Data Science Fundamentals with Python and SQL

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.

Whether you want to become a Data Scientist, Data Analyst, or Data Engineer, knowing how to work with SQL (Structured Query Language) is an essential skill. SQL is a powerful language used for communicating with and extracting data from databases. In this hands-on course, you’ll learn how to retrieve, organize, and manage data using SQL, starting with the fundamentals and moving to more advanced techniques. You’ll also explore how to combine SQL with Python to work with databases and analyze data. By the end of this course, you’ll be able to: - Write SQL queries using SELECT, INSERT, UPDATE, and DELETE to retrieve and manage data. - Filter and organize data using WHERE, COUNT, DISTINCT, LIMIT, ORDER BY, GROUP BY, and built-in database functions. - Create and manage database tables using CREATE, ALTER, and DROP, and understand the differences between DDL and DML. - Query complex datasets using subqueries, string patterns, ranges, and INNER and OUTER JOINs to work with data across multiple tables. - Connect SQL with Python using Jupyter notebooks to access databases and integrate SQL into your data science workflow. - Explore advanced database concepts such as Stored Procedures, Views, and ACID transactions. Throughout the course, you will complete hands-on labs, work with real databases on the Cloud, and gain experience with real data science tools. In the final project, you’ll analyze multiple real-world datasets to demonstrate your ability to query and work with data. Ready to build your SQL skills? Join over 670,000 learners who have enrolled in this course. Earn a shareable certificate to showcase your data science skills on LinkedIn, your resume, or your CV.

Go from zero to working with real data in Python. No programming experience required. Data skills are some of the most in-demand skills in the job market. The U.S. Bureau of Labor Statistics projects data scientist employment to grow 34% from 2024 to 2034, much faster than average, with a median annual wage of $112,590.¹ Python is the language behind that work, and this course is where you start. This IBM course takes you from your first line of code to the skills data teams use every day. You'll start with Python basics, then build up through data structures, logic, functions, error handling, and object-oriented programming. From there, you'll work with real data using NumPy and Pandas, the industry-standard libraries for data analysis and AI, and learn to collect it from the web using REST APIs and web scraping with BeautifulSoup. You'll learn by doing. Across five modules, you'll complete more than 25 hands-on labs and practice projects in Jupyter Notebooks, the same environment professional data scientists use, with nothing to install or set up. By the end, you'll be able to write programs, clean and analyze datasets, pull data from live sources, and automate repetitive tasks, and you'll have practical work to point to in interviews. Whether you're aiming for a career in data science, data analytics, software development, data engineering, AI, or DevOps, or you want to add Python to the skills you already have, this course gives you the foundation to build on. Skills you'll gain include: - Writing Python programs using variables, data structures, loops, functions, and classes - Reading, writing, and processing data in CSV, JSON, and text formats - Analyzing and manipulating data with Pandas and NumPy - Collecting data from websites and services using REST APIs and web scraping - Working in Jupyter Notebooks to develop and document your code - Automating real-world tasks with Python scripts Join 1.5 million+ learners (4.6 stars from 43,000+ reviews, and 95% of learners liked this course). Finish with a shareable certificate you can add to your LinkedIn profile, resume, or CV. ¹ U.S. Bureau of Labor Statistics, Occupational Outlook Handbook: Data Scientists. Median wage as of May 2024; projected growth 2024 to 2034. This course builds foundational Python skills and is a first step toward data roles, not a guarantee of employment or salary.

This Statistics for Data Science course is designed to introduce you to the basic principles of statistical methods and procedures used for data analysis. After completing this course you will have practical knowledge of crucial topics in statistics including - data gathering, summarizing data using descriptive statistics, displaying and visualizing data, examining relationships between variables, probability distributions, expected values, hypothesis testing, introduction to ANOVA (analysis of variance), regression and correlation analysis. You will take a hands-on approach to statistical analysis using Python and Jupyter Notebooks – the tools of choice for Data Scientists and Data Analysts. At the end of the course, you will complete a project to apply various concepts in the course to a Data Science problem involving a real-life inspired scenario and demonstrate an understanding of the foundational statistical thinking and reasoning. The focus is on developing a clear understanding of the different approaches for different data types, developing an intuitive understanding, making appropriate assessments of the proposed methods, using Python to analyze our data, and interpreting the output accurately. This course is suitable for a variety of professionals and students intending to start their journey in data and statistics-driven roles such as Data Scientists, Data Analysts, Business Analysts, Statisticians, and Researchers. It does not require any computer science or statistics background. We strongly recommend taking the Python for Data Science course before starting this course to get familiar with the Python programming language, Jupyter notebooks, and libraries. An optional refresher on Python is also provided. After completing this course, a learner will be able to: ✔Calculate and apply measures of central tendency and measures of dispersion to grouped and ungrouped data. ✔Summarize, present, and visualize data in a way that is clear, concise, and provides a practical insight for non-statisticians needing the results. ✔Identify appropriate hypothesis tests to use for common data sets. ✔Conduct hypothesis tests, correlation tests, and regression analysis. ✔Demonstrate proficiency in statistical analysis using Python and Jupyter Notebooks.