Google dominated the web analytics industry in 2024, with ***** of its web analytics technologies maintaining the top three positions in the global market. Google Global Site Tag was first with a market share of over ** percent, followed by Google Analytics and Google Universal Analytics who had market shares of approximately ** and ** percent, respectively. When all ***** technologies were combined, Google maintained more than ** percent of the total market share.

This dataset is a custom reference of Google Analytics field definitions.

It was specifically compiled to enhance datasets like the Google Analytics 360 data from the Google Merchandise Store, which lacks field descriptions in its original BigQuery schema. By providing detailed definitions for each field, this reference aims to improve the interpretability of the data—especially when used by language models or analytics tools that rely on contextual understanding to process and answer queries effectively.

Case study: How does a bike-share navigate speedy success?

Scenario:

As a data analyst on Cyclistic's marketing team, our focus is on enhancing annual memberships to drive the company's success. We aim to analyze the differing usage patterns between casual riders and annual members to craft a marketing strategy aimed at converting casual riders. Our recommendations, supported by data insights and professional visualizations, await Cyclistic executives' approval to proceed.

About the company

In 2016, Cyclistic launched a bike-share program in Chicago, growing to 5,824 bikes and 692 stations. Initially, their marketing aimed at broad segments with flexible pricing plans attracting both casual riders (single-ride or full-day passes) and annual members. However, recognizing that annual members are more profitable, Cyclistic is shifting focus to convert casual riders into annual members. To achieve this, they plan to analyze historical bike trip data to understand the differences and preferences between the two user groups, aiming to tailor marketing strategies that encourage casual riders to purchase annual memberships.

Project Overview:

This capstone project is a culmination of the skills and knowledge acquired through the Google Professional Data Analytics Certification. It focuses on Track 1, which is centered around Cyclistic, a fictional bike-share company modeled to reflect real-world data analytics scenarios in the transportation and service industry.

Dataset Acknowledgment:

We are grateful to Motivate Inc. for providing the dataset that serves as the foundation of this capstone project. Their contribution has enabled us to apply practical data analytics techniques to a real-world dataset, mirroring the challenges and opportunities present in the bike-sharing sector.

Objective:

The primary goal of this project is to analyze the Cyclistic dataset to uncover actionable insights that could help the company optimize its operations, improve customer satisfaction, and increase its market share. Through comprehensive data exploration, cleaning, analysis, and visualization, we aim to identify patterns and trends that inform strategic business decisions.

Methodology:

Data Collection: Utilizing the dataset provided by Motivate Inc., which includes detailed information on bike usage, customer behavior, and operational metrics. Data Cleaning and Preparation: Ensuring the dataset is accurate, complete, and ready for analysis by addressing any inconsistencies, missing values, or anomalies. Data Analysis: Applying statistical methods and data analytics techniques to extract meaningful insights from the dataset.

Visualization and Reporting:

Creating intuitive and compelling visualizations to present the findings clearly and effectively, facilitating data-driven decision-making. Findings and Recommendations:

Conclusion:

The Cyclistic Capstone Project not only demonstrates the practical application of data analytics skills in a real-world scenario but also provides valuable insights that can drive strategic improvements for Cyclistic. Through this project, showcasing the power of data analytics in transforming data into actionable knowledge, underscoring the importance of data-driven decision-making in today's competitive business landscape.

Acknowledgments:

Special thanks to Motivate Inc. for their support and for providing the dataset that made this project possible. Their contribution is immensely appreciated and has significantly enhanced the learning experience.

STRATEGIES USED

Case Study Roadmap - ASK

●What is the problem you are trying to solve? ●How can your insights drive business decisions?

Key Tasks ● Identify the business task ● Consider key stakeholders

Deliverable ● A clear statement of the business task

Case Study Roadmap - PREPARE

● Where is your data located? ● Are there any problems with the data?

Key tasks ● Download data and store it appropriately. ● Identify how it’s organized.

Deliverable ● A description of all data sources used

Case Study Roadmap - PROCESS

● What tools are you choosing and why? ● What steps have you taken to ensure that your data is clean?

Key tasks ● Choose your tools. ● Document the cleaning process.

Deliverable ● Documentation of any cleaning or manipulation of data

Case Study Roadmap - ANALYZE

● Has your data been properly formaed? ● How will these insights help answer your business questions?

Key tasks ● Perform calculations ● Formatting

Deliverable ● A summary of analysis

Case Study Roadmap - SHARE

● Were you able to answer all questions of stakeholders? ● Can Data visualization help you share findings?

Key tasks ● Present your findings ● Create effective data viz.

Deliverable ● Supporting viz and key findings

**Case Study Roadmap - A...

During a November 2022 survey carried out among professionals involved in content marketing, around ** percent stated that they used Google Analytics to measure the performance of their content marketing activities; ** percent said that they used Adobe Analytics for this purpose. Parse.ly came third as the most used measure tool - with ** percent of content marketers relying on it.

The global social media analytics tools market is experiencing robust growth, driven by the increasing adoption of social media for business and personal use. The market's expansion is fueled by several key factors: the escalating need for businesses to understand consumer behavior and preferences on social platforms, the growing importance of data-driven marketing strategies, and the emergence of sophisticated analytics tools capable of processing vast amounts of social media data. This demand is particularly strong across various segments, including large enterprises leveraging analytics for comprehensive marketing campaigns, small and medium-sized businesses (SMBs) seeking cost-effective solutions to improve their social media ROI, and agencies managing multiple client accounts needing efficient tools to track performance. The market showcases a diverse range of tools encompassing cloud-based, SaaS, web, and mobile applications, catering to a broad spectrum of user needs and technological preferences. While the cloud and SaaS models dominate, the continued evolution of mobile-native applications (Android and iOS) reflects the growing importance of on-the-go access to real-time social media insights. Competition in the market is intense, with established players like Google Analytics and Adobe Analytics vying for market share alongside a plethora of specialized tools catering to specific needs. Companies such as Hootsuite, Sprout Social, and others offer comprehensive dashboards, encompassing social listening, sentiment analysis, and performance tracking. However, the market also presents challenges. Data privacy concerns and the ever-changing social media landscape require constant tool adaptation and improvement. Furthermore, the cost of advanced analytics tools can be a barrier to entry for some businesses, particularly SMBs. Looking ahead, the market will continue its upward trajectory, fueled by technological innovation (e.g., AI-powered analytics), increased data security measures, and the integration of social media analytics with other marketing technologies. This integration will facilitate more holistic and data-driven marketing strategies. Regional growth will vary, with North America and Europe expected to maintain a significant share, while emerging markets in Asia-Pacific show considerable potential for future expansion. The projected Compound Annual Growth Rate (CAGR) reflects a steady and significant expansion of the market over the next decade.

Project Name: Divvy Bikeshare Trip Data_Year2020 Date Range: April 2020 to December 2020. Analyst: Ajith Software: R Program, Microsoft Excel IDE: RStudio

The following are the basic system requirements, necessary for the project: Processor: Intel i3 or AMD Ryzen 3 and higher Internal RAM: 8 GB or higher Operating System: Windows 7 or above, MacOS

**Data Usage License: https://ride.divvybikes.com/data-license-agreement ** Introduction:

In this case, study we aim to utilize different data analysis techniques and tools, to understand the rental patterns of the divvy bike sharing company and understand the key business improvement suggestions. This case study is a mandatory project to be submitted to achieve the Google Data Analytics Certification. The data utilized in this case study was licensed based on the provided data usage license. The trips between April 2020 to December 2020 are used to analyse the data.

Scenario: Marketing team needs to design marketing strategies aimed at converting casual riders into annual members. In order to do that, however, the marketing analyst team needs to better understand how annual members and casual riders differ.

Objective: The main objective of this case study, is to understand the customer usage patterns and the breakdown of customers, based on their subscription status and the average durations of the rental bike usage.

Introduction to Data: The Data provided for this project, is adhered to the data usage license, laid down by the source company. The source data was provided in the CSV files and are month and quarter breakdowns. A total of 13 columns of data was provided in each csv file.

The following are the columns, which were initially observed across the datasets.

Ride_id Ride_type Start_station_name Start_station_id End_station_name End_station_id Usertype Start_time End_time Start_lat Start_lng End_lat End_lng

Documentation, Cleaning and Preparing Data for Analysis: The total size of the datasets, for the year 2020, is approximately 450 MB, which is tiring job, when you have to upload them to the SQL database and visualize using the BI tools. I wanted to improve my skills into R environment and this is the best opportunity and optimal to use R for the data analysis.

For more insights, installation procedures for R and RStudio, please refer to the following URL, for additional information.

R Projects Document: https://www.r-project.org/other-docs.html RStudio Download: https://www.rstudio.com/products/rstudio/ Installation Guide: https://www.youtube.com/watch?v=TFGYlKvQEQ4

The app analytics market is experiencing robust growth, driven by the increasing adoption of mobile applications across various sectors and the need for businesses to understand user behavior and optimize app performance. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033. This growth is fueled by several key trends, including the rising demand for personalized user experiences, the proliferation of mobile-first strategies, and the increasing sophistication of analytics tools offering deeper insights into app usage, user engagement, and monetization strategies. Key players like Google, Adobe, Amazon Web Services, and others are continuously innovating to provide advanced features such as predictive analytics, real-time dashboards, and integration with other marketing technologies. Furthermore, the market is segmented by various factors, including deployment mode (cloud-based, on-premise), application type (gaming, social media, e-commerce), and enterprise size, allowing for targeted solutions and specialized analytics. The competitive landscape is characterized by a mix of established players and emerging startups, leading to continuous innovation and competitive pricing. Despite the positive outlook, the market faces certain restraints, such as the increasing cost of implementing and maintaining advanced analytics solutions and the need for skilled professionals to interpret and leverage the generated data effectively. Data privacy concerns and the increasing complexity of app analytics tools also pose challenges. However, the overall market trajectory is positive, with continuous innovation driving market expansion and adoption across diverse industries. The growth trajectory indicates a significant opportunity for businesses to leverage app analytics for better decision-making, enhanced user experiences, and increased profitability. The anticipated market size in 2033 is projected to exceed $50 billion, reflecting the continued dominance and evolution of mobile applications and the crucial role of analytics in their success.

Discover the booming mobile app analytics tool market! Our in-depth analysis reveals a $15 billion market in 2025, projected to reach $56 billion by 2033 with an 18% CAGR. Learn about key drivers, trends, and top players like Amplitude, Google, and AppsFlyer. Get insights into market segmentation and regional breakdowns.

Google Ads Sales Dataset for Data Analytics Campaigns (Raw & Uncleaned) 📝 Dataset Overview This dataset contains raw, uncleaned advertising data from a simulated Google Ads campaign promoting data analytics courses and services. It closely mimics what real digital marketers and analysts would encounter when working with exported campaign data — including typos, formatting issues, missing values, and inconsistencies.

It is ideal for practicing:

Data cleaning

Exploratory Data Analysis (EDA)

Marketing analytics

Campaign performance insights

Dashboard creation using tools like Excel, Python, or Power BI

📁 Columns in the Dataset Column Name ----- -Description Ad_ID --------Unique ID of the ad campaign Campaign_Name ------Name of the campaign (with typos and variations) Clicks --Number of clicks received Impressions --Number of ad impressions Cost --Total cost of the ad (in ₹ or $ format with missing values) Leads ---Number of leads generated Conversions ----Number of actual conversions (signups, sales, etc.) Conversion Rate ---Calculated conversion rate (Conversions ÷ Clicks) Sale_Amount ---Revenue generated from the conversions Ad_Date------ Date of the ad activity (in inconsistent formats like YYYY/MM/DD, DD-MM-YY) Location ------------City where the ad was served (includes spelling/case variations) Device------------ Device type (Mobile, Desktop, Tablet with mixed casing) Keyword ----------Keyword that triggered the ad (with typos)

⚠️ Data Quality Issues (Intentional) This dataset was intentionally left raw and uncleaned to reflect real-world messiness, such as:

Inconsistent date formats

Spelling errors (e.g., "analitics", "anaytics")

Duplicate rows

Mixed units and symbols in cost/revenue columns

Missing values

Irregular casing in categorical fields (e.g., "mobile", "Mobile", "MOBILE")

🎯 Use Cases Data cleaning exercises in Python (Pandas), R, Excel

Data preprocessing for machine learning

Campaign performance analysis

Conversion optimization tracking

Building dashboards in Power BI, Tableau, or Looker

💡 Sample Analysis Ideas Track campaign cost vs. return (ROI)

Analyze click-through rates (CTR) by device or location

Clean and standardize campaign names and keywords

Investigate keyword performance vs. conversions

🔖 Tags Digital Marketing · Google Ads · Marketing Analytics · Data Cleaning · Pandas Practice · Business Analytics · CRM Data

In March 2025, a total of ***** developer tools were available to customers on the Google Cloud Platform (GCP) marketplace. Most tools belonged to the virtual machines type, with ***** tools, followed by tools for software as a service (SaaS) and APIs with ***.

Discover the booming Business Analytics Tools market! Learn about its $50 billion valuation, 15% CAGR, key drivers, leading vendors (QlikView, Power BI, Tableau), and future trends impacting this rapidly expanding sector. Explore market segmentation and regional analysis in this comprehensive report.

It started as a lightweight alternative to Excel, tucked quietly inside the broader Google ecosystem. But fast-forward to 2025, and Google Sheets isn’t just a spreadsheet tool; it’s a platform reshaping how individuals and businesses collaborate with data. Whether you’re a startup founder tracking KPIs, a school administrator running reports,...

The market for website analytics tools is witnessing significant growth, driven by the increasing adoption of digital marketing and the need for businesses to track and measure the effectiveness of their online campaigns. The market is projected to reach a value of XXX million by 2033, exhibiting a CAGR of XX% over the forecast period (2025-2033). Cloud-based deployment models and the growing adoption of website analytics tools by SMEs are contributing to the market's expansion. Key trends shaping the website analytics tool market include the adoption of artificial intelligence (AI) and machine learning (ML) to enhance data analysis capabilities, the growing importance of data privacy and security, and the emergence of real-time analytics. The increasing availability of open-source website analytics tools is also expected to intensify competition in the market. Prominent players in the market include Matomo, Google Analytics, Tiny Analytics, Mixpanel, SimilarWeb, Hotjar, Woopra, Adobe Analytics, Statcounter, Amplitude, Heap, Visual Website Optimizer (VWO), Kissmetrics, Indicative, Chartbeat, and many others. The market is segmented across regions including North America, South America, Europe, Middle East & Africa, and Asia Pacific.

The AI Data Analytics Tools market is booming, projected to reach $3152 million by 2025, with a 32.6% CAGR. Discover key trends, regional insights, and leading companies driving this explosive growth in healthcare, finance, and more. Explore the latest market analysis now!

Google data search exercises can be used to practice finding data or statistics on a topic of interest, including using Google's own internal tools and by using advanced operators.

Discover the booming web analytics software market! This in-depth analysis reveals a $15 billion market in 2025, projected to reach $45 billion by 2033, driven by cloud adoption and data-driven decision-making. Learn about key players, market trends, and regional growth.

The Indonesia Big Data Analytics Software market is experiencing robust growth, projected to reach a market size of $43.15 million in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 9.35% from 2019 to 2033. This expansion is driven by several key factors. The increasing adoption of cloud-based solutions offers scalability and cost-effectiveness, appealing to both SMEs and large enterprises. Furthermore, various end-user verticals, including manufacturing, oil and gas, retail, healthcare, and others, are increasingly leveraging big data analytics to gain valuable insights from their data, improve operational efficiency, and enhance decision-making processes. Government initiatives promoting digital transformation and technological advancement within Indonesia are also contributing significantly to market growth. The preference for on-premises solutions remains, catering to organizations with stringent data security and compliance requirements. However, this segment's growth might be comparatively slower than the cloud segment due to higher initial investment costs and ongoing maintenance needs. Competition is fierce, with established players like Teradata, SAS, SAP, Tableau, IBM, Oracle, Google, Microsoft, and Cloudera, among others, vying for market share. This competitive landscape fosters innovation and drives the development of advanced analytics solutions tailored to the specific needs of the Indonesian market. The forecast period (2025-2033) anticipates continued strong growth, fueled by increasing digitalization across industries and a rising demand for data-driven insights. While precise figures for individual market segments and regional breakdowns within Indonesia are unavailable, extrapolating from the overall market size and CAGR suggests a substantial expansion across all segments. Growth will likely be unevenly distributed, with the cloud deployment mode and large enterprise segments potentially outpacing others due to their higher adoption rates and greater budgets for advanced analytics technology. The success of individual vendors will depend on factors such as their ability to adapt to the local market’s specific needs, provide strong customer support, and offer competitive pricing and technological advancements. Recent developments include: June 2024: Indosat Ooredoo Hutchison (Indosat) and Google Cloud expanded their long-term alliance to accelerate Indosat’s transformation from telco to AI Native TechCo. The collaboration will combine Indosat’s vast network, operational, and customer datasets with Google Cloud’s unified AI stack to deliver exceptional experiences to over 100 million Indosat customers and generative AI (GenAI) solutions for businesses across Indonesia. These include geospatial analytics and predictive modeling, real-time conversation analysis, and back-office transformation. Indosat’s early adoption of an AI-ready data analytics platform exemplifies its forward-thinking approach., June 2024: Palo Alto Networks launched a new cloud facility in Indonesia, catering to the rising demand for local data residency compliance. The move empowers organizations in Indonesia with access to Palo Alto Networks' Cortex XDR advanced AI and analytics platform that offers a comprehensive security solution by unifying endpoint, network, and cloud data. With this new infrastructure, Indonesian customers can ensure data residency by housing their logs and analytics within the country.. Key drivers for this market are: Higher Emphasis on the Use of Analytics Tools to Empower Decision Making, Rapid Increase in the Generation of Data Coupled with Availability of Several End User Specific Tools due to the Growth in the Local Landscape. Potential restraints include: Higher Emphasis on the Use of Analytics Tools to Empower Decision Making, Rapid Increase in the Generation of Data Coupled with Availability of Several End User Specific Tools due to the Growth in the Local Landscape. Notable trends are: Small and Medium Enterprises to Hold Major Market Share.

Google Play Store is the emerging platform in which millions of applications are developing and releasing by developers. Users can install different categories of applications on their Android phones. .In the beginning, we tried online scraping tools that are available on the internet for data extraction, but we were not satisfied with the extraction process of these tools because each tool has some limitations. Then we used web scraping, for data collection from Google Play Store and we collected the metadata of various categories of applications for one month from 25th March to 24th April in the year 2020. We deleted all duplicated rows and find total null values in each column using a heat map and filled missing values by taking the mean of columns for better analysis. We collected different attributes above 3500 apps of different categories and there are a total of 48 categories of apps in our dataset which we have collected from the Google Play Store. When we were collecting data, we did not try to collect data of any specific category and we tried to collect all categories of data to find more about each category available in Google Play Store dataset. We performed some analysis of this dataset.

if you want to know about analysis, you can read our article .

"Analyzing App Releasing and Updating behavior of Android Apps Developers"

Discover the booming Enterprise Website Analytics Software market! Our report reveals a $15 billion market in 2025, projected to grow at a 12% CAGR through 2033. Learn about key trends, leading companies (Google, Adobe, SEMrush), and regional market shares. Get your insights now!

This Google for Jobs API dataset provides comprehensive real-time job listing data directly from Google's job search engine. It includes detailed job information such as titles, descriptions, requirements, salaries, locations, employer details, and application links. The data aggregates listings from major job boards, company websites, and recruiting platforms that appear in Google for Jobs search results. Users can leverage this API for building job search applications, conducting employment market research, salary analysis, and career development tools. The API supports advanced filtering by location, job type, experience level, salary range, and company size. Whether you're developing a recruitment platform, job board, or workforce analytics tool, this Google for Jobs API provides current and reliable employment data directly from Google's comprehensive job search index.