Data Literacy and Management   

By now you have definitely heard about data science and big data. In this one-week class, we will provide a crash course in what these terms mean and how they play a role in successful organizations. This class is for anyone who wants to learn what all the data science action is about, including those who will eventually need to manage data scientists. The goal is to get you up to speed as quickly as possible on data science without all the fluff. We've designed this course to be as convenient as possible without sacrificing any of the essentials.

This is a focused course designed to rapidly get you up to speed on the field of data science. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward.

After completing this course you will know.

1. How to describe the role data science plays in various contexts
2. How statistics, machine learning, and software engineering play a role in data science
3. How to describe the structure of a data science project
4. Know the key terms and tools used by data scientists
5. How to identify a successful and an unsuccessful data science project
3. The role of a data science manager


Course cover image by r2hox. Creative Commons BY-SA: https://flic.kr/p/gdMuhT

This course will provide you with a basic, intuitive and practical introduction into Probability Theory. You will be able to learn how to apply Probability Theory in different scenarios and you will earn a "toolbox" of methods to deal with uncertainty in your daily life.

The course is split in 5 modules. In each module you will first have an easy introduction into the topic, which will serve as a basis to further develop your knowledge about the topic and acquire the "tools" to deal with uncertainty. Additionally, you will have the opportunity to complete 5 exercise sessions to reflect about the content learned in each module and start applying your earned knowledge right away.

The topics covered are: "Probability", "Conditional Probability", "Applications", "Random Variables", and "Normal Distribution".

You will see how the modules are taught in a lively way, focusing on having an entertaining and useful learning experience! We are looking forward to see you online!

In this course you will interpret the components of a relational data model, convert that model into a relational database, and then test the database design. The process of database design begins with requirements analysis to determine who will use the new database and how it will be used. The results of the detailed analysis are recorded in an Entity Relationship Diagram (ERD), which documents entities and their attributes, along with the relationships between entities. The ERD (logical design) is then converted into the Relational Model, which serves as the blueprint for the actual creation of a database in a database management system. By the end of this course, you will have used a blueprint—a Relational Model—to create a database using SQLiteStudio. In addition, you will have developed test data and queries to validate the database design represented by the Relational Model.

Note: 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.

This is the fourth course in the Data Warehouse for Business Intelligence specialization. Ideally, the courses should be taken in sequence. Effectively and efficiently mining data is the very center of any modern business’s competitive strategy, and a data warehouse is a core component of this data mining. The ability to quickly look back at early trends and have the accurate data – properly formatted – is essential to good decision making. By enabling this historical overview, a data warehouse allows decision makers to learn from past trends and challenges. In essence, the benefit of a data warehouse is continuous improvement.

By the end of the course, you will be able to enhance Conformity And Quality of Data by gaining the knowledge and skills for using data warehouses for business intelligence purposes and for working as a business intelligence developer. You’ll have the opportunity to work with large data sets in a data warehouse environment and will learn the use of MicroStrategy's Online Analytical Processing (OLAP) and Visualization capabilities to create visualizations and dashboards.

The course gives an overview of how business intelligence technologies can support decision making across any number of business sectors. These technologies have had a profound impact on corporate strategy, performance, and competitiveness and broadly encompass decision support systems, business intelligence systems, and visual analytics. Modules are organized around the business intelligence concepts, tools, and applications, and the use of data warehouse for business reporting and online analytical processing, for creating visualizations and dashboards, and for business performance management and descriptive analytics.

This course is intended for business and computer science university students, IT professionals, program managers, business analysts and anyone with career interests in business intelligence.

In order to be successful in this course, you should have either completed Course 3 of the Data Warehousing for Business Intelligence Specialization or have some prior experience with data visualization and document management.

In the fourth course of the Content Strategy Specialization - Ensuring Your Content's Impact - you will look at visual communication and the ways you can be more effective with your font choices, photography, and video. You'll also dive deeper into social communities to help you understand how these communities form and what you can do to build your role within them. The last module is pivotal for Content Strategists. It will help you to understand how best to measure your content to maximize its effectiveness relative to the time you commit to it.

While this MOOC does share the theoretical elements of Content Strategy, there is a much greater emphasis on its application. Creating trend-worthy headlines and blogs, social media plans, digital measurement templates, video and photography content are all skills you will have in your toolkit by the end of the MOOC, ready to apply at your organization. And speaking of toolkits, we have included one that you can download and take back to work which includes the practical tips from the learnings in this course as well as the previous one on Expanding Your Content's Reach.

Guest lecturers in this course include:
-- Zach Wise, Associate Professor, Medill Northwestern
-- Rich Gordon, Professor & Director of Digital Innovation, Medill, Northwestern
-- Randy Hlavac, Lecturer, Medill, Northwestern (and lead professor of the Social Media Marketing Specialization also on Coursera)

Want your content to go viral? Who doesn't! It takes a thoughtful, integrated approach to make content that stands out in our increasingly oversaturated world. In this fourth course of the Social Marketing Specialization - "Content, Advertising & Social IMC" - you will learn how marketers are successfully navigating today's media landscape. You will learn why developing engaging content for your audience is an essential component in effective social marketing. A panel of experts will unlock the paid/owned/earned media riddle and replace it with an integrated who/what/where approach that utilizes platform-specific messaging to grow your market share. This course also includes an overview of the integrated marketing communications strategy for social and how it is being deployed around the globe, as well as gamification tips to keep your audiences coming back for more. In addition, you will learn the secrets to advertising on Facebook and other social sites.

Additional MOOC 4 faculty include:
* Judy Ungar Franks (President, The Marketing Democracy, Ltd. & Lecturer, Medill Integrated Marketing Communications, Northwestern)
* Steffi Decker (Junior Partner, Chong and Koster)
* Joey Strawn (Director of Integrated Marketing, Industrial Strength Marketing)

Tableau is widely recognized as one of the premier data visualization software programs. For many years access to the program was limited to those who purchased licenses. Recently, Tableau launched a public version that grants the ability to create amazing data visualizations for free. Account members can also share and join projects to collaborate on projects that can change the world.

In this project, we will learn how to create an account, create an Interactive Graph in Tableau and share it with others.

Learning to use this in-demand tool has applications in Marketing, Finance, Operations, Sales, and many other business functions.

Note: 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.

This course introduces students to data and statistics. By the end of the course, students should be able to interpret descriptive statistics, causal analyses and visualizations to draw meaningful insights.

The course first introduces a framework for thinking about the various purposes of statistical analysis. We’ll talk about how analysts use data for descriptive, causal and predictive inference. We’ll then cover how to develop a research study for causal analysis, compute and interpret descriptive statistics and design effective visualizations. The course will help you to become a thoughtful and critical consumer of analytics.

If you are in a field that increasingly relies on data-driven decision making, but you feel unequipped to interpret and evaluate data, this course will help you develop these fundamental tools of data literacy.

This course teaches you to fetch and process data from services on the Internet. It covers Python list comprehensions and provides opportunities to practice extracting from and processing deeply nested data. You'll also learn how to use the Python requests module to interact with REST APIs and what to look for in documentation of those APIs. For the final project, you will construct a “tag recommender” for the flickr photo sharing site.

The course is well-suited for you if you have already taken the "Python Basics" and "Python Functions, Files, and Dictionaries" courses (courses 1 and 2 of the Python 3 Programming Specialization). If you are already familiar with Python fundamentals but want practice at retrieving and processing complex nested data from Internet services, you can also benefit from this course without taking the previous two.

This is the third of five courses in the Python 3 Programming Specialization.

The Data Science for Business Innovation nano-course is a compendium of the must-have expertise in data science for executives and middle-management to foster data-driven innovation. The course explains what Data Science is and why it is so hyped.

You will learn:
* the value that Data Science can create
* the main classes of problems that Data Science can solve
* the difference is between descriptive, predictive, and prescriptive analytics
* the roles of machine learning and artificial intelligence.

From a more technical perspective, the course covers supervised, unsupervised and semi-supervised methods, and explains what can be obtained with classification, clustering, and regression techniques. It discusses the role of NoSQL data models and technologies, and the role and impact of scalable cloud-based computation platforms. All topics are covered with example-based lectures, discussing use cases, success stories, and realistic examples.

Following this nano-course, if you wish to further deepen your data science knowledge, you can attend the Data Science for Business Innovation live course https://professionalschool.eitdigital.eu/data-science-for-business-innovation

Have you ever had the perfect data science experience? The data pull went perfectly. There were no merging errors or missing data. Hypotheses were clearly defined prior to analyses. Randomization was performed for the treatment of interest. The analytic plan was outlined prior to analysis and followed exactly. The conclusions were clear and actionable decisions were obvious. Has that every happened to you? Of course not. Data analysis in real life is messy. How does one manage a team facing real data analyses? In this one-week course, we contrast the ideal with what happens in real life. By contrasting the ideal, you will learn key concepts that will help you manage real life analyses.

This is a focused course designed to rapidly get you up to speed on doing data science in real life. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward.

After completing this course you will know how to:

1, Describe the “perfect” data science experience
2. Identify strengths and weaknesses in experimental designs
3. Describe possible pitfalls when pulling / assembling data and learn solutions for managing data pulls.
4. Challenge statistical modeling assumptions and drive feedback to data analysts
5. Describe common pitfalls in communicating data analyses
6. Get a glimpse into a day in the life of a data analysis manager.

The course will be taught at a conceptual level for active managers of data scientists and statisticians. Some key concepts being discussed include:
1. Experimental design, randomization, A/B testing
2. Causal inference, counterfactuals,
3. Strategies for managing data quality.
4. Bias and confounding
5. Contrasting machine learning versus classical statistical inference

Course promo:
https://www.youtube.com/watch?v=9BIYmw5wnBI

Course cover image by Jonathan Gross. Creative Commons BY-ND https://flic.kr/p/q1vudb

Despite the recent increase in computing power and access to data over the last couple of decades, our ability to use the data within the decision making process is either lost or not maximized at all too often, we don't have a solid understanding of the questions being asked and how to apply the data correctly to the problem at hand.

This course has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem solving is relevant and properly manipulated to address the question at hand.

Accordingly, in this course, you will learn:
- The major steps involved in tackling a data science problem.
- The major steps involved in practicing data science, from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment.
- How data scientists think!

"A picture is worth a thousand words". We are all familiar with this expression. It especially applies when trying to explain the insight obtained from the analysis of increasingly large datasets. Data visualization plays an essential role in the representation of both small and large-scale data.

One of the key skills of a data scientist is the ability to tell a compelling story, visualizing data and findings in an approachable and stimulating way. Learning how to leverage a software tool to visualize data will also enable you to extract information, better understand the data, and make more effective decisions.

The main goal of this Data Visualization with Python course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people. Various techniques have been developed for presenting data visually but in this course, we will be using several data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium.

LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.

In this project-based course, you will follow your own interests to create a portfolio worthy single-frame viz or multi-frame data story that will be shared on Tableau Public. You will use all the skills taught in this Specialization to complete this project step-by-step, with guidance from your instructors along the way. You will first create a project proposal to identify your goals for the project, including the question you wish to answer or explore with data. You will then find data that will provide the information you are seeking. You will then import that data into Tableau and prepare it for analysis. Next you will create a dashboard that will allow you to explore the data in depth and identify meaningful insights. You will then give structure to your data story by writing the story arc in narrative form. Finally, you will consult your design checklist to craft the final viz or data story in Tableau. This is your opportunity to show the world what you’re capable of - so think big, and have confidence in your skills!

The capstone course, Design and Build a Data Warehouse for Business Intelligence Implementation, features a real-world case study that integrates your learning across all courses in the specialization. In response to business requirements presented in a case study, you’ll design and build a small data warehouse, create data integration workflows to refresh the warehouse, write SQL statements to support analytical and summary query requirements, and use the MicroStrategy business intelligence platform to create dashboards and visualizations.

In the first part of the capstone course, you’ll be introduced to a medium-sized firm, learning about their data warehouse and business intelligence requirements and existing data sources. You’ll first architect a warehouse schema and dimensional model for a small data warehouse. You’ll then create data integration workflows using Pentaho Data Integration to refresh your data warehouse. Next, you’ll write SQL statements for analytical query requirements and create materialized views to support summary data management. For data integration workflows and analytical queries, you can use either Oracle or PostgreSQL. Finally, you will use MicroStrategy OLAP capabilities to gain insights into your data warehouse. In the completed project, you’ll have built a small data warehouse containing a schema design, data integration workflows, analytical queries, materialized views, dashboards and visualizations that you’ll be proud to show to your current and prospective employers.

In this project-centered course*, you will create a content-rich infographic on a topic of your choice using Adobe Illustrator (which you can download for a free, 30-day trial). You might choose to create a visual representation of data from the world of sports, entertainment, politics, or science, to explain a business trend or environmental issue, or even to present a theme or development from your personal life. Your finished infographic will engage your target audience and convey information clearly through effective use of design elements such as typography, color, and structure.

Whether you’re a graphic designer, a writer or the intern in the department, you’ll learn:
• what an infographic is and what makes a good one
• how to work within your limits
• how to work with a team (if you have one)
• why infographics are effective
• techniques for spotting data in stories
• six valuable steps for planning an effective infographic
• how to use and make some of the building blocks of infographics: maps, charts and flow charts
• ways data can be visualized to clarify it and give it meaning
• how to effectively design a good infographic by effectively using elements like type, color and an underlying grid structure
• some free or cheap, online tools for making various kinds of infographics

As you work on your project, you’ll learn more about why infographics are effective, what makes a good infographic, and how to plan and design an infographic for maximum impact. You’ll explore various approaches to data visualization, and you’ll practice creating visualizations like maps, charts, flow charts, and simple drawings in your free version of Adobe Illustrator. Please note that if you are new to learning graphics software, making these graphics could take much longer than estimated as you learn and grow.

What you’ll need to get started:
This project-based course is aimed at anyone interested in understanding, designing, and using infographics - from students and hobbyists to professional graphic designers.

We’ll use Adobe Illustrator for some components of the project. If you don’t have access to the full version of Illustrator,you can download a free version at www.Adobe.com/Illustrator. If the free 30-day trial runs out, you can "purchase" it for a month for about $20.

*About Project-Centered Courses: Project centered courses are designed specifically to help you complete a personally meaningful real-world project, with your instructor and a community of like-minded learners providing guidance and suggestions along the way. By actively applying new concepts as you learn, you’ll master the course content more efficiently; you’ll also get a head start on using the skills you gain to make positive changes in your life and career. When you complete the course, you’ll have a finished project that you’ll be proud to use and share. When you enroll in certain courses, you’ll be asked to pay a small fee to share your work with others for peer review.

This first course in the IBM Machine Learning Professional Certificate introduces you to Machine Learning and the content of the professional certificate. In this course you will realize the importance of good, quality data. You will learn common techniques to retrieve your data, clean it, apply feature engineering, and have it ready for preliminary analysis and hypothesis testing.


By the end of this course you should be able to:
Retrieve data from multiple data sources: SQL, NoSQL databases, APIs, Cloud 
Describe and use common feature selection and feature engineering techniques
Handle categorical and ordinal features, as well as missing values
Use a variety of techniques for detecting and dealing with outliers
Articulate why feature scaling is important and use a variety of scaling techniques
 
Who should take this course?
This course targets aspiring data scientists interested in acquiring hands-on experience  with Machine Learning and Artificial Intelligence in a business setting.
 
What skills should you have?
To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Calculus, Linear Algebra, Probability, and Statistics.

In this 2-hour long project-based course, you will learn how to perform Exploratory Data Analysis (EDA) in Python. You will use external Python packages such as Pandas, Numpy, Matplotlib, Seaborn etc. to conduct univariate analysis, bivariate analysis, correlation analysis and identify and handle duplicate/missing data.

Note: 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.

Producing visualizations is an important first step in exploring and analyzing real-world data sets. As such, visualization is an indispensable method in any data scientist's toolbox. It is also a powerful tool to identify problems in analyses and for illustrating results.In this project-based course, we will employ the statistical data visualization library, Seaborn, to discover and explore the relationships in the Breast Cancer Wisconsin (Diagnostic) Data Set. We will cover key concepts in exploratory data analysis (EDA) using visualizations to identify and interpret inherent relationships in the data set, produce various chart types including histograms, violin plots, box plots, joint plots, pair grids, and heatmaps, customize plot aesthetics and apply faceting methods to visualize higher dimensional data.

This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. 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 Python, Jupyter, and scikit-learn 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.

In this course, we will expand our exploration of statistical inference techniques by focusing on the science and art of fitting statistical models to data. We will build on the concepts presented in the Statistical Inference course (Course 2) to emphasize the importance of connecting research questions to our data analysis methods. We will also focus on various modeling objectives, including making inference about relationships between variables and generating predictions for future observations.

This course will introduce and explore various statistical modeling techniques, including linear regression, logistic regression, generalized linear models, hierarchical and mixed effects (or multilevel) models, and Bayesian inference techniques. All techniques will be illustrated using a variety of real data sets, and the course will emphasize different modeling approaches for different types of data sets, depending on the study design underlying the data (referring back to Course 1, Understanding and Visualizing Data with Python).

During these lab-based sessions, learners will work through tutorials focusing on specific case studies to help solidify the week’s statistical concepts, which will include further deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course utilizes the Jupyter Notebook environment within Coursera.

Welcome to Fundamentals of Big Data, the fourth course of the Key Technologies of Data Analytics specialization. By enrolling in this course, you are taking the next step in your career in data analytics. This course is the fourth of a series that aims to prepare you for a role working in data analytics. In this course, you will be introduced to many of the core concepts of big data. You will learn about the primary systems used in big data. We’ll go through phases of a common big data life cycle. This course covers a wide variety of topics that are critical for understanding big data and are designed to give you an introduction and overview as you begin to build relevant knowledge and skills.

In this 2-hour long project-based course, you will learn the basics of using Power BI Desktop software. We will do this by analyzing data on credit card defaults with Power BI Desktop. Power BI Desktop is a free Business Intelligence application from Microsoft that lets you load, transform, and visualize data. You can create interactive reports and dashboards quite easily, and quickly. We will learn some of the basics of Power BI by importing, transforming, and visualizing the data.

This course is aimed at learners who are looking to get started with the Power BI Desktop software. There are no hard prerequisites and any competent computer user should be able to complete the project successfully.

Note: 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.

This course aims to help you to draw better statistical inferences from empirical research. First, we will discuss how to correctly interpret p-values, effect sizes, confidence intervals, Bayes Factors, and likelihood ratios, and how these statistics answer different questions you might be interested in. Then, you will learn how to design experiments where the false positive rate is controlled, and how to decide upon the sample size for your study, for example in order to achieve high statistical power. Subsequently, you will learn how to interpret evidence in the scientific literature given widespread publication bias, for example by learning about p-curve analysis. Finally, we will talk about how to do philosophy of science, theory construction, and cumulative science, including how to perform replication studies, why and how to pre-register your experiment, and how to share your results following Open Science principles.

In practical, hands on assignments, you will learn how to simulate t-tests to learn which p-values you can expect, calculate likelihood ratio's and get an introduction the binomial Bayesian statistics, and learn about the positive predictive value which expresses the probability published research findings are true. We will experience the problems with optional stopping and learn how to prevent these problems by using sequential analyses. You will calculate effect sizes, see how confidence intervals work through simulations, and practice doing a-priori power analyses. Finally, you will learn how to examine whether the null hypothesis is true using equivalence testing and Bayesian statistics, and how to pre-register a study, and share your data on the Open Science Framework.

All videos now have Chinese subtitles. More than 30.000 learners have enrolled so far!

If you enjoyed this course, I can recommend following it up with me new course "Improving Your Statistical Questions"

The courses in the Introduction to Project Management Principles and Practices Specialization are a recommended precursor to UCI's Applied Project Management Certificate.

Successful projects require careful upfront planning. In this course, you’ll learn the key roles and responsibilities of the project manager and project team. You’ll also learn to answer some key questions upfront to help you meet project objectives: What will this project accomplish? Why is this project important? Who benefits from this project? How will we plan for successful outcomes?

Upon completing this course, you will be able to:
1. Identify the key characteristics of a project
2. Identify primary project constraints
3. Define the role and responsibilities of the project manager
4. Identify Project Organizational Structures
5. Understand the definition of a Project Stakeholder
6. Identify project stakeholders
7. Identify information needs of the project stakeholders
8. Define responsibility for managing stakeholder and controlling stakeholder engagement
9. Define the purpose of using a project charter
10. Summarize the key elements of a project plan
11. Identify common sources of conflict within a project environment
12. Describe the difference between authority and influence

Welcome to Introduction to Analytic Thinking, Data Science, and Data Mining. In this course, we will begin with an exploration of the field and profession of data science with a focus on the skills and ethical considerations required when working with data. We will review the types of business problems data science can solve and discuss the application of the CRISP-DM process to data mining efforts. A brief overview of Descriptive, Predictive, and Prescriptive Analytics will be provided, and we will conclude the course with an exploratory activity to learn more about the tools and resources you might find in a data science toolkit.

This course is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this course will introduce you to problem definition and data preparation in a machine learning project.

By the end of the course, you will be able to clearly define a machine learning problem using two approaches. You will learn to survey available data resources and identify potential ML applications. You will learn to take a business need and turn it into a machine learning application. You will prepare data for effective machine learning applications.

This is the first course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.

This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction. In addition, we have designed practice exercises that will give you hands-on experience implementing these data science models on data sets. These practice exercises will teach you how to implement machine learning algorithms with PyTorch, open source libraries used by leading tech companies in the machine learning field (e.g., Google, NVIDIA, CocaCola, eBay, Snapchat, Uber and many more).

Learning Python gives the programmer a wide variety of career paths to choose from. Python is an open-source (free) programming language that is used in web programming, data science, artificial intelligence, and many scientific applications. Learning Python allows the programmer to focus on solving problems, rather than focusing on syntax. Its relative size and simplified syntax give it an edge over languages like Java and C++, yet the abundance of libraries gives it the power needed to accomplish great things.

In this tutorial you will create a guessing game application that pits the computer against the user. You will create variables, decision constructs, and loops in python to create the game.

Note: 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.

In this guided project, you will get hands-on experience working with a relational database using MySQL Workbench from Oracle. The basic knowledge you learn will allow you to work with any other relational database.

At the end of this project, you will be able to create a billing report and a club member roster.

Welcome to this project-based course on Linear Regression with NumPy and Python. In this project, you will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn and statsmodels. The aim of this project and is to implement all the machinery, including gradient descent and linear regression, of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals.

This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. 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 Python, Jupyter, NumPy, and Seaborn pre-installed.

In this 2-hour long project-based course, you will learn how to implement Linear Regression using Python and Numpy. Linear Regression is an important, fundamental concept if you want break into Machine Learning and Deep Learning. Even though popular machine learning frameworks have implementations of linear regression available, it's still a great idea to learn to implement it on your own to understand the mechanics of optimization algorithm, and the training process.

Since this is a practical, project-based course, you will need to have a theoretical understanding of linear regression, and gradient descent. We will focus on the practical aspect of implementing linear regression with gradient descent, but not on the theoretical aspect.

Note: 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.

This one-week course describes the process of analyzing data and how to manage that process. We describe the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling, interpretation, and communication. In addition, we will describe how to direct analytic activities within a team and to drive the data analysis process towards coherent and useful results.

This is a focused course designed to rapidly get you up to speed on the process of data analysis and how it can be managed. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward.

After completing this course you will know how to….

1. Describe the basic data analysis iteration
2. Identify different types of questions and translate them to specific datasets
3. Describe different types of data pulls
4. Explore datasets to determine if data are appropriate for a given question
5. Direct model building efforts in common data analyses
6. Interpret the results from common data analyses
7. Integrate statistical findings to form coherent data analysis presentations

Commitment: 1 week of study, 4-6 hours

Course cover image by fdecomite. Creative Commons BY https://flic.kr/p/4HjmvD

This course will help you manage project risk effectively by identifying, analyzing, and communicating inevitable changes to project scope and objectives. You will understand and practice the elements needed to measure and report on project scope, schedule, and cost performance. You will be equipped with the tools to manage change in the least disruptive way possible for your team and other project stakeholders.

Upon completing this course, you will be able to:
1. Define components of a communications management plan
2. Understand the importance of communications channels
3. Define the key elements needed to measure and report on project scope, schedule, and cost performance
4. Identify project risk events
5. Prioritize identified risks
6. Develop responses for a high priority risk
7. Identify and analyze changes to project scope
8. Describe causes and effects of project changes
9. Define the purpose of conducting a lessons learned session

This course provides a framework for how analysts can create and evaluate quantitative measures. Consider the many tricky concepts that are often of interest to analysts, such as health, educational attainment and trust in government. This course will explore various approaches for quantifying these concepts. The course begins with an overview of the different levels of measurement and ways to transform variables. We’ll then discuss how to construct and build a measurement model. We’ll next examine surveys, as they are one of the most frequently used measurement tools. As part of this discussion, we’ll cover survey sampling, design and evaluation. Lastly, we’ll consider different ways to judge the quality of a measure, such as by its level of reliability or validity. By the end of this course, you should be able to develop and critically assess measures for concepts worth study. After all, a good analysis is built on good measures.

In this 2-hour long project-based course, you will build and evaluate multiple linear regression models using Python. You will use scikit-learn to calculate the regression, while using pandas for data management and seaborn for data visualization. The data for this project consists of the very popular Advertising dataset to predict sales revenue based on advertising spending through media such as TV, radio, and newspaper.

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

- Build univariate and multivariate linear regression models using scikit-learn
- Perform Exploratory Data Analysis (EDA) and data visualization with seaborn
- Evaluate model fit and accuracy using numerical measures such as R² and RMSE
- Model interaction effects in regression using basic feature engineering techniques

This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. 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, this means instant access to a cloud desktop with Jupyter Notebooks 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.

In this 2-hour long project-based course, you will learn how to retrieve data from tables in a database using SQL SELECT statement with SQL Aggregate functions. The aggregate functions we will consider in this project are COUNT, SUM, MIN, MAX and AVG. Aggregate functions are used to summarize data from rows of a table into a single value. In addition, you will learn how to set conditions on the output of an aggregate function using the HAVING clause. Finally, you will learn how to tidy up the result set of aggregate functions using the ROUND function.

Note: You do not need to be a data administrator or data analyst to be successful in this guided project, just a familiarity with querying databases using SQL SELECT statement suffice for this project. If you are not familiar with SQL and want to learn the basics, start with my previous guided projects titled “Performing Data definition and Manipulation in SQL." and “Querying Databases using SQL SELECT statement”

This course introduces the basics of Python 3, including conditional execution and iteration as control structures, and strings and lists as data structures. You'll program an on-screen Turtle to draw pretty pictures. You'll also learn to draw reference diagrams as a way to reason about program executions, which will help to build up your debugging skills. The course has no prerequisites. It will cover Chapters 1-9 of the textbook "Fundamentals of Python Programming," which is the accompanying text (optional and free) for this course.

The course is for you if you're a newcomer to Python programming, if you need a refresher on Python basics, or if you may have had some exposure to Python programming but want a more in-depth exposition and vocabulary for describing and reasoning about programs.

This is the first of five courses in the Python 3 Programming Specialization.

This course introduces the dictionary data structure and user-defined functions. You’ll learn about local and global variables, optional and keyword parameter-passing, named functions and lambda expressions. You’ll also learn about Python’s sorted function and how to control the order in which it sorts by passing in another function as an input. For your final project, you’ll read in simulated social media data from a file, compute sentiment scores, and write out .csv files. It covers chapters 10-16 of the textbook “Fundamentals of Python Programming,” which is the accompanying text (optional and free) for this course.

The course is well-suited for you if you have already taken the "Python Basics" course and want to gain further fundamental knowledge of the Python language. Together, both courses are geared towards newcomers to Python programming, those who need a refresher on Python basics, or those who may have had some exposure to Python programming but want a more in-depth exposition and vocabulary for describing and reasoning about programs.

This is a follow-up to the "Python Basics" course (course 1 of the Python 3 Programming Specialization), and it is the second of five courses in the specialization.

This course will introduce you to the linear regression model, which is a powerful tool that researchers can use to measure the relationship between multiple variables. We’ll begin by exploring the components of a bivariate regression model, which estimates the relationship between an independent and dependent variable. Building on this foundation, we’ll then discuss how to create and interpret a multivariate model, binary dependent variable model and interactive model. We’ll also consider how different types of variables, such as categorical and dummy variables, can be appropriately incorporated into a model. Overall, we’ll discuss some of the many different ways a regression model can be used for both descriptive and causal inference, as well as the limitations of this analytical tool. By the end of the course, you should be able to interpret and critically evaluate a multivariate regression analysis.

What are some of the most popular data science tools, how do you use them, and what are their features? In this course, you'll learn about Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. You will learn about what each tool is used for, what programming languages they can execute, their features and limitations. With the tools hosted in the cloud on Skills Network Labs, you will be able to test each tool and follow instructions to run simple code in Python, R or Scala. To end the course, you will create a final project with a Jupyter Notebook on IBM Watson Studio and demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers.

This course focuses on how analysts can measure and describe the confidence they have in their findings. The course begins with an overview of the key probability rules and concepts that govern the calculation of uncertainty measures. We’ll then apply these ideas to variables (which are the building blocks of statistics) and their associated probability distributions. The second half of the course will delve into the computation and interpretation of uncertainty. We’ll discuss how to conduct a hypothesis test using both test statistics and confidence intervals. Finally, we’ll consider the role of hypothesis testing in a regression context, including what we can and cannot learn from the statistical significance of a coefficient. By the end of the course, you should be able to discuss statistical findings in probabilistic terms and interpret the uncertainty of a particular estimate.