Tag Archives: recommended


Google Analytics Individual Qualification (GAIQ):
The Google Analytics Individual Qualification (GAIQ) certification is one of the most popular and reliable certifications for data analysts. The GAIQ certification demonstrates an in-depth understanding of Google Analytics and the ability to use it proficiently to analyze data and make business decisions. The GAIA exam tests candidates on their knowledge of core functions like setting up Google Analytics, understanding the data, creating and customizing reports, integrating with other tools, implementing enhanced ecommerce tracking, and using Google Analytics for marketing and advertising measurement. Obtaining the GAIQ credential helps data analysts showcase their expertise with Google Analytics to potential employers.

Microsoft Power BI Certified Professional:
Power BI is one of the leading tools used by organizations worldwide for data visualization, analysis and reporting. The Microsoft Power BI Certified Professional certification validates candidates’ skills in connecting to and importing data from various data sources into Power BI using the Power BI service and Power BI Desktop. It tests candidates’ ability to analyze data using DAX (Data Analysis Expressions) functions and build interactive data visualizations and dashboards in Power BI. Earning this certification demonstrates to employers that data analysts can extract insights from data using Microsoft’s Power BI tool and handle the entire data analysis process from data preparation to visualization.

Tableau Desktop Specialist:
Tableau is a very popular BI tool used across industries for interactive data visualization. The Tableau Desktop Specialist certification demonstrates proficiency in connecting to databases and files, designing visualizations like graphs, tables and maps, customizing dashboards, handling calculations and joining multiple data sources using Tableau. It validates data analysts’ skills in using Tableau for preparation, analysis and presentation of data in a visual storytelling format. Passing this exam shows that the candidate understands tableau capabilities and best practices to efficiently transform raw data into impactful data stories. Earning this credential boosts data analysts’ career prospects.

certified Analytics Professional CAP®:
The CAP or Certified Analytics Professional certification is a vendor-neutral credential from the International Institute for Analytics (IIA). It demonstrates mastery over the entire data analysis process as well as principles of business management and communication. The CAP exam tests knowledge of specific analytical techniques and methods along with the ability to apply them appropriately to solve business problems. It covers topics like statistical analysis, data mining, predictive modeling, optimization modeling, experimentation, and communicating results to stakeholders. The CAP certification underscores data analysts’ capability to extract insights from complex datasets and translate them into actionable business recommendations. It is a much coveted certification for analytics professionals.

Oracle Certified Associate, Oracle Analytics Cloud:
This Oracle certification validates the skills required to design, develop and deploy analytics applications on Oracle Analytics Cloud (OAC). It tests knowledge of core concepts like OAC architecture, objects, security model, semantic modelling and data integration capabilities. Candidates are evaluated on their ability to architect solutions for OAC, load data from various sources, create dashboards and stories using preconfigured UI templates and publish/share them. Passing this Oracle credential establishes data analysts as OAC experts who can fully leverage the tool to deliver analytics and business intelligence projects on cloud. This opens up opportunities in OAC domain across various organizations worldwide.

Certified Analytics Professional Program (CAP®) in People Analytics:
This CAP certification offered by IIA focuses specifically on assessing competencies required for people analytics roles. It validates skills in procuring HR, talent and compensation data, performing statistical analyses to obtain insights into employee engagement, retention, performance and much more. Candidates are tested on using predictive modeling techniques like segmentation, attribution and predictive hiring to enhance people strategies and decisions. Earning this credential demonstrates mastery of people analytics methods, tools and theories to best leverage workforce data and enable data-driven HR. It equips data analysts with specialized credentials highly valued by HR departments and people analytics teams.

So These are some of the highly sought-after online certifications that validate data analysis skills through rigorous exams. Certifications endorsed by leading BI tool vendors like Google, Microsoft, Tableau and Oracle directly correlate to market demand. The IIA CAP credential is respected across industries for its vendor-neutral, advanced level of assessment. And the CAP in People Analytics addresses the fast emerging domain of talent/workforce analytics. Adding any of these credentials to their profile greatly enhances data analysts’ employability and career growth prospects in their field.


Scratch is one of the most popular and widely used coding tools for younger students and would be suitable for many middle school capstone projects. Developed by the Lifelong Kindergarten group at the MIT Media Lab, Scratch allows students to program by dragging and dropping blocks of code to create interactive stories, games, and animations. It uses a visual, block-based programming language that does not require students to know any text-based syntax. This makes it very accessible for beginners. Scratch’s online community is also very active and encourages sharing of projects, which could help students get feedback and ideas on their capstone work. The platform is freely available at scratch.mit.edu.

Another good option is App Lab from Code.org. App Lab allows students to code games, animations and more using a simple drag-and-drop interface very similar to Scratch, but is web-based rather than a downloaded application. It also integrates with Code.org’s larger suite of curriculum and courses, which teachers can leverage for lesson planning and project ideas aligned to state standards. Like Scratch, App Lab has a large online sharing community as well. An advantage it has over Scratch is the ability to more easily add features like sound, images and interaction with device hardware like the camera. This could allow students to create more robust apps and games for their capstone project.

For students looking to do more complex programming beyond drag-and-drop, another recommended tool is Microsoft MakeCode. MakeCode has editors for creating projects using JavaScript/TypeScript, as well as specialized versions for microcontrollers like micro:bit and Circuit Playground Express that allow physical computing projects. The JavaScript editor in particular could work well for a more advanced middle school capstone project, as it allows for coding things like websites, games and more using real code. Many of Code.org’s courses are also compatible with MakeCode which can provide structure and ideas. The community is also very active online to help students with challenges. MakeCode allows students to share and remix each other’s projects too.

If the capstone involves hardware projects, the physical computing versions of MakeCode like micro:bit and Circuit Playground Express are excellent choices. These allow students to code microcontrollers to control lights, motors, sensors and more using block and text-based languages. This could enable projects like data logging devices, robots, interactive art installations and more. Both include extensive libraries of sample projects and are designed to be very beginner friendly. They also have large learning communities online for help and inspiration.

Another good programmable hardware option is littleBits. littleBits are magnetic snap-together electronic blocks like buttons, LEDs, motors and sensors that connect together using the contact points. The blocks can then be programmed by dragging color-coded magnetic wires between power, input and output blocks. This allows hands-on physical computing and circuitry projects without needing to solder or know electronics. Kits include pre-made project examples as well as an online library of community projects. Since there is no screen, littleBits is best combined with another coding tool if an interactive program is desired. It opens up many options for physical computing and tinkering types of projects.

All of these recommended tools – Scratch, App Lab, Microsoft MakeCode, micro:bit, Circuit Playground Express and littleBits – are suitable options for engaging middle school students in coding and leveraging the constructionist learning approach of learning by making capstone projects. When selecting a tool, considerations should include students’ experience levels, the type of project being undertaken, availability of resources, and how well a tool aligns to curriculum standards. Teachers can also find additional tools that work well, these provide a solid starting point and have large user communities for additional support. The most suitable tool will depend on each unique situation, but these are excellent choices to explore for computer science learning through personally meaningful capstone work.


There are several programming languages and tools that would be well-suited for developing a cybersecurity vulnerability assessment tool. The key considerations when selecting languages and frameworks include flexibility, extensibility, security features, community support, and interoperability with other systems.

For the primary development language, Python would be an excellent choice. Python has become the de facto standard for security applications due to its extensive ecosystem of libraries, readability, and support for multiple paradigms. Major vulnerability scanning platforms like Nmap and Hydra are implemented in Python, demonstrating its viability for this type of tool. Some key Python libraries that could be leveraged include nmap, Django/Flask for the UI, SQLAlchemy for the database, xmltodict for parsing results, and matplotlib for visualizations.

JavaScript would also be a valid option, enabled by frameworks like Node.js. This could allow a richer front-end experience compared to Python, while still relying on Python in the backend for performance-critical tasks like scanning. Frameworks like Electron could package the application as a desktop program. The asynchronous nature of Node would help make long-running scanning operations more efficient.

For the main application framework, Django or Flask would be good choices in Python due to their maturity, security features like CSRF protection, and large ecosystem. These provide a solid MVC framework out of the box with tools for user auth, schema migration, and APIs. Alternatively, in JavaScript, frameworks like Express, Next.js and Nest could deliver responsive and secure frontend/backend capabilities.

In addition to the primary languages, other technologies could play supporting roles:

C/C++ – For performance-critical libraries like network packet crafting/parsing. libpcap, DNSEnum, Masscan were written in C.

Go – For high-performance network services within the application. Could offload intensive tasks from the primary lang.

SQL (e.g. PostgreSQL) – To store scanned data, configuration, rules, etc. in a database. Include robust models and migrator.

NoSQL (e.g. MongoDB) – May be useful for certain unstructured data like plugin results.

Docker – Critical for easily deployable, reproducible, and upgradeable application packages.

Kubernetes – To deploy containerized app at scale across multiple machines.

Prometheus – To collect and store metrics from scanner processes.

Grafana – For visualizing scanning metrics over time (performance, issues found, etc).

On the scanning side, the tool should incorporate existing open-source vulnerability scanning frameworks rather than building custom scanners due to the immense effort required. Frameworks like Nmap, OpenVAS, Nessus and Metasploit provide exhaustive libraries for discovery, banners, OS/service detection, vulnerability testing, and exploitation that have been extensively tested and hardened. The tool can securely invoke these frameworks over APIs or CLI and parse/normalize their output. It can also integrate commercial tools as paid add-ons.

Custom scanners may still be developed as plug-ins for techniques not covered by existing tools, like custom DAST crawlers, specialized configuration analyzers, or dynamic application analysis. The tool should support an extensible plugin architecture allowing third-parties to integrate new analysis modules over a standardized interface. Basic plugins could be developed in the core languages, with more intense ones like fuzzers in C/C++.

For the interface, a responsive SPA-style Web UI implemented in JavaScript with a REST API backend would provide the most flexible access. It enables a convenient GUI as well as programmatic use. The API design should follow best practices for security, documentation, and versioning. Authentication is crucial, using a mechanism like JSON Web Tokens enforced by the frontend framework. Authorization and activity logging must also be integrated. Regular security testing of the app is critical before deployment.

A combination of Python, JavaScript, C/C++, SQL/NoSQL would likely provide the best balance of capabilities for a full-featured, high-performance, secure and extensible vulnerability assessment tool. By leveraging maturity of established frameworks and libraries, the effort can focus on integration work rather than re-implementing common solutions. With a layered architecture, scalable deployment, and emphasis on testability and open architecture – such a tool could effectively and reliably assess security of a wide range of target environments.


One of the most common and reliable sources for obtaining corporate financial statements is directly from the company itself. Most public companies are required by law to file annual (10-K) and quarterly (10-Q) financial statements with the U.S. Securities and Exchange Commission (SEC). These disclosures contain detailed income statements, balance sheets, cash flow statements, footnotes, and other important information. Companies also typically make recent financial statements available on their investor relations website.

For public companies in the U.S., you can access EDGAR (Electronic Data Gathering, Analysis, and Retrieval system), the SEC’s electronic public database that contains registration statements, periodic reports, and other forms submitted by companies. On EDGAR, you can search for a company by its ticker symbol or CIK number to find and download its financial statements going back several years. This direct source from the SEC provides assurance that the financials have been reviewed and deemed acceptable by regulatory authorities.

Another valuable source for public company financials is commercially available databases like Compustat, provided by S&P Global Market Intelligence. Compustat contains financial metrics and statements for both U.S. and global public companies standardized into uniform accounts. The database goes back decades, allowing for trend and ratio analysis over long time periods. While not a direct SEC source, Compustat applies standardized adjustments and classifications to the raw data for easier comparison across firms.

For private companies, the availability and reliability of financial statements may vary significantly. Financials are often only provided to potential investors and not publicly disclosed. Sources to consider include: asking the company directly, checking business information providers like Dunn & Bradstreet, searching corporate filings if the company has ever gone public before, or tapping professional network contacts to see if anybody has access. State business registrations may also publish limited private company financial data.

Another option is to back into private company financials by compiling income statements estimated from industry ratios/benchmarks and filling in balance sheet accounts based on known operating metrics. This requires making assumptions but can at least provide a starting point when actual statements are not available. Consulting private company databases like PitchBook or Closely may also turn up some useful historical financial snapshots.

For foreign public companies, their local stock exchange websites often house recent annual reports containing home-country GAAP financial statements along with English translations. Other country-specific sources include commercial registries, regulator filing repositories, and local databases analogous to EDGAR or Compustat. Language barriers may be an issue, so using translation tools and searching in the company’s native language can help uncover more information.

Industry trade associations are another worthwhile resource as they may publish aggregate financial benchmarks and data useful for analyzing trends within a given sector. Speaking with investment banks that specialize in M&A advisory within an industry can also potentially connect you with private company client financials. And valuation industry participants sometimes sharestatement sanitized private transaction comps among each other for comparative modeling purposes.

Secondary sources offering company overviews and research reports may round out your diligence. Providers like FactSet, Bloomberg, Morningstar, and Capital IQ summarize key financial metrics. Reading sell-side analyst initiation reports can provide insights as the analysts have scrutinized full financials as part of their due diligence. And valuation service firms like Houlihan Lokey publish quarterly and annual research on public comparable company trading multiples bankers use for valuation benchmarks.

Gaining access to high quality financial statement information, especially for private companies, may require tapping multiple sources and creative problem-solving given availability limitations. But thorough financial analysis grounded in reliable statements remains essential for conducting accurate company valuation work. Let me know if any part of the process would benefit from additional details or examples.