Python Programming for Data Analysis (Free PDF)

 

This textbook grew out of notes for the ECE143 Programming for Data Analysis class that the author has been teaching at University of California, San Diego, which is a requirement for both graduate and undergraduate degrees in Machine Learning and Data Science. This book is ideal for readers with some Python programming experience. The book covers key language concepts that must be understood to program effectively, especially for data analysis applications. Certain low-level language features are discussed in detail, especially Python memory management and data structures. Using Python effectively means taking advantage of its vast ecosystem. The book discusses Python package management and how to use third-party modules as well as how to structure your own Python modules.  The section on object-oriented programming explains features of the language that facilitate common programming patterns.

After developing the key Python language features, the book moves on to third-party modules that are foundational for effective data analysis, starting with Numpy. The book develops key Numpy concepts and discusses internal Numpy array data structures and memory usage. Then, the author moves onto Pandas and details its many features for data processing and alignment. Because strong visualizations are important for communicating data analysis, key modules such as Matplotlib are developed in detail, along with web-based options such as Bokeh, Holoviews, Altair, and Plotly.

The text is sprinkled with many tricks-of-the-trade that help avoid common pitfalls. The author explains the internal logic embodied in the Python language so that readers can get into the Python mindset and make better design choices in their codes, which is especially helpful for newcomers to both Python and data analysis. 

To get the most out of this book, open a Python interpreter and type along with the many code samples.

Buy : Python Programming for Data Analysis 

PDF Download : 

Read More

Process Mining: Data science in Action (Free Course)

 

There are 6 modules in this course

Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains.

Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis. Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining. Process mining seeks the confrontation between event data (i.e., observed behavior) and process models (hand-made or discovered automatically). This technology has become available only recently, but it can be applied to any type of operational processes (organizations and systems). Example applications include: analyzing treatment processes in hospitals, improving customer service processes in a multinational, understanding the browsing behavior of customers using booking site, analyzing failures of a baggage handling system, and improving the user interface of an X-ray machine. All of these applications have in common that dynamic behavior needs to be related to process models. Hence, we refer to this as "data science in action".

The course explains the key analysis techniques in process mining. Participants will learn various process discovery algorithms. These can be used to automatically learn process models from raw event data. Various other process analysis techniques that use event data will be presented. Moreover, the course will provide easy-to-use software, real-life data sets, and practical skills to directly apply the theory in a variety of application domains.

This course starts with an overview of approaches and technologies that use event data to support decision making and business process (re)design. Then the course focuses on process mining as a bridge between data mining and business process modeling. The course is at an introductory level with various practical assignments.

The course covers the three main types of process mining.

1. The first type of process mining is discovery. A discovery technique takes an event log and produces a process model without using any a-priori information. An example is the Alpha-algorithm that takes an event log and produces a process model (a Petri net) explaining the behavior recorded in the log.

2. The second type of process mining is conformance. Here, an existing process model is compared with an event log of the same process. Conformance checking can be used to check if reality, as recorded in the log, conforms to the model and vice versa.

3. The third type of process mining is enhancement. Here, the idea is to extend or improve an existing process model using information about the actual process recorded in some event log. Whereas conformance checking measures the alignment between model and reality, this third type of process mining aims at changing or extending the a-priori model. An example is the extension of a process model with performance information, e.g., showing bottlenecks. Process mining techniques can be used in an offline, but also online setting. The latter is known as operational support. An example is the detection of non-conformance at the moment the deviation actually takes place. Another example is time prediction for running cases, i.e., given a partially executed case the remaining processing time is estimated based on historic information of similar cases.

Process mining provides not only a bridge between data mining and business process management; it also helps to address the classical divide between "business" and "IT". Evidence-based business process management based on process mining helps to create a common ground for business process improvement and information systems development.

The course uses many examples using real-life event logs to illustrate the concepts and algorithms. After taking this course, one is able to run process mining projects and have a good understanding of the Business Process Intelligence field.

After taking this course you should:

- have a good understanding of Business Process Intelligence techniques (in particular process mining),

- understand the role of Big Data in today’s society,

- be able to relate process mining techniques to other analysis techniques such as simulation, business intelligence, data mining, machine learning, and verification,

- be able to apply basic process discovery techniques to learn a process model from an event log (both manually and using tools),

- be able to apply basic conformance checking techniques to compare event logs and process models (both manually and using tools),

- be able to extend a process model with information extracted from the event log (e.g., show bottlenecks),

- have a good understanding of the data needed to start a process mining project,

- be able to characterize the questions that can be answered based on such event data,

- explain how process mining can also be used for operational support (prediction and recommendation), and

- be able to conduct process mining projects in a structured manner.

Join Free - Process Mining: Data science in Action

Read More

From Excel to Power BI (Free Course)

 

What you'll learn

Learners will be instructed in how to make use of Excel and Power BI to collect, maintain, share and collaborate, and to make data driven decisions

There is 1 module in this course

Are you using Excel to manage, analyze, and visualize your data? Would you like to do more? Perhaps you've considered Power BI as an alternative, but have been intimidated by the idea of working in an advanced environment. The fact is, many of the same tools and mechanisms exist across both these Microsoft products. This means Excel users are actually uniquely positioned to transition to data modeling and visualization in Power BI! Using methods that will feel familiar, you can learn to use Power BI to make data-driven business decisions using large volumes of data. 

We will help you to build fundamental Power BI knowledge and skills, including: 

• Importing data from Excel and other locations into Power BI.

• Understanding the Power BI environment and its three Views.

• Building beginner-to-moderate level skills for navigating the Power BI product.

• Exploring influential relationships within datasets. 

• Designing Power BI visuals and reports.

• Building effective dashboards for sharing, presenting, and collaborating with peers in Power BI Service.

For this course you will need:

• A basic understanding of data analysis processes in Excel.

• At least a free Power BI licensed account, including:

• The Power BI desktop application.

• Power BI Online in Microsoft 365.

Course duration is approximately three hours. Learning is divided into five modules, the fifth being a cumulative assessment. The curriculum design includes video lessons, interactive learning using short, how-to video tutorials, and practice opportunities using COMPLIMENTARY DATASETS. Intended audiences include business students, small business owners, administrative assistants, accountants, retail managers, estimators, project managers, business analysts, and anyone who is inclined to make data-driven business decisions. Join us for the journey!

Join Free - From Excel to Power BI

Read More

Introduction to Statistics (Free Course)

 

There are 12 modules in this course

Stanford's "Introduction to Statistics" teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By the end of the course, you will be able to perform exploratory data analysis, understand key principles of sampling, and select appropriate tests of significance for multiple contexts. You will gain the foundational skills that prepare you to pursue more advanced topics in statistical thinking and machine learning.

Topics include Descriptive Statistics, Sampling and Randomized Controlled Experiments, Probability, Sampling Distributions and the Central Limit Theorem, Regression, Common Tests of Significance, Resampling, Multiple Comparisons.

Free Course - Introduction to Statistics

Read More

Process Data from Dirty to Clean

 

What you'll learn

Define data integrity with reference to types of integrity and risk to data integrity

Apply basic SQL functions for use in cleaning string variables in a database

Develop basic SQL queries for use on databases

Describe the process involved in verifying the results of cleaning data

There are 6 modules in this course

This is the fourth course in the Google Data Analytics Certificate. These courses will equip you with the skills needed to apply to introductory-level data analyst jobs. In this course, you’ll continue to build your understanding of data analytics and the concepts and tools that data analysts use in their work. You’ll learn how to check and clean your data using spreadsheets and SQL as well as how to verify and report your data cleaning results. 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 be able to do the following:

 - Learn how to check for data integrity.

 - Discover data cleaning techniques using spreadsheets. 

 - Develop basic SQL queries for use on databases.

 - Apply basic SQL functions for cleaning and transforming data.

 - Gain an understanding of how to verify the results of cleaning data.

 - Explore the elements and importance of data cleaning reports.

Read More

Analyze Data to Answer Questions

 

What you'll learn

Discuss the importance of organizing your data before analysis with references to sorts and filters

Demonstrate an understanding of what is involved in the conversion and formatting of data

Apply the use of functions and syntax to create SQL queries for combining data from multiple database tables

Describe the use of functions to conduct basic calculations on data in spreadsheets

There are 4 modules in this course

This is the fifth course in the Google Data Analytics Certificate. These courses will equip you with the skills needed to apply to introductory-level data analyst jobs. In this course, you’ll explore the “analyze” phase of the data analysis process. You’ll take what you’ve learned to this point and apply it to your analysis to make sense of the data you’ve collected. You’ll learn how to organize and format your data using spreadsheets and SQL to help you look at and think about your data in different ways. You’ll also find out how to perform complex calculations on your data to complete business objectives. You’ll learn how to use formulas, functions, and SQL queries as you conduct your analysis. 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:

 - Learn how to organize data for analysis.

 - Discover the processes for formatting and adjusting data. 

 - Gain an understanding of how to aggregate data in spreadsheets and by using SQL.

 - Use formulas and functions in spreadsheets for data calculations.

 - Learn how to complete calculations using SQL queries.

JOIN FREE - Analyze Data to Answer Questions

Read More

Go Beyond the Numbers: Translate Data into Insights

 

What you'll learn

Apply the exploratory data analysis (EDA) process

Explore the benefits of structuring and cleaning data

Investigate raw data using Python

Create data visualizations using Tableau 

There are 5 modules in this course

This is the third of seven courses in the Google Advanced Data Analytics Certificate. In this course, you’ll learn how to find the story within data and tell that story in a compelling way. You'll discover how data professionals use storytelling to better understand their data and communicate key insights to teammates and stakeholders. You'll also practice exploratory data analysis and learn how to create effective data visualizations. 

Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you build your data analytics skills to prepare for your career. 

Learners who complete the seven courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate.  

By the end of this course, you will:

-Use Python tools to examine raw data structure and format

-Select relevant Python libraries to clean raw data

-Demonstrate how to transform categorical data into numerical data with Python

-Utilize input validation skills to validate a dataset with Python

-Identify techniques for creating accessible data visualizations with Tableau

-Determine decisions about missing data and outliers 

-Structure and organize data by manipulating date strings

Join Free- Go Beyond the Numbers: Translate Data into Insights

Read More

Decisions, Decisions: Dashboards and Reports

 

What you'll learn

Design BI visualizations

Practice using BI reporting and dashboard tools

Create presentations to share key BI insights with stakeholders

Develop professional materials for your job search

There are 6 modules in this course

You’re almost there! This is the third and final course in the Google Business Intelligence Certificate. In this course, you’ll apply your understanding of stakeholder needs, plan and create BI visuals, and design reporting tools, including dashboards. You’ll also explore how to answer business questions with flexible and interactive dashboards that can monitor data over long periods of time.

Google employees who currently work in BI will guide you through this course by providing hands-on activities that simulate job tasks, sharing examples from their day-to-day work, and helping you build business intelligence skills to prepare for a career in the field. 

Learners who complete the three courses in this certificate program will have the skills needed to apply for business intelligence jobs. This certificate program assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate.  

By the end of this course, you will:

-Explain how BI visualizations answer business questions

-Identify complications that may arise during the creation of BI visualizations

-Produce charts that represent BI data monitored over time

-Use dashboard and reporting tools

-Build dashboards using best practices to meet stakeholder needs

-Iterate on a dashboard to meet changing project requirements

-Design BI presentations to share insights with stakeholders

-Create or update a resume and prepare for BI interviews

Join Free - Decisions, Decisions: Dashboards and Reports

Read More

Ask Questions to Make Data-Driven Decisions

 

What you'll learn

Explain how each step of the problem-solving road map contributes to common analysis scenarios.

Discuss the use of data in the decision-making process.

Demonstrate the use of spreadsheets to complete basic tasks of the data analyst including entering and organizing data.

Describe the key ideas associated with structured thinking.

There are 4 modules in this course

This is the second course in the Google Data Analytics Certificate. These courses will equip you with the skills needed to apply to introductory-level data analyst jobs. You’ll build on your understanding of the topics that were introduced in the first Google Data Analytics Certificate course. The material will help you learn how to ask effective questions to make data-driven decisions, while connecting with stakeholders’ needs. 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:

- Learn about effective questioning techniques that can help guide analysis. 

- Gain an understanding of data-driven decision-making and how data analysts present findings.

- Explore a variety of real-world business scenarios to support an understanding of questioning and decision-making.

- Discover how and why spreadsheets are an important tool for data analysts.

- Examine the key ideas associated with structured thinking and how they can help analysts better understand problems and develop solutions.

- Learn strategies for managing the expectations of stakeholders while establishing clear communication with a data analytics team to achieve business objectives.

Join Free- Ask Questions to Make Data-Driven Decisions

Read More

Google Data Analytics Capstone: Complete a Case Study

 

What you'll learn

Differentiate between a capstone, case study, and a portfolio

Identify the key features and attributes of a completed case study

Apply the practices and procedures associated with the data analysis process to a given set of data

Discuss the use of case studies/portfolios when communicating with recruiters and potential employers

There are 4 modules in this course

This course is the eighth course in the Google Data Analytics Certificate. You’ll have the opportunity to complete an optional case study, which will help prepare you for the data analytics job hunt. Case studies are commonly used by employers to assess analytical skills. For your case study, you’ll choose an analytics-based scenario. You’ll then ask questions, prepare, process, analyze, visualize and act on the data from the scenario. You’ll also learn other useful job hunt skills through videos with common interview questions and responses, helpful materials to build a portfolio online, and more. 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:

 - Learn the benefits and uses of case studies and portfolios in the job search.

 - Explore real world job interview scenarios and common interview questions.

 - Discover how case studies can be a part of the job interview process. 

 - Examine and consider different case study scenarios. 

 - Have the chance to complete your own case study for your portfolio.

Join FREE - Google Data Analytics Capstone: Complete a Case Study

Read More

Foundations: Data, Data, Everywhere

 

What you'll learn

Define and explain key concepts involved in data analytics including data, data analysis, and data ecosystem

Conduct an analytical thinking self assessment giving specific examples of the application of analytical thinking

Discuss the role of spreadsheets, query languages, and data visualization tools in data analytics

Describe the role of a data analyst with specific reference to jobs/positions

There are 5 modules in this course

This is the first course in the Google Data Analytics Certificate. These courses will equip you with the skills you need to apply to introductory-level data analyst jobs. Organizations of all kinds need data analysts to help them improve their processes, identify opportunities and trends, launch new products, and make thoughtful decisions. In this course, you’ll be introduced to the world of data analytics through hands-on curriculum developed by Google. The material shared covers plenty of key data analytics topics, and it’s designed to give you an overview of what’s to come in the Google Data Analytics Certificate. Current Google data analysts will 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:

- Gain an understanding of the practices and processes used by a junior or associate data analyst in their day-to-day job. 

- Learn about key analytical skills (data cleaning, data analysis, data visualization) and tools (spreadsheets, SQL, R programming, Tableau) that you can add to your professional toolbox. 

- Discover a wide variety of terms and concepts relevant to the role of a junior data analyst, such as the data life cycle and the data analysis process. 

- Evaluate the role of analytics in the data ecosystem. 

- Conduct an analytical thinking self-assessment. 

- Explore job opportunities available to you upon program completion, and learn about best practices in the job search.

Join Free - Foundations: Data, Data, Everywhere

Read More

Google Business Intelligence Professional Certificate

 

What you'll learn

Explore the roles of business intelligence (BI) professionals within an organization

Practice data modeling and extract, transform, load (ETL) processes that meet organizational goals 

Design data visualizations that answer business questions

Create dashboards that effectively communicate data insights to stakeholders

Professional Certificate - 3 course series

Get professional training designed by Google and take the next step in your career with advanced skills in the high-growth field of business intelligence. There are over 166,000 open jobs in business intelligence and the median salary for entry-level roles is $96,000.¹

Business intelligence professionals collect, organize, interpret, and report on data to help organizations make informed business decisions. Some responsibilities include measuring performance, tracking revenue or spending, and monitoring progress.

This certificate builds on your data analytics skills and experience to take your career to the next level. It's designed for graduates of the 

Google Data Analytics Certificate

 or people with equivalent data analytics experience. Expand your knowledge with practical, hands-on projects, featuring BigQuery, SQL, and Tableau.

After three courses, you’ll be prepared for jobs like business intelligence analyst, business intelligence engineer, business intelligence developer, and more. At under 10 hours a week, the certificate program can be completed in less than two months. Upon completion, you can apply for jobs with Google and over 150 U.S. employers, including Deloitte, Target, and Verizon.

75% of certificate graduates report a positive career outcome (e.g., new job, promotion or raise) within six months of completion2

¹Lightcast™ US Job Postings (Last 12 Months: 1/1/2022 – 12/31/2022) 

2Based on program graduate survey responses, US 2022

Applied Learning Project

This program includes over 70 hours of instruction and 50+ practice-based assessments, which will help you simulate real-world business intelligence scenarios that are critical for success in the workplace. The content is highly interactive and exclusively developed by Google employees with decades of experience in business intelligence. Through a mix of videos, assessments, and hands-on labs, you’ll get introduced to BI tools and platforms and key technical skills required for an entry-level job.

Platforms and tools you will learn include: BigQuery, SQL, Tableau

In addition to expert training and hands-on projects, you'll complete a portfolio project that you can share with potential employers to showcase your new skill set. Learn concrete skills that top employers are hiring for right now.

Join free - Google Business Intelligence Professional Certificate

Read More

Foundations of Data Science

 

What you'll learn

Understand common careers and industries that use advanced data analytics

Investigate the impact data analysis can have on decision-making

Explain how data professionals preserve data privacy and ethics 

Develop a project plan considering roles and responsibilities of team members

There are 5 modules in this course

This is the first of seven courses in the Google Advanced Data Analytics Certificate, which will help develop the skills needed to apply for more advanced data professional roles, such as an entry-level data scientist or advanced-level data analyst. Data professionals analyze data to help businesses make better decisions. To do this, they use powerful techniques like data storytelling, statistics, and machine learning. In this course, you’ll begin your learning journey by exploring the role of data professionals in the workplace. You’ll also learn about the project workflow PACE (Plan, Analyze, Construct, Execute) and how it can help you organize data projects.   

Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career. 

Learners who complete the seven courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate.  

By the end of this course, you will:

-Describe the functions of data analytics and data science within an organization

-Identify tools used by data professionals 

-Explore the value of data-based roles in organizations 

-Investigate career opportunities for a data professional 

-Explain a data project workflow 

-Develop effective communication skills

Join free - Foundations of Data Science

Read More

Python Project for Data Science

 

What you'll learn

Play the role of a Data Scientist / Data Analyst working on a real project.

Demonstrate your Skills in Python - the language of choice for Data Science and Data Analysis. 

Apply Python fundamentals, Python data structures, and working with data in Python.

Build a dashboard using Python and libraries like Pandas, Beautiful Soup and Plotly using Jupyter notebook.

There is 1 module in this course

This mini-course is intended to for you to demonstrate foundational Python skills for working with data. This course primarily involves completing a project in which you will assume the role of a Data Scientist or a Data Analyst and be provided with a real-world data set and a real-world inspired scenario to identify patterns and trends. 

You will perform specific data science and data analytics tasks such as extracting data, web scraping, visualizing data and creating a dashboard. This project will showcase your proficiency with Python and using libraries such as Pandas and Beautiful Soup within a Jupyter Notebook. Upon completion you will have an impressive project to add to your job portfolio.   

PRE-REQUISITE: **Python for Data Science, AI and Development** course from IBM is a pre-requisite for this project course. Please ensure that before taking this course you have either completed the Python for Data Science, AI and Development course from IBM or have equivalent proficiency in working with Python and data.  

NOTE: This course is not intended to teach you Python and does not have too much instructional content. It is intended for you to apply prior Python knowledge.

Join - Python Project for Data Science

Read More

IBM: SQL for Data Science (Free Course)

 

Learn how to use and apply the powerful language of SQL to better communicate and extract data from databases - a must for anyone working in the data science field.

About this course

Please Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!

Much of the world's data lives in databases. SQL (or Structured Query Language) is a powerful programming language that is used for communicating with and extracting various data types from databases. A working knowledge of databases and SQL is necessary to advance as a data scientist or a machine learning specialist. The purpose of this course is to introduce relational database concepts and help you learn and apply foundational knowledge of the SQL language. It is also intended to get you started with performing SQL access in a data science environment.

The emphasis in this course is on hands-on, practical learning. As such, you will work with real databases, real data science tools, and real-world datasets. You will create a database instance in the cloud. Through a series of hands-on labs, you will practice building and running SQL queries. You will also learn how to access databases from Jupyter notebooks using SQL and Python.

No prior knowledge of databases, SQL, Python, or programming is required.

What you'll learn

Learn and apply foundational knowledge of the SQL language

How to create a database in the cloud

How to use string patterns and ranges to query data

How to sort and group data in result sets and by data type

How to analyze data using Python

JOIN Free - IBM: SQL for Data Science

Read More

IBM Data Analyst Professional Certificate

 Prepare for a career as a data analyst. Gain the in-demand skills and hands-on experience to get job-ready in as little as 4 months. No prior experience required.

What you'll learn

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

Learn how to visualize data and present findings using various charts in Excel spreadsheets and BI tools like IBM Cognos Analytics & Tableau

Develop working knowledge of Python language for analyzing data using Python libraries like Pandas and Numpy, and invoke APIs and Web Services 

Gain technical experience through hands on labs and projects and build a portfolio to showcase your work

Prepare for a career in Data Analytics

Receive professional-level training from IBM

Demonstrate your proficiency in portfolio-ready projects

Earn an employer-recognized certificate from IBM

Qualify for in-demand job titles: Data Analyst, Associate Data Analyst, Business Analyst

JOIN - IBM Data Analyst Professional Certificate

Read More

Data Science Challenge (Free Course)

Data Science Challenge 

Duration - Less than 2 hours

Cost - Free

This project requires you to independently complete the following steps:

1.  Importing and preprocessing data

2. Analyze the data

3. Build machine learning models

4. Evaluate machine learning models

Join now - Data Science Challenge (Free Course)

Read More

IBM: Python Basics for Data Science (Free Course)

 

This Python course provides a beginner-friendly introduction to Python for Data Science. Practice through lab exercises, and you'll be ready to create your first Python scripts on your own!

About this course

Please Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!

What you'll learn

The objectives of this course is to get you started with Python as the programming language and give you a taste of how to start working with data in Python.

In this course you will learn about:

• What Python is and why it is useful
• The application of Python to Data Science
• How to define variables in Python
• Sets and conditional statements in Python
• The purpose of having functions in Python
• How to operate on files to read and write data in Python
• How to use pandas, a must have package for anyone attempting data analysis in Python.

JOIN - IBM: Python Basics for Data Science

Read More

Foundations of Data Science: K-Means Clustering in Python (Free Course)

 

What you'll learn

Define and explain the key concepts of data clustering    

Demonstrate understanding of the key constructs and features of the Python language.    

Implement in Python the principle steps of the K-means algorithm.    

Design and execute a whole data clustering workflow and interpret the outputs.    

Free Join - Foundations of Data Science: K-Means Clustering in Python

Read More