Friday 26 April 2024
Practical Time Series Analysis
Python Coding April 26, 2024 Course, Coursera, Data Science No comments
There are 6 modules in this course
Welcome to Practical Time Series Analysis!
Many of us are "accidental" data analysts. We trained in the sciences, business, or engineering and then found ourselves confronted with data for which we have no formal analytic training. This course is designed for people with some technical competencies who would like more than a "cookbook" approach, but who still need to concentrate on the routine sorts of presentation and analysis that deepen the understanding of our professional topics.
In practical Time Series Analysis we look at data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, the price of agricultural products, and more. We look at several mathematical models that might be used to describe the processes which generate these types of data. We also look at graphical representations that provide insights into our data. Finally, we also learn how to make forecasts that say intelligent things about what we might expect in the future.
Please take a few minutes to explore the course site. You will find video lectures with supporting written materials as well as quizzes to help emphasize important points. The language for the course is R, a free implementation of the S language. It is a professional environment and fairly easy to learn.
You can discuss material from the course with your fellow learners. Please take a moment to introduce yourself!
Join Free: Practical Time Series Analysis
Time Series Analysis can take effort to learn- we have tried to present those ideas that are "mission critical" in a way where you understand enough of the math to fell satisfied while also being immediately productive. We hope you enjoy the class!
Thursday 18 April 2024
Meta Data Analyst Professional Certificate
Python Coding April 18, 2024 Data Science No comments
Why Take a Meta Data Analyst Professional Certificate?
Collect, clean, sort, evaluate, and visualize data
Apply the Obtain, Sort, Explore, Model, Interpret (OSEMN) framework to guide the data analysis process
Learn to use statistical analysis, including hypothesis testing, regression analysis, and more, to make data-driven decisions
Develop an understanding of the foundational principles underpinning effective data management and usability of data assets within organizational context
Aquire the confidence to add the following skills to add to your resume:
Data analysis
Python Programming
Statistics
Data management
Data-driven decision making
Data visualization
Linear Regression
Hypothesis testing
Data Management
Tableau
Join Free: Meta Data Analyst Professional Certificate
What you'll learn
Collect, clean, sort, evaluate, and visualize data
Apply the OSEMN, framework to guide the data analysis process, ensuring a comprehensive and structured approach to deriving actionable insights
Use statistical analysis, including hypothesis testing, regression analysis, and more, to make data-driven decisions
Develop an understanding of the foundational principles of effective data management and usability of data assets within organizational context
Professional Certificate - 5 course series
Prepare for a career in the high-growth field of data analytics. In this program, you’ll build in-demand technical skills like Python, Statistics, and SQL in spreadsheets to get job-ready in 5 months or less, no prior experience needed.
Data analysis involves collecting, processing, and analyzing data to extract insights that can inform decision-making and strategy across an organization.
In this program, you’ll learn basic data analysis principles, how data informs decisions, and how to apply the OSEMN framework to approach common analytics questions. You’ll also learn how to use essential tools like SQL, Python, and Tableau to collect, connect, visualize, and analyze relevant data.
You’ll learn how to apply common statistical methods to writing hypotheses through project scenarios to gain practical experience with designing experiments and analyzing results.
When you complete this full program, you’ll have a portfolio of hands-on projects and a Professional Certificate from Meta to showcase your expertise.
Applied Learning Project
Throughout the program, you’ll get to practice your new data analysis skills through hands-on projects including:
Identifying data sources
Using spreadsheets to clean and filter data
Using Python to sort and explore data
Using Tableau to visualize results
Using statistical analyses
By the end, you’ll have a professional portfolio that you can show to prospective employers or utilize for your own business.
Tuesday 16 April 2024
do you know difference between Data Analyst , Data Scientist and Data Engineer?
Python Coding April 16, 2024 Data Science No comments
Data Analyst
A data analyst sits between business intelligence and data science. They provide vital information to business stakeholders.
Data Management in SQL (PostgreSQL)
Data Analysis in SQL (PostgreSQL)
Exploratory Analysis Theory
Statistical Experimentation Theory
Free Certification : Data Analyst Certification
Data Scientist Associate
A data scientist is a professional responsible for collecting, analyzing and interpreting extremely large amounts of data.
R / Python Programming
Data Manipulation in R/Python
1.1 Calculate metrics to effectively report characteristics of data and relationships between
features
● Calculate measures of center (e.g. mean, median, mode) for variables using R or Python.
● Calculate measures of spread (e.g. range, standard deviation, variance) for variables
using R or Python.
● Calculate skewness for variables using R or Python.
● Calculate missingness for variables and explain its influence on reporting characteristics
of data and relationships in R or Python.
● Calculate the correlation between variables using R or Python.
1.2 Create data visualizations in coding language to demonstrate the characteristics of data
● Create and customize bar charts using R or Python.
● Create and customize box plots using R or Python.
● Create and customize line graphs using R or Python.
● Create and customize histograms graph using R or Python.
1.3 Create data visualizations in coding language to represent the relationships between
features
● Create and customize scatterplots using R or Python.
● Create and customize heatmaps using R or Python.
● Create and customize pivot tables using R or Python.
1.4 Identify and reduce the impact of characteristics of data
● Identify when imputation methods should be used and implement them to reduce the
impact of missing data on analysis or modeling using R or Python.
● Describe when a transformation to a variable is required and implement corresponding
transformations using R or Python.
● Describe the differences between types of missingness and identify relevant approaches
to handling types of missingness.
● Identify and handle outliers using R or Python.
Statistical Fundamentals in R/Python
2.1 Perform standard data import, joining and aggregation tasks
● Import data from flat files into R or Python.
● Import data from databases into R or Python
● Aggregate numeric, categorical variables and dates by groups using R or Python.
● Combine multiple tables by rows or columns using R or Python.
● Filter data based on different criteria using R or Python.
2.2 Perform standard cleaning tasks to prepare data for analysis
● Match strings in a dataset with specific patterns using R or Python.
● Convert values between data types in R or Python.
● Clean categorical and text data by manipulating strings in R or Python.
● Clean date and time data in R or Python.
2.3 Assess data quality and perform validation tasks
● Identify and replace missing values using R or Python.
● Perform different types of data validation tasks (e.g. consistency, constraints, range
validation, uniqueness) using R or Python.
● Identify and validate data types in a data set using R or Python.
2.4 Collect data from non-standard formats by modifying existing code
● Adapt provided code to import data from an API using R or Python.
● Identify the structure of HTML and JSON data and parse them into a usable format for
data processing and analysis using R or Python
Importing & Cleaning in R/Python
Machine Learning Fundamentals in R/Python
Free Certification : Data Science
Data Engineer
A data engineer collects, stores, and pre-processes data for easy access and use within an organization. Associate certification is available.
Data Management in SQL (PostgreSQL)
Exploratory Analysis Theory
Free Certification : Data Science
Sunday 14 April 2024
4 Free books to master Data Analytics
Python Coding April 14, 2024 Data Science No comments
Storytelling with Data: A Data Visualization Guide for Business Professionals
Don't simply show your data - tell a story with it!
Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory but made accessible through numerous real-world examples - ready for immediate application to your next graph or presentation.
Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to:
Understand the importance of context and audience
Determine the appropriate type of graph for your situation
Recognize and eliminate the clutter clouding your information
Direct your audience's attention to the most important parts of your data
Think like a designer and utilize concepts of design in data visualization
Leverage the power of storytelling to help your message resonate with your audience
Together, the lessons in this book will help you turn your data into high-impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data - Storytelling with Data will give you the skills and power to tell it!
Fundamentals of Data Analytics: Learn Essential Skills, Embrace the Future, and Catapult Your Career in the Data-Driven World—A Comprehensive Guide to Data Literacy for Beginners
Gain a competitive edge in today’s data-driven world and build a rich career as a data professional that drives business success and innovation…
Today, data is everywhere… and it has become the essential building block of this modern society.
And that’s why now is the perfect time to pursue a career in data.
But what does it take to become a competent data professional?
This book is your ultimate guide to understanding the fundamentals of data analytics, helping you unlock the expertise of efficiently solving real-world data-related problems.
Here is just a fraction of what you will discover:
A beginner-friendly 5-step framework to kickstart your journey into analyzing and processing data
How to get started with the fundamental concepts, theories, and models for accurately analyzing data
Everything you ever needed to know about data mining and machine learning principles
Why business run on a data-driven culture, and how you can leverage it using real-time business intelligence analytics
Strategies and techniques to build a problem-solving mindset that can overcome any complex and unique dataset
How to create compelling and dynamic visualizations that help generate insights and make data-driven decisions
The 4 pillars of a new digital world that will transform the landscape of analyzing data
And much more.
Believe it or not, you can be terrible in math or statistics and still pursue a career in data.
And this book is here to guide you throughout this journey, so that crunching data becomes second nature to you.
Ready to master the fundamentals and build a successful career in data analytics? Click the “Add to Cart” button right now.
PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.
Data Analytics for Absolute Beginners: A Deconstructed Guide to Data Literacy: Python for Data Science, Book 2
Data Analytics, Data Visualization & Communicating Data: 3 books in 1: Learn the Processes of Data Analytics and Data Science, Create Engaging Data Visualizations, and Present Data Effectively
Harvard Business Review called data science “the sexiest job of the 21st century,” so it's no surprise that data science jobs have grown up to 20 times in the last three years. With demand outpacing supply, companies are willing to pay top dollar for talented data professionals. However, to stand out in one of these positions, having foundational knowledge of interpreting data is essential. You can be a spreadsheet guru, but without the ability to turn raw data into valuable insights, the data will render useless. That leads us to data analytics and visualization, the ability to examine data sets, draw meaningful conclusions and trends, and present those findings to the decision-maker effectively.
Mastering this skill will undoubtedly lead to better and faster business decisions. The three audiobooks in this series will cover the foundational knowledge of data analytics, data visualization, and presenting data, so you can master this essential skill in no time. This series includes:
Everything data analytics: a beginner's guide to data literacy and understanding the processes that turns data into insights.
Beginner's guide to data visualization: how to understand, design, and optimize over 40 different charts.
How to win with your data visualizations: the five part guide for junior analysts to create effective data visualizations and engaging data stories.
These three audiobooks cover an extensive amount of information, such as:
Overview of the data collection, management, and storage processes.
Fundamentals of cleaning data.
Essential machine learning algorithms required for analysis such as regression, clustering, classification, and more....
The fundamentals of data visualization.
An in-depth view of over 40 plus charts and when to use them.
A comprehensive data visualization design guide.
Walkthrough on how to present data effectively.
And so much more!
Tuesday 2 April 2024
Doughnut Plot using Python
Python Coding April 02, 2024 Data Science, Python No comments
import plotly.graph_objects as go
# Sample data
labels = ['A', 'B', 'C', 'D']
values = [20, 30, 40, 10]
colors = ['#FFA07A', '#FFD700', '#6495ED', '#ADFF2F']
# Create doughnut plot
fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.5, marker=dict(colors=colors))])
fig.update_traces(textinfo='percent+label', textfont_size=14, hoverinfo='label+percent')
fig.update_layout(title_text="Customized Doughnut Plot", showlegend=False)
# Show plot
fig.show()
#clcoding.com
import matplotlib.pyplot as plt
# Sample data
labels = ['Category A', 'Category B', 'Category C', 'Category D']
sizes = [20, 30, 40, 10]
explode = (0, 0.1, 0, 0) # "explode" the 2nd slice
# Create doughnut plot
fig, ax = plt.subplots()
ax.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', startangle=90, shadow=True, colors=plt.cm.tab20.colors)
ax.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle
# Draw a white circle at the center to create a doughnut plot
centre_circle = plt.Circle((0, 0), 0.7, color='white', fc='white', linewidth=1.25)
fig.gca().add_artist(centre_circle)
# Add a title
plt.title('Doughnut Plot with Exploded Segment and Shadow Effect')
# Show plot
plt.show()
#clcoding.com
import plotly.graph_objects as go
# Sample data
labels = ['A', 'B', 'C', 'D']
values = [20, 30, 40, 10]
# Create doughnut plot
fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.5)])
fig.update_layout(title_text="Doughnut Plot")
# Show plot
fig.show()
#clcoding.com
import matplotlib.pyplot as plt
# Sample data
labels = ['Category A', 'Category B', 'Category C', 'Category D']
sizes = [20, 30, 40, 10]
# Create doughnut plot
fig, ax = plt.subplots()
ax.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90, colors=plt.cm.tab20.colors)
ax.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle
# Draw a white circle at the center to create a doughnut plot
centre_circle = plt.Circle((0, 0), 0.7, color='white', fc='white', linewidth=1.25)
fig.gca().add_artist(centre_circle)
# Add a title
plt.title('Doughnut Plot')
# Show plot
plt.show()
#clcoding.com
Friday 8 March 2024
Fractal Data Science Professional Certificate
Python Coding March 08, 2024 Coursera, Data Science No comments
What you'll learn
Apply structured problem-solving techniques to dissect and address complex data-related challenges encountered in real-world scenarios.
Utilize SQL proficiency to retrieve, manipulate data and employ data visualization skills using Power BI to communicate insights.
Apply Python expertise for data manipulation, analysis and implement machine learning algorithms to create predictive models for applications.
Create compelling data stories to influence your audience and master the art of critically analyzing data while making decisions and recommendations.
Join Free: Fractal Data Science Professional Certificate
Professional Certificate - 8 course series
CertNexus Certified Data Science Practitioner Professional Certificate
Python Coding March 08, 2024 Coursera, Data Science No comments
Advance your career with in-demand skills
Receive professional-level training from CertNexus
Demonstrate your technical proficiency
Earn an employer-recognized certificate from CertNexus
Prepare for an industry certification exam
Join Free: CertNexus Certified Data Science Practitioner Professional Certificate
Professional Certificate - 5 course series
IBM Data Engineering Professional Certificate
Python Coding March 08, 2024 Coursera, Data Science, IBM No comments
What you'll learn
Master the most up-to-date practical skills and knowledge data engineers use in their daily roles
Learn to create, design, & manage relational databases & apply database administration (DBA) concepts to RDBMSs such as MySQL, PostgreSQL, & IBM Db2
Develop working knowledge of NoSQL & Big Data using MongoDB, Cassandra, Cloudant, Hadoop, Apache Spark, Spark SQL, Spark ML, and Spark Streaming
Implement ETL & Data Pipelines with Bash, Airflow & Kafka; architect, populate, deploy Data Warehouses; create BI reports & interactive dashboards
Join Free: IBM Data Engineering Professional Certificate
Professional Certificate - 13 course series
Preparing for Google Cloud Certification: Cloud Data Engineer Professional Certificate
Python Coding March 08, 2024 Books, Data Science, Google No comments
What you'll learn
Identify the purpose and value of the key Big Data and Machine Learning products in Google Cloud.
Employ BigQuery to carry out interactive data analysis.
Use Cloud SQL and Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud.
Choose between different data processing products on Google Cloud.
Join Free: Preparing for Google Cloud Certification: Cloud Data Engineer Professional Certificate
Professional Certificate - 6 course series
Tableau Business Intelligence Analyst Professional Certificate
Python Coding March 08, 2024 Books, Data Science No comments
What you'll learn
Gain the essential skills necessary to excel in an entry-level Business Intelligence Analytics role.
Learn to use Tableau Public to manipulate and prepare data for analysis.
Craft and dissect data visualizations that reveal patterns and drive actionable insights.
Construct captivating narratives through data, enabling stakeholders to explore insights effectively.
Join Free: Tableau Business Intelligence Analyst Professional Certificate
Professional Certificate - 8 course series
Wednesday 6 March 2024
Data Analysis and Visualization Foundations Specialization
Python Coding March 06, 2024 Coursera, Data Science, IBM No comments
What you'll learn
Describe the data ecosystem, tasks a Data Analyst performs, as well as skills and tools required for successful data analysis
Explain basic functionality of spreadsheets and utilize Excel to perform a variety of data analysis tasks like data wrangling and data mining
List various types of charts and plots and create them in Excel as well as work with Cognos Analytics to generate interactive dashboards
Join Free: Data Analysis and Visualization Foundations Specialization
Specialization - 4 course series
IBM AI Foundations for Business Specialization
Python Coding March 06, 2024 AI, Data Science, IBM No comments
Advance your subject-matter expertise
Learn in-demand skills from university and industry experts
Master a subject or tool with hands-on projects
Develop a deep understanding of key concepts
Earn a career certificate from IBM
Join Free: IBM AI Foundations for Business Specialization
Specialization - 3 course series
IBM & Darden Digital Strategy Specialization
Python Coding March 06, 2024 Data Science, IBM No comments
What you'll learn
Understand the value of data and how the rapid growth of technologies such as artificial intelligence and cloud computing are transforming business.
Join Free: IBM & Darden Digital Strategy Specialization
Specialization - 6 course series
Data Science Fundamentals with Python and SQL Specialization
Python Coding March 06, 2024 Data Science, IBM, Python, SQL No comments
What you'll learn
Working knowledge of Data Science Tools such as Jupyter Notebooks, R Studio, GitHub, Watson Studio
Python programming basics including data structures, logic, working with files, invoking APIs, and libraries such as Pandas and Numpy
Statistical Analysis techniques including Descriptive Statistics, Data Visualization, Probability Distribution, Hypothesis Testing and Regression
Relational Database fundamentals including SQL query language, Select statements, sorting & filtering, database functions, accessing multiple tables
Join Free: Data Science Fundamentals with Python and SQL Specialization
Specialization - 5 course series
Introduction to Data Science Specialization
Python Coding March 06, 2024 Books, Coursera, Data Science No comments
What you'll learn
Describe what data science and machine learning are, their applications & use cases, and various types of tasks performed by data scientists
Gain hands-on familiarity with common data science tools including JupyterLab, R Studio, GitHub and Watson Studio
Develop the mindset to work like a data scientist, and follow a methodology to tackle different types of data science problems
Write SQL statements and query Cloud databases using Python from Jupyter notebooks
Join Free: Introduction to Data Science Specialization
Specialization - 4 course series
Tuesday 5 March 2024
Econometric Python: Harnessing Data Science for Economic Analysis: The Science of Pythonomics in 2024
Python Coding March 05, 2024 Books, Data Science, Python No comments
Reactive Publishing
In the rapidly evolving landscape of economics, "Econometric Python" emerges as a groundbreaking guide, perfectly blending the intricate world of econometrics with the dynamic capabilities of Python. This book is crafted for economists, data scientists, researchers, and students who aspire to revolutionize their approach to economic data analysis.
At its center, "Econometric Python" serves as a beacon for those navigating the complexities of econometric models, offering a unique perspective on applying Python's powerful data science tools in economic research. The book starts with a fundamental introduction to Python, focusing on aspects most relevant to econometric analysis. This makes it an invaluable resource for both Python novices and seasoned programmers.
As the narrative unfolds, readers are led through a series of progressively complex econometric techniques, all demonstrated with Python's state-of-the-art libraries such as pandas, NumPy, and statsmodels. Each chapter is meticulously designed to balance theory and practice, providing in-depth explanations of econometric concepts, followed by practical coding examples.
Key features of "Econometric Python" include:
Comprehensive Coverage: From basic economic concepts to advanced econometric models, the book covers a wide array of topics, ensuring a thorough understanding of both theoretical and practical aspects.
Hands-On Approach: With real-world datasets and step-by-step coding tutorials, readers gain hands-on experience in applying econometric theories using Python.
Latest Trends and Techniques: Stay abreast of the latest developments in both econometrics and Python programming, including machine learning applications in economic data analysis.
Expert Insights: The authors, renowned in the fields of economics and data science, provide valuable insights and tips, enhancing the learning experience.
"Econometric Python" is more than just a textbook; it's a journey into the future of economic analysis. By the end of this book, readers will not only be proficient in using Python for econometric analysis but will also be equipped with the skills to contribute innovatively to the field of economics. Whether it's for academic purposes, professional development, or personal interest, this book is an indispensable asset for anyone looking to merge the power of data science with economic analysis.
Hard Copy: Econometric Python: Harnessing Data Science for Economic Analysis: The Science of Pythonomics in 2024
Python Data Science 2024: Explore Data, Build Skills, and Make Data-Driven Decisions in 30 Days (Machine Learning and Data Analysis for Beginners)
Python Coding March 05, 2024 Books, Data Science, Machine Learning, Python No comments
Data Science Crash Course for Beginners with Python...
Uncover the energy of records in 30 days with Python Data Science 2024!
Are you searching for a hands-on strategy to study Python coding and Python for Data Analysis fast?
This beginner-friendly route offers you the abilities and self-belief to discover data, construct sensible abilities, and begin making data-driven selections inside a month.
On the program:
Deep mastering
Neural Networks and Deep Learning
Deep Learning Parameters and Hyper-parameters
Deep Neural Networks Layers
Deep Learning Activation Functions
Convolutional Neural Network
Python Data Structures
Best practices in Python and Zen of Python
Installing Python
Python
These are some of the subjects included in this book:
Fundamentals of deep learning
Fundamentals of probability
Fundamentals of statistics
Fundamentals of linear algebra
Introduction to desktop gaining knowledge of and deep learning
Fundamentals of computer learning
Deep gaining knowledge of parameters and hyper-parameters
Deep neural networks layers
Deep getting to know activation functions
Convolutional neural network
Deep mastering in exercise (in jupyter notebooks)
Python information structures
Best practices in python and zen of Python
Installing Python
At the cease of this course, you may be in a position to:
Confidently deal with real-world datasets.
Wrangle, analyze, and visualize facts the usage of Python.
Turn records into actionable insights and knowledgeable decisions.
Speak the language of data-driven professionals.
Lay the basis for in addition studying in statistics science and computing device learning.
Hard Copy: Python Data Science 2024: Explore Data, Build Skills, and Make Data-Driven Decisions in 30 Days (Machine Learning and Data Analysis for Beginners)
Tuesday 27 February 2024
Python Data Science Handbook: Essential Tools for Working with Data
Python Coding February 27, 2024 Books, Data Science, Python No comments
Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all—IPython, NumPy, pandas, Matplotlib, Scikit-Learn, and other related tools.
Working scientists and data crunchers familiar with reading and writing Python code will find the second edition of this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.
With this handbook, you'll learn how:
IPython and Jupyter provide computational environments for scientists using Python
NumPy includes the ndarray for efficient storage and manipulation of dense data arrays
Pandas contains the DataFrame for efficient storage and manipulation of labeled/columnar data
Matplotlib includes capabilities for a flexible range of data visualizations
Scikit-learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms
Join Free: Python Data Science Handbook: Essential Tools for Working with Data
Foundations of Data Science with Python (Chapman & Hall/CRC The Python Series)
Python Coding February 27, 2024 Books, Data Science, Python No comments
Foundations of Data Science with Python introduces readers to the fundamentals of data science, including data manipulation and visualization, probability, statistics, and dimensionality reduction. This book is targeted toward engineers and scientists, but it should be readily understandable to anyone who knows basic calculus and the essentials of computer programming. It uses a computational-first approach to data science: the reader will learn how to use Python and the associated data-science libraries to visualize, transform, and model data, as well as how to conduct statistical tests using real data sets. Rather than relying on obscure formulas that only apply to very specific statistical tests, this book teaches readers how to perform statistical tests via resampling; this is a simple and general approach to conducting statistical tests using simulations that draw samples from the data being analyzed. The statistical techniques and tools are explained and demonstrated using a diverse collection of data sets to conduct statistical tests related to contemporary topics, from the effects of socioeconomic factors on the spread of the COVID-19 virus to the impact of state laws on firearms mortality.
This book can be used as an undergraduate textbook for an Introduction to Data Science course or to provide a more contemporary approach in courses like Engineering Statistics. However, it is also intended to be accessible to practicing engineers and scientists who need to gain foundational knowledge of data science.
Key Features:
Applies a modern, computational approach to working with data
Uses real data sets to conduct statistical tests that address a diverse set of contemporary issues
Teaches the fundamentals of some of the most important tools in the Python data-science stack
Provides a basic, but rigorous, introduction to Probability and its application to Statistics
Offers an accompanying website that provides a unique set of online, interactive tools to help the reader learn the material
Hard Copy: Foundations of Data Science with Python (Chapman & Hall/CRC The Python Series)
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