• Take a business online
• Make it easy for people to find a business on the web
• Reach more people locally or on mobile
• Reach more customers with advertising
• Track and measure web traffic
• Sell products or services online
• Take a business global

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• Identify your main business goals and turn them into effective marketing campaigns.
• Maximize your measurement by assigning value to your marketing efforts and make informed marketing decisions.
• Turn insights into action using a test-and-learn mindset.

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• Understand how Google Analytics collects and processes data
• Set up your Analytics account in a way that supports your business objectives
• Describe how data is compiled into reports in Analytics

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• Setup a Google Analytics 4 property for a website or an app
• Collect the data you need for your business and use the various reporting tools and features
• Recognize key measurement features that can show the effectiveness of your online marketing efforts

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• Navigate the Google Analytics interface
• Understand the reports that are available in
• Analytics Generate and gain insight from your reports
• Manage the data you are sending to Analytics

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• 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

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• 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

• Descriptive Statistics, Sampling and Randomized Controlled Experiments, Probability, Sampling Distributions and the Central Limit Theorem, Regression, Common Tests of Significance, Resampling, Multiple Comparisons.

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• Data wrangling
• Web scraping
• Hypothesis building
• Python programming concepts
• Data exploration
• Mathematical computing

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• Modeling
• Spreadsheet
• Microsoft Excel
• Business Analytics
• Solver

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