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IBM Data Analyst Professional (2024)

📍 About the certificate

Prepare for a career as a data analyst. Build job-ready skills – and must-have AI skills – for an in-demand career.

In this program, you’ll learn in-demand skills like Python, Excel, and SQL to get job-ready in as little as 4 months, 10 hours a week.

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🥇 Professional Certificate

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📙 Course Structures

There are 11 Courses in this Professional Certificate Specialization are as follows:

• Introduction to Data Analytics

• Explain what Data Analytics is and the key steps in the Data Analytics process

• Differentiate between different data roles such as Data Engineer, Data Analyst, Data Scientist, Business Analyst, and Business Intelligence Analyst

• Describe the different types of data structures, file formats, and sources of data

• Describe the data analysis process involving collecting, wrangling, mining, and visualizing data

• Excel Basics for Data Analysis

• Display working knowledge of Excel for Data Analysis.

• Perform basic spreadsheet tasks including navigation, data entry, and using formulas.

• Employ data quality techniques to import and clean data in Excel.

• Analyze data in spreadsheets by using filter, sort, look-up functions, as well as pivot tables.

• Data Visualization and Dashboards with Excel and Cognos

• Create basic visualizations such as line graphs, bar graphs, and pie charts using Excel spreadsheets.

• Explain the important role charts play in telling a data-driven story.

• Construct advanced charts and visualizations such as Treemaps, Sparklines, Histogram, Scatter Plots, and Filled Map Charts.

• Build and share interactive dashboards using Excel and Cognos Analytics.

• Python for Data Science, AI & Development

• Learn Python - the most popular programming language and for Data Science and Software Development.

• Apply Python programming logic Variables, Data Structures, Branching, Loops, Functions, Objects & Classes.

• Demonstrate proficiency in using Python libraries such as Pandas & Numpy, and developing code using Jupyter Notebooks.

• Access and web scrape data using APIs and Python libraries like Beautiful Soup.

• Python Project for Data Science

• Play the role of a Data Scientist / Data Analyst working on a real project.

• Demonstrate your Skills in Python - the language of choice for Data Science and Data Analysis.

• Apply Python fundamentals, Python data structures, and working with data in Python.

• Build a dashboard using Python and libraries like Pandas, Beautiful Soup and Plotly using Jupyter notebook.

• Databases and SQL for Data Science

• Analyze data within a database using SQL and Python.

• Create a relational database and work with multiple tables using DDL commands.

• Construct basic to intermediate level SQL queries using DML commands.

• Compose more powerful queries with advanced SQL techniques like views, transactions, stored procedures, and joins.

• Data Analysis with Python

• Develop Python code for cleaning and preparing data for analysis - including handling missing values, formatting, normalizing, and binning data

• Perform exploratory data analysis and apply analytical techniques to real-word datasets using libraries such as Pandas, Numpy and Scipy

• Manipulate data using dataframes, summarize data, understand data distribution, perform correlation and create data pipelines

• Build and evaluate regression models using machine learning scikit-learn library and use them for prediction and decision making

• Data Visualization with Python

• Implement data visualization techniques and plots using Python libraries, such as Matplotlib, Seaborn, and Folium to tell a stimulating story

• Create different types of charts and plots such as line, area, histograms, bar, pie, box, scatter, and bubble

• Create advanced visualizations such as waffle charts, word clouds, regression plots, maps with markers, & choropleth maps

• Generate interactive dashboards containing scatter, line, bar, bubble, pie, and sunburst charts using the Dash framework and Plotly library

• IBM Data Analyst Capstone Project

• Apply different techniques to collect and wrangle data

• Showcase your Data Analysis and Visualization skills

• Create a data analysis report and a compelling presentation

• Demonstrate proficiency with various Python Libraries

• Generative AI: Enhance your Data Analytics Career

• Describe how you can use Generative AI tools and techniques in the context of data analytics across industries

• Implement various data analytic processes such as data preparation, analysis, visualization and storytelling using Generative AI tools

• Evaluate real-world case studies showcasing the successful application of Generative AI in deriving meaningful insights

• Analyze the ethical considerations and challenges associated with using Generative AI in data analytics

• Data Analyst Career Guide and Interview Preparation

• Describe the role of a data analyst and some career path options as well as the prospective opportunities in the field.

• Explain how to build a foundation for a job search, including researching job listings, writing a resume, and making a portfolio of work.

• Summarize what a candidate can expect during a typical job interview cycle, different types of interviews, and how to prepare for interviews.

• Explain how to give an effective interview, including techniques for answering questions and how to make a professional personal presentation.

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