How many times have you decided to learn a programming language but got stuck somewhere along the way, grew frustrated, and gave up? This specialization is designed for learners who have little or no programming experience but want to use Python as a tool to play with data. The first course will introduce you to programming languages, with Python as an example. You are going to learn how to use variables and operators, as well as input/output and flow controls to build simple Python programs. The pace will be very slow, so you will feel comfortable learning Python as quickly or as slowly as you like. Are you ready? Let's go! Logo image courtesy of Mourizal Zativa. Available on Unsplash here: https://unsplash.com/photos/gNMVpAPe3PE

This is a self-paced lab that takes place in the Google Cloud console. This lab shows you how to query public tables and load sample data into BigQuery using the GCP Console. Watch the following short video Get Meaningful Insights with Google BigQuery.

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

In this course, we extend your business forecasting expertise from the first two courses of our Business Forecasting Specialisation on Time Series Models and Regression Models. We will explore the role of judgmental forecasting, when more quantitative forecasting methods have limitations, and we need to generate further business insights. We will be exploring some structured methodologies to create judgmental business forecasts using Business Indicators, Subjective Assessment Methods, and Exploratory Methods. For each of these methods, we will look at how we can use Excel to help us in achieving these judgmental forecasts and how Excel can help us visualising our forecast findings. Being judgmental forecasting methods, we will also look at the role of biases in Business Forecasting,

In daily life, our connections with family and friends form our social networks. Across the country, roads between different places form transportation networks. In research areas, collaborations among different researchers form research collaboration networks. Visible or invisible, networks exist in many aspects of our life. Being able to visualize networks will help us understand relationships embedded in complicated network information. In this project, learners will visualize various types of static networks of marvel heroes using the igraph package and base R plot functions. We can easily use static networks in reports and presentations. A good handle of this method will help learners, from both academia and industry, quickly express informative relationships and connections among different variables.

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 build bar graphs, scatter plots, Choropleth maps and Wordcloud to analyze and visualize the global scenario of Covid-19 and perform medical analysis to various conditions that contribute to death due to Covid-19. We will be using two separate datasets for this guided project. The first dataset has been taken from worldometer and the second one has been made available by the Centers for Disease Control and Prevention (CDC), United States. We will be using Python as our Programming language and Google Colab as our notebook. It is required for you to have a Gmail Account for this project. It is recommended to have some experience in the Python programming language but even if you do not have any prior experience in Python programming or medical science, you will be able to complete this project. This project is beginner-friendly. We will visualize the current global scenario of Covid-19 using bar graphs and scatter plots followed by geographical data visualization using Choropleth maps. Then we will dive into medical analysis. We will then visualize how Covid deaths vary with respect to age group and how various pre-existing medical conditions vary with age. Then we will visualize and analyze how various medical conditions contribute to Covid death. We will also compare the performance of all the 50 states in the US against Covid. In the final task, we will finish by creating WordCloud text visualization of various medical conditions and condition groups that contribute to Covid deaths. 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 one hour long project-based course, you will tackle a real-world problem in data analysis and visualization. You will process a dataset of crime incidents in the city of Boston, and use this data to create an animated heatmap displaying crime hotspots. Heatmaps use color to display a quantity that changes over two dimensions. By the end of this project, you will have created heatmaps using code you will write in Python.

It is easy to spend money, but hard to get the value. From 2007 to 2010, Apple made $27 billion from iPhone with a profit of $15.6 billion. Apple could not achieve this success without its global sourcing strategy. However, one of Apple’s key suppliers, Samsung Electronics, became a competitor and used its cost advantage to over-take Apple in the global market. Meanwhile, many new suppliers and products are emerging constantly. To continue the success, Apple must explore the global markets to identify and select new suppliers that are capable, inexpensive and financially robust. The question is, how to do it right for this year? What Apple experienced is typical in practice, as a company may have thousands of suppliers, and numerous new suppliers and products / services emerge constantly and globally, which requires a frequent adjustment of the supply base. In this course, you will learn sourcing analytics which applies data analytics and business intelligence to supplier development and management. Specifically, you will learn market intelligence, bargain power analysis, and supplier analysis, to identify and select suppliers with the objective of getting more value with less spend.

At the end of this project, you will learn about the Where, Why, and How of List Comprehension in Python. We are going to start with a quick introduction to lists and then we will talk about what list comprehension is and how and where we can use it. In the final task, we will load a JSON dataset containing information about UFO observations reported by civilians around the globe. we are going to use list comprehension to extract useful information out of our data.