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.
There are 11 Courses in this Professional Certificate Specialization are as follows:
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Explain what Data Analytics is and the key steps in the Data Analytics process
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Differentiate between different data roles such as Data Engineer, Data Analyst, Data Scientist, Business Analyst, and Business Intelligence Analyst
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Describe the different types of data structures, file formats, and sources of data
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Describe the data analysis process involving collecting, wrangling, mining, and visualizing data
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Display working knowledge of Excel for Data Analysis.
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Perform basic spreadsheet tasks including navigation, data entry, and using formulas.
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Employ data quality techniques to import and clean data in Excel.
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Analyze data in spreadsheets by using filter, sort, look-up functions, as well as pivot tables.
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Create basic visualizations such as line graphs, bar graphs, and pie charts using Excel spreadsheets.
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Explain the important role charts play in telling a data-driven story.
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Construct advanced charts and visualizations such as Treemaps, Sparklines, Histogram, Scatter Plots, and Filled Map Charts.
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Build and share interactive dashboards using Excel and Cognos Analytics.
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Learn Python - the most popular programming language and for Data Science and Software Development.
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Apply Python programming logic Variables, Data Structures, Branching, Loops, Functions, Objects & Classes.
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Demonstrate proficiency in using Python libraries such as Pandas & Numpy, and developing code using Jupyter Notebooks.
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Access and web scrape data using APIs and Python libraries like Beautiful Soup.
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Play the role of a Data Scientist / Data Analyst working on a real project.
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Demonstrate your Skills in Python - the language of choice for Data Science and Data Analysis.
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Apply Python fundamentals, Python data structures, and working with data in Python.
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Build a dashboard using Python and libraries like Pandas, Beautiful Soup and Plotly using Jupyter notebook.
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Analyze data within a database using SQL and Python.
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Create a relational database and work with multiple tables using DDL commands.
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Construct basic to intermediate level SQL queries using DML commands.
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Compose more powerful queries with advanced SQL techniques like views, transactions, stored procedures, and joins.
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Develop Python code for cleaning and preparing data for analysis - including handling missing values, formatting, normalizing, and binning data
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Perform exploratory data analysis and apply analytical techniques to real-word datasets using libraries such as Pandas, Numpy and Scipy
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Manipulate data using dataframes, summarize data, understand data distribution, perform correlation and create data pipelines
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Build and evaluate regression models using machine learning scikit-learn library and use them for prediction and decision making
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Implement data visualization techniques and plots using Python libraries, such as Matplotlib, Seaborn, and Folium to tell a stimulating story
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Create different types of charts and plots such as line, area, histograms, bar, pie, box, scatter, and bubble
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Create advanced visualizations such as waffle charts, word clouds, regression plots, maps with markers, & choropleth maps
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Generate interactive dashboards containing scatter, line, bar, bubble, pie, and sunburst charts using the Dash framework and Plotly library
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Apply different techniques to collect and wrangle data
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Showcase your Data Analysis and Visualization skills
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Create a data analysis report and a compelling presentation
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Demonstrate proficiency with various Python Libraries
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Describe how you can use Generative AI tools and techniques in the context of data analytics across industries
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Implement various data analytic processes such as data preparation, analysis, visualization and storytelling using Generative AI tools
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Evaluate real-world case studies showcasing the successful application of Generative AI in deriving meaningful insights
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Analyze the ethical considerations and challenges associated with using Generative AI in data analytics
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Describe the role of a data analyst and some career path options as well as the prospective opportunities in the field.
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Explain how to build a foundation for a job search, including researching job listings, writing a resume, and making a portfolio of work.
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Summarize what a candidate can expect during a typical job interview cycle, different types of interviews, and how to prepare for interviews.
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Explain how to give an effective interview, including techniques for answering questions and how to make a professional personal presentation.
