Chi-Square Distance:

def chi_square_distance(X, Y):

    X = X / np.sum(X)

    Y = Y / np.sum(Y)

    return np.sum((X - Y) ** 2 / (X + Y))

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Chi-Square distance:", chi_square_distance(point1, point2))

#clcoding.com

Chi-Square distance: 0.01923076923076923

15. Spearman Correlation:

from scipy.stats import spearmanr

def spearman_correlation(X, Y):

    return spearmanr(X, Y)[0]

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Spearman correlation:", spearman_correlation(point1, point2))

#clcoding.com

Spearman correlation: 0.9999999999999999

16. Canberra Distance:

from scipy.spatial.distance import canberra

def canberra_distance(X, Y):

    return canberra(X, Y)

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Canberra distance:", canberra_distance(point1, point2))

#clcoding.com

Canberra distance: 0.8333333333333333

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10 different data charts using Python

 # 10 different data charts using Python

pip install matplotlib seaborn

# 1. Line Chart:

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]

y = [10, 12, 5, 8, 3]

plt.plot(x, y)

plt.title('Line Chart')

plt.xlabel('X-axis')

plt.ylabel('Y-axis')

plt.show()

#clcoding.com

# 2. Bar Chart:

import matplotlib.pyplot as plt

categories = ['A', 'B', 'C', 'D']

values = [25, 40, 30, 20]

plt.bar(categories, values)

plt.title('Bar Chart')

plt.xlabel('Categories')

plt.ylabel('Values')

plt.show()

#clcoding.com

# 3. Pie Chart:

import matplotlib.pyplot as plt

labels = ['Category A', 'Category B', 'Category C']

sizes = [30, 45, 25]

plt.pie(sizes, labels=labels, autopct='%1.1f%%')

plt.title('Pie Chart')

plt.show()

#clcoding.com

# 4. Histogram:

import matplotlib.pyplot as plt

import numpy as np

data = np.random.randn(1000)

plt.hist(data, bins=30, edgecolor='black')

plt.title('Histogram')

plt.xlabel('Values')

plt.ylabel('Frequency')

plt.show()

#clcoding.com

# 5. Scatter Plot:

import matplotlib.pyplot as plt

import numpy as np

x = np.random.rand(50)

y = 2 * x + 1 + 0.1 * np.random.randn(50)

plt.scatter(x, y)

plt.title('Scatter Plot')

plt.xlabel('X-axis')

plt.ylabel('Y-axis')

plt.show()

#clcoding.com

# 6. Box Plot:

import seaborn as sns

import numpy as np

data = [np.random.normal(0, std, 100) for std in range(1, 4)]

sns.boxplot(data=data)

plt.title('Box Plot')

plt.xlabel('Category')

plt.ylabel('Values')

plt.show()

#clcoding.com

# 7. Violin Plot:

import seaborn as sns

import numpy as np

data = [np.random.normal(0, std, 100) for std in range(1, 4)]

sns.violinplot(data=data)

plt.title('Violin Plot')

plt.xlabel('Category')

plt.ylabel('Values')

plt.show()

#clcoding.com

# 8. Heatmap:

import seaborn as sns

import numpy as np

data = np.random.rand(10, 10)

sns.heatmap(data, annot=True)

plt.title('Heatmap')

plt.show()

#clcoding.com

# 9. Area Chart:

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]

y1 = [10, 15, 25, 30, 35]

y2 = [5, 10, 20, 25, 30]

plt.fill_between(x, y1, y2, color='skyblue', alpha=0.4)

plt.title('Area Chart')

plt.xlabel('X-axis')

plt.ylabel('Y-axis')

plt.show()

#clcoding.com

# 10. Radar Chart:

import matplotlib.pyplot as plt

import numpy as np

labels = np.array([' A', ' B', ' C', ' D', ' E'])

data = np.array([4, 5, 3, 4, 2])

angles = np.linspace(0, 2 * np.pi, len(labels), endpoint=False)

data = np.concatenate((data, [data[0]]))

angles = np.concatenate((angles, [angles[0]]))

plt.polar(angles, data, marker='o')

plt.fill(angles, data, alpha=0.25)

plt.title('Radar Chart')

plt.show()

#clcoding.com

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Learn SQL Basics for Data Science Specialization

 

What you'll learn

Use SQL commands to filter, sort, & summarize data; manipulate strings, dates, & numerical data from different sources for analysis

Assess and create datasets to solve your business questions and problems using SQL

Use the collaborative Databricks workspace and create an end-to-end pipeline that reads data, transforms it, and saves the result

Develop a project proposal & select your data, perform statistical analysis & develop metrics, and present your findings & make recommendations

Join Free: Learn SQL Basics for Data Science Specialization

Specialization - 4 course series

This Specialization is intended for a learner with no previous coding experience seeking to develop SQL query fluency. Through four progressively more difficult SQL projects with data science applications, you will cover topics such as SQL basics, data wrangling, SQL analysis, AB testing, distributed computing using Apache Spark, Delta Lake and more. These topics will prepare you to apply SQL creatively to analyze and explore data; demonstrate efficiency in writing queries; create data analysis datasets; conduct feature engineering, use SQL with other data analysis and machine learning toolsets; and use SQL with unstructured data sets. 

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Data Visualization and Dashboards with Excel and Cognos

 

What you'll learn

Create basic visualizations such as line graphs, bar graphs, and pie charts using Excel spreadsheets.

Explain the important role charts play in telling a data-driven story. 

Construct advanced charts and visualizations such as Treemaps, Sparklines, Histogram, Scatter Plots, and Filled Map Charts.

Build and share interactive dashboards using Excel and Cognos Analytics.

Join Free: Data Visualization and Dashboards with Excel and Cognos

There are 4 modules in this course

Learn how to create data visualizations and dashboards using spreadsheets and analytics tools. This course covers some of the first steps for telling a compelling story with your data using various types of charts and graphs. You'll learn the basics of visualizing data with Excel and IBM Cognos Analytics without having to write any code. 

You'll start by creating simple charts in Excel such as line, pie and bar charts. You will then create more advanced visualizations with Treemaps, Scatter Charts, Histograms, Filled Map Charts, and Sparklines. Next you’ll also work with the Excel PivotChart feature as well as assemble several visualizations in an Excel dashboard.  

This course also teaches you how to use business intelligence (BI) tools like Cognos Analytics  to create interactive dashboards. By the end of the course you will have an appreciation for the key role that data visualizations play in communicating your data analysis findings, and the ability to effectively create them. 

Throughout this course there will be numerous hands-on labs to help you develop practical experience for working with Excel and Cognos. There is also a final project in which you’ll create a set of data visualizations and an interactive dashboard to add to your portfolio, which you can share with peers, professional communities or prospective employers.

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Data Visualization with Tableau Specialization

 

What you'll learn

Examine, navigate, and learn to use the various features of Tableau

Assess the quality of the data and perform exploratory analysis

 Create and design visualizations and dashboards for your intended audience

 Combine the data to and follow the best practices to present your story

Join Free: Data Visualization with Tableau Specialization

Specialization - 5 course series

In 2020 the world will generate 50 times the amount of data as in 2011. And 75 times the number of information sources (IDC, 2011). Being able to use this data provides huge opportunities and to turn these opportunities into reality, people need to use data to solve problems.

 This Specialization, in collaboration with Tableau, is intended for newcomers to data visualization with no prior experience using Tableau. We leverage Tableau's library of resources to demonstrate best practices for data visualization and data storytelling. You will view examples from real world business cases and journalistic examples from leading media companies. 

By the end of this specialization, you will be able to generate powerful reports and dashboards that will help people make decisions and take action based on their business data. You will use Tableau to create high-impact visualizations of common data analyses to help you see and understand your data. You will apply predicative analytics to improve business decision making.  The Specialization culminates in a Capstone Project in which you will use sample data to create visualizations, dashboards, and data models to prepare a presentation to the executive leadership of a fictional company.

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Microsoft Power BI Data Analyst Professional Certificate

Microsoft Power BI Data Analyst Professional Certificate

 

What you'll learn

Learn to use Power BI to connect to data sources and transform them into meaningful insights.  

Prepare Excel data for analysis in Power BI using the most common formulas and functions in a worksheet.     

Learn to use the visualization and report capabilities of Power BI to create compelling reports and dashboards.  

Demonstrate your new skills with a capstone project and prepare for the industry-recognized Microsoft PL-300 Certification exam.  

Join Free: Microsoft Power BI Data Analyst Professional Certificate

Professional Certificate - 8 course series

Learners who complete this program will receive a 50% discount voucher to take the PL-300 Certification Exam. 

Business Intelligence analysts are highly sought after as more organizations rely on data-driven decision-making. Microsoft Power BI is the leading data analytics, business intelligence, and reporting tool in the field, used by 97% of Fortune 500 companies to make decisions based on data-driven insights and analytics.1 Prepare for a new career in this high-growth field with professional training from Microsoft — an industry-recognized leader in data analytics and business intelligence.

Through a mix of videos, assessments, and hands-on activities, you will engage with the key concepts of Power BI, transforming data into meaningful insights and creating compelling reports and dashboards. You will learn to prepare data in Excel for analysis in Power BI, form data models using the Star schema, perform calculations in DAX, and more.

In your final project, you will showcase your new Power BI and data analysis skills using a real-world scenario. When you complete this Professional Certificate, you’ll have tangible examples to talk about in your job interviews and you’ll also be prepared to take the industry-recognized PL-300: Microsoft Power BI Data Analyst certification exam.

1Microsoft named a Leader in the 2023 Gartner® Magic Quadrant™ for Analytics and BI Platforms (April 2023)

Applied Learning Project

This program has been uniquely mapped to key job skills required in a Power BI data analyst role. In each course, you’ll be able to consolidate what you have learned by completing a project that simulates a real-world data analysis scenario using Power BI. You’ll also complete a final capstone project where you’ll showcase all your new Power BI data analytical skills.

The projects will include:

● A real-world scenario where you connect to data sources and transform data into an optimized data model for data analysis. 

● A real-world scenario where you demonstrate data storytelling through dashboards, reports and charts to solve business challenges and identify new opportunities.

A real-world capstone project where you analyze the performance of a multinational business and prepare executive dashboards and reports.

To round off your learning, you’ll take a mock exam that has been set up in a similar style to the industry-recognized Exam PL-300: Microsoft Power BI Data Analyst.

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Data Analysis with R Programming

 

What you'll learn

Describe the R programming language and its programming environment.

Explain the fundamental concepts associated with programming in R including functions, variables, data types, pipes, and vectors.

Describe the options for generating visualizations in R.

Demonstrate an understanding of the basic formatting in R Markdown to create structure and emphasize content.

Join Free: Data Analysis with R Programming

There are 5 modules in this course

This course is the seventh course in the Google Data Analytics Certificate. In this course, you’ll learn about the programming language known as R. You’ll find out how to use RStudio, the environment that allows you to work with R, and the software applications and tools that are unique to R, such as R packages. You’ll discover how R lets you clean, organize, analyze, visualize, and report data in new and more powerful ways. Current Google data analysts will continue to instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources.

Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary.

By the end of this course, you will:

- Examine the benefits of using the R programming language.

- Discover how to use RStudio to apply R to your analysis. 

- Explore the fundamental concepts associated with programming in R. 

- Understand the contents and components of R packages including the Tidyverse package.

- Gain an understanding of dataframes and their use in R.

- Discover the options for generating visualizations in R.

- Learn about R Markdown for documenting R programming.

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