IBM Data Analyst

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.

Analyzing data with Python is a key skill for aspiring Data Scientists and Analysts! This course takes you from the basics of importing and cleaning data to building and evaluating predictive models. You’ll learn how to collect data from various sources, wrangle and format it, perform exploratory data analysis (EDA), and create effective visualizations. As you progress, you’ll build linear, multiple, and polynomial regression models, construct data pipelines, and refine your models for better accuracy. Through hands-on labs and projects, you’ll gain practical experience using popular Python libraries such as Pandas, NumPy, Matplotlib, Seaborn, SciPy, and Scikit-learn. These tools will help you manipulate data, create insights, and make predictions. By completing this course, you’ll not only develop strong data analysis skills but also earn a Coursera certificate and an IBM digital badge to showcase your achievement.

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.

Get job-ready for a data analyst career in as little as 4 months. No prior experience needed.   Data skills are among the most in-demand skills in the job market. The U.S. Bureau of Labor Statistics projects employment of data scientists, a path this program also prepares you for, to grow 34% from 2024 to 2034, much faster than average, with a median annual wage of $112,590.¹ Every one of those careers starts with the same foundation: collecting, cleaning, analyzing, and communicating data. That's exactly what you'll learn here.   This IBM Professional Certificate teaches the skills employers look for in entry-level data analyst roles, including Data Analyst, Associate Data Analyst, and Business Analyst. You'll learn the tools professional analysts use every day: Excel, SQL, Python, Pandas, NumPy, Jupyter Notebooks, and IBM Cognos Analytics. You'll also have the option to learn how generative AI tools and techniques are used in data analysis, so you're ready for how the job is changing.   You won't just learn the tools, you'll use them. You'll work with real-world data sources and project scenarios, and you'll build a portfolio of hands-on projects that show hiring managers what you can do:   * Import, clean, and analyze a fleet vehicle inventory with Excel pivot tables * Turn car sales KPI data into an interactive dashboard with visualizations * Extract and graph financial data with the Pandas library in Python * Query census, crime, and school demographic data sets with SQL * Wrangle data, plot graphs, and build regression models to predict housing prices * Create a dynamic Python dashboard to monitor and improve US domestic flight reliability   You'll finish with a real-world capstone project designed to showcase your new skills, plus the soft skills that set analysts apart: working with stakeholders and telling a clear story with data.   What you'll walk away with:   * A Professional Certificate from IBM and an IBM Digital Badge to share on LinkedIn and your resume * A portfolio of projects and a capstone you can discuss in interviews * Access to career resources to support your job search * Up to 12 college credits and 6 ECTS credits, as this program is ACE® and FIBAA recommended²   It's also a strong foundation for what comes next. The skills you build here carry directly into data science, artificial intelligence, deep learning, and data engineering.   Join 500,000+ learners, backed by 98,000+ reviews of courses in this program.   ¹ U.S. Bureau of Labor Statistics, Occupational Outlook Handbook: Data Scientists. Median wage as of May 2024; projected growth for 2024 to 2034. This program is not a guarantee of employment or salary.   ² Credit recommendations are subject to acceptance by individual institutions.

Learn how to create data visualizations and dashboards using spreadsheets and analytics tools. This course covers some of the first steps for telling a compelling story with your data using various types of charts and graphs. You'll learn the basics of visualizing data with Excel and IBM Cognos Analytics without having to write any code. You'll start by creating simple charts in Excel such as line, pie and bar charts. You will then create more advanced visualizations with Treemaps, Scatter Charts, Histograms, Filled Map Charts, and Sparklines. Next you’ll also work with the Excel PivotChart feature as well as assemble several visualizations in an Excel dashboard. This course also teaches you how to use business intelligence (BI) tools like Cognos Analytics to create interactive dashboards. By the end of the course you will have an appreciation for the key role that data visualizations play in communicating your data analysis findings, and the ability to effectively create them. Throughout this course there will be numerous hands-on labs to help you develop practical experience for working with Excel and Cognos. There is also a final project in which you’ll create a set of data visualizations and an interactive dashboard to add to your portfolio, which you can share with peers, professional communities or prospective employers.

In an increasingly data-centric world, the ability to derive meaningful insights from raw data is essential. The IBM Data Analyst Capstone Project gives you the opportunity to apply the skills and techniques learned throughout the IBM Data Analyst Professional Certificate. Working with actual datasets, you will carry out tasks commonly performed by professional data analysts, such as data collection from multiple sources, data wrangling, exploratory analysis, statistical analysis, data visualization, and creating interactive dashboards. Your final deliverable will include a comprehensive data analysis report, complete with an executive summary, detailed insights, and a conclusion for organizational stakeholders. Throughout the project, you will demonstrate your proficiency in tools such as Jupyter Notebooks, SQL, Relational Databases (RDBMS), and Business Intelligence (BI) tools like IBM Cognos Analytics. You will also apply Python libraries, including Pandas, Numpy, Scikit-learn, Scipy, Matplotlib, and Seaborn. We recommend completing the previous courses in the Professional Certificate before starting this capstone project, as it integrates all key concepts and techniques into a single, real-world scenario.

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.

Ready to start a career in Data Analysis but not sure where to begin? This beginner-friendly course introduces you to the world of data and helps you build a strong foundation for your data career. You’ll discover what Data Analysts do, the skills and tools they use, and how their roles differ from those of Data Scientists and Data Engineers. You’ll also hear directly from data professionals as they share practical tips and career advice to help you get started. Explore the data ecosystem and learn how data is stored, managed, and processed. You’ll become familiar with databases, data warehouses, data marts, data lakes, and data pipelines, as well as Big Data technologies such as Hadoop, Hive, and Spark. Most importantly, you’ll learn how to turn raw data into meaningful insights. You’ll explore the key stages of data analysis, from collecting and cleaning data to analyzing results and communicating your findings through visualizations. You’ll then apply your knowledge in a hands-on final project based on a real-world data analysis scenario. By the end of this course, you’ll be able to: - Explain what Data Analytics is and describe the key stages of the data analysis process. - Differentiate between data roles such as Data Engineer, Data Analyst, Data Scientist, Business Analyst, and Business Intelligence Analyst. - Identify different data structures, file formats, and data sources. - Describe how data is collected, cleaned, analyzed, and visualized to uncover meaningful insights. No prior experience in data analysis, spreadsheets, or computer science is required. Whether you’re exploring a career in data or looking to develop new skills, this course will help you take your first steps toward a career in data. Join over 1,000,000 learners who have enrolled in this course and earn a shareable certificate to showcase your skills on LinkedIn, your resume, or your CV.

One of the most important skills of successful data scientists and data analysts is the ability to tell a compelling story by visualizing data and findings in an approachable and stimulating way. In this course you will learn many ways to effectively visualize both small and large-scale data. You will be able to take data that at first glance has little meaning and present that data in a form that conveys insights. This course will teach you to work with many Data Visualization tools and techniques. You will learn to create various types of basic and advanced graphs and charts like: Waffle Charts, Area Plots, Histograms, Bar Charts, Pie Charts, Scatter Plots, Word Clouds, Choropleth Maps, and many more! You will also create interactive dashboards that allow even those without any Data Science experience to better understand data, and make more effective and informed decisions. You will learn hands-on by completing numerous labs and a final project to practice and apply the many aspects and techniques of Data Visualization using Jupyter Notebooks and a Cloud-based IDE. You will use several data visualization libraries in Python, including Matplotlib, Seaborn, Folium, Plotly & Dash.

This comprehensive course unravels the potential of generative AI in data analytics. The course will provide an in-depth knowledge of the fundamental concepts, models, tools, and generative AI applications regarding the data analytics landscape. In this course, you will examine real-world applications and use generative AI to gain data insights using techniques such as prompts, visualization, storytelling, querying and so on. In addition, you will understand the ethical implications, considerations, and challenges of using generative AI in data analytics across different industries. You will acquire practical experience through hands-on labs where you will leverage generative AI models and tools such as ChatGPT, ChatCSV, Mostly.AI, SQLthroughAI and more. Finally, you will apply the concepts learned throughout the course to a data analytics project. Also, you will have an opportunity to test your knowledge with practice and graded quizzes and earn a certificate. This course is suitable for both practicing data analysts as well as learners aspiring to start a career in data analytics. It requires some basic knowledge of data analytics, prompt engineering, Python programming and generative artificial intelligence.