Data Science Foundations

Do you want to know why data science has been labeled the sexiest profession of the 21st century? After taking this course, you will be able to answer this question, understand what data science is and what data scientists do, and learn about career paths in the field. The art of uncovering insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and accurately predicted the Nile River's flooding every year. Since then, people have continued to use data to derive insights and predict outcomes. Recently, they have carved out a unique and distinct field for the work they do. This field is data science. In today's world, we use Data Science to find patterns in data and make meaningful, data-driven conclusions and predictions. This course is for everyone and teaches concepts like how data scientists use machine learning and deep learning and how companies apply data science in business. You will meet several data scientists, who will share their insights and experiences in data science. By taking this introductory course, you will begin your journey into this thriving field.

In order to be successful in Data Science, you need to be skilled with using tools that Data Science professionals employ as part of their jobs. This course teaches you about the popular tools in Data Science and how to use them. You will become familiar with the Data Scientist’s tool kit which includes: Libraries & Packages, Data Sets, Machine Learning Models, Kernels, as well as the various Open source, commercial, Big Data and Cloud-based tools. Work with Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. You will understand what each tool is used for, what programming languages they can execute, their features and limitations. This course gives plenty of hands-on experience in order to develop skills for working with these Data Science Tools. 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. Towards the end the course, you will create a final project with a Jupyter Notebook. You will demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers.

Kickstart your Python journey with this beginner-friendly, self-paced course taught by an expert. Python is one of the most popular programming languages, and the demand for individuals with Python skills continues to grow. This course takes you from zero to programming in Python in a matter of hours—no prior programming experience is necessary! You’ll begin with Python basics, including data types, expressions, variables, and string operations. You will explore essential data structures such as lists, tuples, dictionaries, and sets, learning how to create, access, and manipulate them. Next, you will delve into logic concepts like conditions and branching, learning how to use loops and functions, along with important programming principles like exception handling and object-oriented programming. As you progress, you will gain practical experience reading from and writing to files and working with common file formats. You’ll also use powerful Python libraries like NumPy and Pandas for data manipulation and analysis. The course also covers APIs and web scraping, teaching you how to interact with REST APIs using libraries like requests and extract data from websites using BeautifulSoup. You will practice and apply what you learn through hands-on labs using Jupyter Notebooks. By the end of this course, you’ll feel comfortable creating basic programs, working with data, and automating real-world tasks using Python. This course is suitable for individuals interested in pursuing careers in Data Science, Data Analytics, Software Development, Data Engineering, AI, and DevOps and a variety of other technology-related roles.

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.

This course is the seventh of eight. In this project, we will tackle a prediction problem: forecasting the number of bicycles that will be rented on a given day. Using historical data, we will consider factors such as weather conditions, the day of the week, and other relevant variables to accurately predict daily bicycle rentals. This will help ensure that our bicycle rental service is prepared with the appropriate number of bicycles each day. We will learn specifically about data acquisition and correlation.

This course is the fourth of eight courses aiming to help you to become confident working with algorithms and data structure. You will learn the history of algorithms, discretisation and pseudocode and Euclidean algorithm in pseudocode.

This course is the sixth of eight courses. This project provides an in-depth exploration of key Data Science concepts focusing on algorithm design. It enhances essential mathematics, statistics, and programming skills required for common data analysis tasks. You will engage in a variety of mathematical and programming exercises while completing a data clustering project using the K-means algorithm on a provided dataset.