Trending February 2024 # Big Data For The Midmarket: Value In Six Areas # Suggested March 2024 # Top 10 Popular

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Also see: Choosing a Big Data Solution: Seven Steps

Big returns are already being achieved by mid-market companies that have deployed Big Data projects. The Big Data package – heavy-duty storage, processors and analytics tools combing through huge volumes of varied data types in real time or near real time – has moved beyond early adopter phase. It’s quickly becoming a mainstream revenue and profit generator, according to a new survey and report project I participated in.

An important insight for IT professionals is the widespread use of Big Data among organizations. The conventional wisdom says that marketing is the only department that has taken the Big Data deep dive, but our survey and others are finding important applications in customer service, operations, supply chain management, financial analysis and human resources. As you can see in the following table, marketing systems are expected to be the sixth most important source of internal data for Big Data programs over the next few years:

Darin Bartik, Dell’s executive director of information management products, explains that while marketing and social media have become synonymous with Big Data in large companies, mid-market companies find Big Data opportunities in other areas since they may not have large social media activities underway. “Manufacturing firms have a tremendous opportunity to analyze sensor data and other manufacturing operations,” he says. “Supply chain data, such as partner behavior, is one example. There are many different aspects beyond sales and marketing.”

However, IT pros may want to think twice before approaching the CFO or the COO with a Big Data project for the finance or operations departments. Successful Big Data projects are typically based on a collaboration between the business units and the IT department, where the line of business takes the lead. Bartik and others warn that, like other big and important IT initiatives, a Big Data project needs executive sponsorship first and foremost.

“Senior executives in an organization know their big operational challenges, such as global competition or a tough sales environment, and should tackle those problems first,” Bartik says. Since solving those problems will be most likely to attract top executive interest, they are the most likely to be funded and supported. And once those Big Data solutions are successful they will naturally lead to addressing other corporate pain points.

While we all know that executive sponsorship is always a pre-requisite for a large successful IT project, other ingredients are quite important for Big Data success, too. As you can see from this list of Big Data challenges confronted by the early adopters, there is no one major sink hole, but lots of potholes. Here’s the top 10 list:

Survey respondents also noted a wide variety of explanations for Big Data project failures. Note that many of them are either not on the top 10 challenge list, or have widely different rankings:

The paucity of data scientists and others who are skilled in statistical analysis, business and IT issues is well known, but is especially acute for the mid market. “These are tough people to find,” notes Bartik. “I often recommend that companies create a data science team that is not located in one line of business or isolated in IT. Take your best and brightest from the technical, analytical and business areas and create a separate team. They need to exercise their Big Data muscles first.”

Closely related to the paucity of Big Data skills is the surplus of gut feel. Many Big Data projects fail because there isn’t a data-driven decision culture. Too many senior executives will insist that their prior experience trumps analytics based insights. While these cultural issues are way outside of the IT professionals’ control, they can capsize even the most well-funded and well-executed Big Data project.

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Top Tips For Citizen Data Scientists To Become Experts In Big Data

Here’s how citizen data scientists can become well versed in big data

With data scientists regularly topping the charts as one of the most in-demand roles globally, many organizations are increasingly turning to non-traditional employees to help make sense of their most valuable asset: data. These so-called citizen data scientists, typically self-taught specialists in any given field with a penchant for analysis, are likewise becoming champions for important projects with business-defining impact. They’re often leading the charge when it comes to the global adoption of machine learning (ML) and artificial intelligence (AI), for example, and can arm senior leaders with the intelligence needed to navigate business disruption. Chances are you’ve seen several articles from industry luminaries and analysts talking about how important these roles are for the future. But seemingly every opinion piece overlooks the most crucial challenge facing citizen data scientists today: collecting better data. The most pressing concern is not about tooling or using R or Python2 but, instead, something more foundational. By neglecting to address data collection and preparation, many citizen data scientists do not have the most basic building blocks needed to accomplish their goals. And without better data, it becomes much more challenging to turn potentially great ideas into tangible business outcomes in a simple, repeatable, and cost-efficient way. When it comes to how machine learning models are operationalized (or not), otherwise known as the path to deployment, we see the same three patterns crop up repeatedly. Often, success is determined by the quality of the data collected and how difficult it is to set up and maintain these models. The first category occurs in data-savvy companies where the business identifies a machine learning requirement. A team of engineers and data scientists is assembled to get started, and these teams spend extraordinary amounts of time building data pipelines, creating training data sets, moving and transforming data, building models, and eventually deploying the model into production. This process typically takes six to 12 months. It is expensive to operationalize, fragile to maintain, and difficult to evolve. The second category is where a citizen data scientist creates a prototype ML model. This model is often the result of a moment of inspiration, insight, or even an intuitive hunch. The model shows some encouraging results, and it is proposed to the business. The problem is that to get this prototype model into production requires all the painful steps highlighted in the first category. Unless the model shows something extraordinary, it is put on a backlog and is rarely seen again. The last, and perhaps the most demoralizing category of all, are those ideas that never even get explored because of roadblocks that make it difficult, if not impossible, to operationalize. This category has all sorts of nuances, some of which are not at all obvious. For example, consider the data scientist who wants features in their model that reflect certain behaviors of visitors on their website or mobile application. But of course, IT has other priorities, so unless the citizen data scientist can persuade the IT department that their project should rise to the top of their list, it’s not uncommon for such projects to face months of delays — assuming IT is willing to make the change in the first place. With that in mind, technology that lowers the bar for experimentation increases accessibility (with appropriate guardrails) and ultimately, democratizes data science is worth consideration. And companies should do everything they can to remove roadblocks that prevent data scientists from creating data models in a time-efficient and scalable way, including adopting CDPs to streamline data collection and storage. But it’s up to the chief information officers and those tasked with implementing CDPs to ensure that the technology meets expectations. Otherwise, data scientists (citizen or otherwise) may continue to lack the building blocks they need to be effective. First and foremost, in these considerations, data collection needs to be automated and tagless. Because understanding visitor behaviors via tagging is effectively coding in disguise. Citizen data scientist experimentation is severely hampered when IT has to get involved in code changes to data layers. And while IT can and should be involved from a governance perspective, the key is that citizens data scientists must have automated collection systems in place that are both flexible and scalable. Second, identity is the glue in which data scientists can piece together disparate information streams for organizations to find true value. Thankfully, organizations have a myriad of identifiers about their customers to reference, including email addresses, usernames, and account numbers. And identity graphs can help organizations create order from the chaos so that it becomes possible to identify visitors in real-time, making these features essential for analyzing user behavior across devices.

How To Use Data To Prove The Value Of Seo

It’s no secret: SEO is not a flavor of the month. Some people, however, still doubt its effectiveness.

The cost of SEO has grown exponentially. The cost of content production along with increasing expectations from the audience and fierce competition require larger budgets each year.

Then there’s the ROI of SEO, which can be hard to establish. That’s especially true today, when organic traffic has merged with content marketing and many other disciplines, making it much harder to distinguish its impact on the organization.

And of course, if done incorrectly, SEO can also cause some serious damage to a business or even destroy it completely.

The result of such perception is a slow shift towards PPC (even also among many SEOs). Its ROI is (supposedly) easier to establish and the results are instant. Not to mention that the risk to business is none while job prospects keep growing.

But of course as SEOs, you and I know better. We know how important our role is in the company’s marketing chúng tôi challenge lies in convincing the non-believers to see it that way.

Here are some ideas how you can prove the value SEO brings to your organisation.

Set the Context

First of all, it’s important for companies to understand what’s already happening with SEO across all industries. Data on the effectiveness of organic search performance across industries can help you explain what’s involved and show how SEO can help achieving overall company objectives. Companies like Gartner, eMarketer, and others can provide you with data and statistics you need for this.

Define Point of Reference

The second step is to illustrate where your company is today and where it was (if possible) before engaging with an SEO, regardless of whether it was you or your predecessor. Setting up those benchmarks will help you to illustrate the impact SEO has had on the company and make the case for why it’s an important part of the marketing mix.

Show Opportunities

Lastly, you need to show what else your company can achieve with SEO. This will help you present the road ahead but also set goals to report on. Unlike a common perception, SEO plays a number of roles in the company, including:

Generates new business opportunities

Wins attention of the target audience in different stages of the buying cycle

Raises awareness of your brand

Helps to build relationships with prospects and customers

Expands on your reach

Builds up your brand and authority

5 Metrics that Prove Your SEO is Working

The above points are just the groundwork you need to set before you can start to regularly present the value you add to the company. Below is a list of elements you should include in your overall reporting practice.

1. Define Goals

You probably know exactly what you are trying to achieve. Others, however, might be oblivious or have completely different expectations. First and foremost then you should define the goals you are working towards. After all, there is so much you can achieve with SEO, so you need to pin down exactly which elements you want to be responsible for.

2. Track CPL (Cost per Lead)

3. Add & Measure Monetary Value On Every Customer Touch Point

By its nature, SEO affects many channels. Customers find your site and call in to your office or grab the phone and dial your number. They send emails or inquire through other channels — and that doesn’t even include work of mouth references. Place a value on each of these touch points. Naturally, such research can never be 100% accurate. But even approximate values can highlight the benefit of your channel to the business.

4. Measure Assisted Conversions

Even with online conversions, what ended up as a paid channel sale might have started through an organic listing. Therefore, you need to measure the impact of SEO on assisted conversions. Luckily that’s easy to do in  Analytics.

5. Run Correlation Tests

There are many theories you test in your work, from the effect of a simple meta data change may have on page rankings to more complex ones. But proving that all this work is making a difference can be more problematic. Simple correlation tests can help you visualize and show beyond a doubt that your theories work.

Conclusion

Organizations become skeptical about SEO. To some it seems to offer no ROI and  tangible results. Some start to consider PPC as a much viable option and thus diminish the influence SEO have on the organization. To change that you need to prove the value your work brings to the organization through proper reporting and data.

Have you had success proving the value of SEO to your clients? What have you discovered works well?

Image credit:

Anne / Creative Commons License

Ken Teegardin / Creative Commons License

Unlock The Value Of Google Trends Data With Web Scraping

Google Trends is an excellent data source for e-commerce businesses and website owners. It enables businesses to 

Identify trends in their industry. 

Monitor their competitors. 

Optimize SEO and content marketing strategy. 

Identify their niche market.

Understand customers’ behaviors and expectations.

Aside from the benefits of Google Trends data, extracting data manually is time-consuming. Web scraping enables e-commerce businesses to automatically collect Google Trends data in a structured format.

In this guide, we will explain how web scraping bots extract data from Google Trends step by step. In addition, we will provide you with the top 5 web scraping use cases in Google Trends, along with tips and business outcomes.

What is Google Trends data?

Google Trends is a free analytics tool displaying trending searches and keyword popularity. It provides anonymized and categorized datasets to make data easier to understand and use. It allows users to access real-time (data from the last 7 days) and non-real time (data from 2004 to the last 72 hours).

Is it legal to scrape Google Trends?

It is legal to scrape publicly available web data. However, data protection regulations such as GDPR and CCPA make it illegal to scrape personally identifiable information (PII). Scraping publicly available Google data is not illegal unless your scraping activities do not harm the website or you use scraped data for any harmful purpose.

How to scrape Google Trends data?

Identify your search term or topic. 

Select your geographical location (see Figure 1). 

Figure 1: Country-Region dropdown menu in Google Trends

Search for the term or topic you identified (see Figure 2). 

Figure 2: Two search functions of Google Trends for the same search term

Google Trends will provide a chart displaying interest in your specific term over the past 12 months. The highest point on the chart represents the peak popularity for the web scraping search (see Figure 3). 

Figure 3: Search interest for web scraping over the given time.

Enter the exact search term into your Google Trends scraper. 

Select the time range for the keyword (i.e., past 12 months). 

Choose your geographical location. 

Run the scraper to get the dataset from Google Trends 

Download the scraped data in the format of your choice.

Sponsored

Bright Data’s Google Trends Scraper API automatically collects public data from Google Trends. It scrapes Google Trends data, such as 

Search terms and topics,

Latest stories & insights,

Recently trending.

5 ways to make the most of Google Trends data using web scraping 1. Target Competitive Keywords

You can use Google Trends to learn about the search volume for your target keywords and the most popular related topics. It enables users to conduct multiple searches simultaneously (see Figure 4). You can compare a couple of search terms based on their search interest at a given time and location.  

In the following example, Google Trends displays the search interest for the terms “proxy server” and “VPN” in the United States over the last 12 months. There is no clear downward or upward for these keywords over the past 12 months. However, it does not show the exact amount of traffic. If you observe a downward trend in your target keyword, you can focus on keywords that are trending upward.

Figure 4: Comparison of two keywords’ popularity.

2. An easier way to conduct niche market research 3. Understand market trends on Google Shopping

Google Shopping allows customers to browse products from various sellers who featured their products on it (see Figure 6). 85% of all product searches are conducted on Amazon and Google. It is a great source for retailers to analyze competitors. Google Trends enables businesses to understand which products are trending up or down on Google Shopping at any given time and location.  

Figure 6:  Google service that enables customers to search for products on online.

When we search for wireless headphones on Google Shopping, Google Trends shows us wireless headphones interest in the United States over the last 12 months ( see Figure 7). If you scroll down the page, you can see in which locations the wireless headphones term is most popular. 

A quick recommendation: When you enter a term or topic into the search bar, you will see wireless headphones (search term) and wireless headphones (topic) appear in the dropdown menu. The search term only focuses on the actual search term and provides more focused results, whereas the topic covers related queries and terms and provides more comprehensive results. 

Figure 7: Example of Google Trends data concerning wireless headphones.

Assume you have added a product to Google Shopping, say a computer mouse, and want to analyze customer preferences and industry trends. I searched for a computer mouse on Google Trends to see dominant keywords in the industry and which products have the most traffic. You can see the rising keywords related to your search term in the related queries section. Glass mouse skate is the most popular search term for a computer mouse on Google Shopping (see Figure 8). Search terms labeled with breakout mean it grew by more than 5000%.

Figure 8: Example of related queries concerning computer mouse.

It provides information about customer preferences and needs. We can see that customers are interested in ergonomic mouse and wireless mouse (see Figure 9). You can see which areas in the United States are most interested in the computer mouse term .

Figure 9: Example of top rising keywords concerning computer mouse.

 4. Use your competitive keywords on YouTube to reach target customers 

Youtube is the 2nd most visited website in the world behind Google for September 2023. Youtube marketing helps businesses inform their potential customers about their products and services. A well-targeted Youtube content will empower your target audience in the purchase decision process. You can use Google Trends to understand the most popular topics in your industry and what searches your target audience makes. 

Assume you provide web scraping services and want to create video content that answers the questions of your target audience. Here’s a step-by-step guide to using scraped Google Trends data for YouTube marketing:

Search for the web scraping term and select “web scraping – search term” to get the most focused results (see Figure 10). 

Figure 10: Google Trends search results for the query “web scraping.”

Choose geolocation and a time range. 

Change the search property, switch from the web search to YouTube search. 

Check the top and rising queries to see what people are looking for with the keyword you entered. Top keywords show the most commonly searched terms in the United States when using web scraping. Rising searches are terms that have the most significant growth within the given period. Your competitive keywords are what is web scraping, web scraping with r, selenium web scraping, etc. (see Figure 11).

Figure 11:  Rising queries for web scraping term.

5. Monitor your competitors’ popularity 

Determine the leading brands in your industry: Let’s look at the competition between Netflix, HBO, and Hulu.

Determine the target locations: Let’s go with the United States. We want to see the search interest for the terms we entered in the given region.

Choose a time frame: In our case, we’d like to see the search interest over the past five years.

We can see that HBO Max and Hulu are performing almost at the same level after 2023. Netflix has clear upward trends at certain times. We can also see that it has been stable since 2023 (see Figure 12). 

Figure 12: Comparison of the popularity of multiple brands in the same industry.

How to Get Google Trends comparison data

Select the time range. 

Choose geo-location. 

Enter each search term that you would like to compare. 

Run the scraper to get the required data from Google Trends. 

You can save scraped data in the desired format, including CSV, Excel, and JSON. 

You can schedule scraping time. For instance, if you want to extract data regularly, say at the beginning of each month, set the web scraper to extract data on a monthly basis. The bot will run at the time you specify.

Further Reading

If you want to learn more about web scraping and how it can benefit your business, feel free to read our articles on the topic:

Also, feel free to check our data-driven list of web scrapers

For a comprehensive view of web scraping, how it works, use cases, and tools, feel free to download our in-depth whitepaper on the topic:

If you have any further questions about how to get data from Google Trends, you can reach us:

Gulbahar Karatas

Gülbahar is an AIMultiple industry analyst focused on web data collections and applications of web data.

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Big Data Is The Driver Of The Cannabis Industry

We live in a time where information has turned into an incredible driver for both development and change. Generation of data decides the idea of new framework, businesses, the ascent of restraining infrastructures and the development of economics. In late years, innovation and big data have turned out to be a basic requirement to business achievement, and the cannabis business is no exemption. Machine learning, Artificial Intelligence (AI), databases, and predictive analytics are majorly affecting cannabusinesses, and additionally their financial investors, consumers, and buyers. Cannabiz Media sees that affect directly through the development of the Cannabiz Media License Database. Using modern algorithms and new innovations in data accumulation technology, programming is currently ready to help marijuana businesses follow regulations, meet requests, anticipate patterns, amplify deals, and enhance the viability of medicinal weed. Since cannabis is as yet considered a schedule 1 sedate by the national government, leading clinical research into its pharmacology is a noteworthy challenge. This implies the developing cannabis market is deficient with regards to the clinical information required that will enable cannabis enterprises to grow new and better items. However, Worldwide Cannabis Applications Corp (GCAC) plans to change that. Citizen Green technology by GCAC harnesses the power of artificial intelligence and blockchain to assemble clinical information straight from customers, mainly streamlining the procedure that hinders cannabis product development. Basically, Citizen Green appreciates individuals who finish reviews with a digital money (cryptocurrency) they can use toward products from worldwide medical marijuana/weed programs. Yet, that is not all. By reconfiguring the survey information into a clinical standard and integrating it with real study data, GCAC reports that its Citizen Green innovation gives enhanced patient results and enables researchers to distinguish qualified members for clinical investigations. This eventually accelerates the approval procedure for new medicinal cannabis products. Kathleen Burke of MarketWatch believes that big data and technology are everything in growing a plant-based industry. To her, it is the genuine driver of development, crediting more value to it than compost. Data is completely crucial and aides in responsibility, deciding target markets, making key estimations and the creation of informed and guided choices. Content ought to be enhanced by owners and partners given the substantial volume of data emerging out of every task in the cannabis business. Over the supply chain, we discover small and private enterprises are progressively utilizing data to make their tasks more proficient while creating more salary en-route. Being precise with information gives new insights and open doors for organizations. Cannabis Media featured this thought which trusts that big data as databases, forecasts, and even artificial intelligence that could help in deciding the direction and impacts of the weed business in the current monetary atmosphere. Insights got from enormous information could possibly be utilized to find out about current patterns, the most recent customer requests, new regulations set locally as well as everywhere throughout the world, and additionally courses on the best way to boost benefits. The distribution procedure for cannabis products varies between states, and this is additionally entangled by extra administrative and security concerns. Nonetheless, with regards to getting the products to the customer or patient, innovation and big data are demonstrating their value. Web and mobile applications created by organizations like Eaze, Meadow, and GreenRush enable buyers to pick their cannabis products and have them conveyed right to their doors. It may appear that big data and cannabis conveyance are remarkable partners, however, the fact of the matter is the polar opposite. Eaze can catch customer data pertained to the client area, time spent thinking about a product, buys, and that’s just the beginning. For instance, by breaking down this information and coupling it with machine learning, predictive analytics, and artificial intelligence, Eaze is capable of putting the information into a usable configuration, enabling organizations to acquire a better profit for their marketing efforts by focusing on purchasers explicit product messages, grow new items, make unique offers, and the sky’s the limit from there. Basically, technology gives the business a superior by and large comprehension of the customer, as well as how the customer utilizes their items.

Top 28 Cheat Sheets For Machine Learning, Data Science, Probability, Sql & Big Data

Overview

Data Science is constantly evolving with new tools, frameworks and technologies

Each tool/technique has its own unique use case along with features and functions

Refer to this exhaustive list of cheat sheets concerning popular Data Science concepts

Introduction

Data Science is an ever-growing field, there are numerous tools & techniques to remember. It is not possible for anyone to remember all the functions, operations and formulas of each concept. That’s why we have cheat sheets. But there are a plethora of cheat sheets available out there, choosing the right cheat sheet is a tough task. So, I decided to write this article.

Here I have selected the cheat sheets on the following criteria: comprehensiveness, clarity, and content.

After applying these filters, I have collated some 28 cheat sheets on machine learning, data science, probability, SQL and Big Data. For your convenience, I have segregated the cheat sheets separately for each of the above topics. There are cheat sheets on tools & techniques, various libraries & languages.

Read on to know which cheat sheet to use for a particular topic.

Python for Data Science Cheat Sheets

If you are starting to learn Python, then this cheat sheet is the best resource for you. In this cheat sheet, you will find a step-by-step guide to learn Python. It gives out resources to follow, Python libraries you must know and few helpful tips.

This cheat sheet by Datacamp covers all the basics of Python required for data science. If you have just started working on Python then keep this as a quick reference. Mug up these cheat codes for variables & data types functions, string operation, type conversion, lists & commonly used NumPy operations. The unique aspect of this cheat sheet is it lists down important Python libraries & gives cheat codes for selecting & importing these libraries.

NumPy is a core library for scientific computing in Python. In this cheat sheet from DataCamp you will find cheat codes for creating NumPy arrays, performing mathematics operation on array, subsetting, slicing, indexing & array manipulation. The unique aspect of this cheat sheet is it gives each function has been categorized & explained in simple English.

Your best resource to perform data exploration in Python using NumPy, Pandas & Matplotlib. With this cheat sheet you will learn how to load files in python, convert variables, sort data, create plots, create sample datasets, treat missing values & many more. It is one of the simplified cheat sheet on data exploration.

Pandas is one of the important libraries in Python. This cheat sheet on data exploration operation in Python using Pandas is your go-to resource to know each step involved in data exploration. You will find cheat codes for reading & writing data, preview of dataframes, rename columns of dataframe, aggregate the data, etc.

Be it a data scientist or a non-techie, visualization is easily interpreted by both. In visual graphs & plots, data comes to life & speaks for itself. In this cheat sheet, learn how to perform data visualization in Python. Explore the different ways in which you can plot your data. Find step by step approach to plot histograms, bar charts, line graph, scatter plot, etc.

This cheat sheet on Bokeh, an interactive visualization library in Python is especially useful with large datasets. In this cheat sheet by DataCamp, you will get basic steps for plotting, renderers & visual customization, save plots & create statistical charts.

Here is a cheat sheet on scikit-learn for each technique in Python. It provides different functions used for pre-processing, regression, classification, clustering, dimensionality reduction, model selection & metric along with their description. The unique aspect of this cheat sheet is it depicts the complete stages of machine learning.

Text cleaning can be a cumbersome process. And knowing the right procedures is the key to getting the desired result. Refer this cheat sheet to perform text data cleaning in Python step by step. Follow this cheat sheet to know when you remove stop words, punctuation, expressions, etc. The unique aspect of this cheat sheet is each step has been explained with codes & examples.

R for Data Science Cheat Sheets

Use this reference sheet for cheats codes for all functions & operators under R. Understand what the different terms mean under R. It explains all the functions under data creation, data processing, data manipulation, model function, selection and many more.

Learn how to import data with readr, tibble and tidyr. Find functions to write & read functions in tibble. It also provides you useful arguments, reshape data, combine cells with tidyr.

This cheat sheet from RStudio is a reference material for data transformation with dplyr. Get short codes & operators for all operations under data transformation. Then be it summarize cases, group case, manipulation, vectorize & combine variables.

This cheat sheet gives a step by step guide to  data exploration in R. Learn how to load file in R, convert variables to different data types, transpose a dataset, sort dataframe, create plots & many more.

Above we saw cheat sheet on data visualization in Python. Here is a data visualization cheat sheet to give the different graphs by which you can plot the data. With a few lines of code, you can create beautiful charts and data stories. R has awesome libraries to create basic and more evolved visualizations like Bar Chart, Histogram, Scatter Plot, Map visualization, Mosaic Plot and various others.

This cheat sheet is specifically for creating a visualization in R using ggplot2. ggplot2 works on the grammar of graphics and is built on a set of visual marks that represent data point. Get cheat codes to create one variable & two variable graphical component. Along with different techniques for creating plots in R.

Caret package provides a set of functions that streamlines the process of creating predictive models. The cheat sheet includes functions for data splitting, pre-processing, feature selection, model tuning & visualization.

This cheat sheet provides functions for text mining, outlier detection, clustering, classification, social network analysis, big data, parallel computing using R. This cheat sheet gives you all the functions & operators used for data mining in R.

Cloud computing has made it very easy for us to access our files & data from anywhere. In this cheat sheet, you will learn about how to use cloud computing in R. Follow this step by step guide to use R programming on AWS.

Machine Learning Cheat Sheets

In this cheat sheet, you will get codes in Python & R for various commonly used machine learning algorithms. The algorithms included are Linear regression, logistics regression, decision tree, SVM, Naive Bayes, KNN, K-means, random forest & few others.

This cheat sheet is provided from the official makers of scikit-learn. Many people face the problem of choosing a particular machine learning algorithm for different data types & problems. With the help of this cheat sheet, you have the complete flow for solving a machine learning problem.

This cheat sheet helps you choose the best Azure Machine Learning Studio algorithm for your predictive analytics solution. Developed by Microsoft Azure team itself cheat sheet gives you a clear path as per the nature of the data.

Probability Cheat Sheets

Refer this cheat sheet for a quick overview on Poisson Distribution, Normal distribution, Binomial Distribution, Geometric Distribution and many more. It gives notation, formulas & a brief explanation in simple English for each distribution.

SQL & MySQL Cheat Sheets

In this cheat sheet, learn how to perform basic operations in SQL. Get function for inserting data, update data, deleting data, grouping data, order data, etc. If you have started using SQL this the best reference guide.

In this cheat sheet, you will find commonly used MySQL & SQL commands. Get cheat codes for MySQL mathematical function, MySQL string function, basic MySQL commands. You will also find SQL commands for modifying & querying.

Big Data Cheat Sheets

It is rightly said Hadoop has a vast ecosystem & includes various operations. Learn about the various operators, how they work & what operation they are responsible for. The cheat sheet has been broken down into a respective general function like distributed systems, processing data, getting data in/out & administration.

Here is a cheat sheet for Apache Spark for various operations like transformation, actions, persistence methods, additional transformation & actions, extended RDD, streaming transformation, RDD persistence, etc.

In this cheat sheet, get commands for Hive functions. It provides cheat codes for data functions, mathematical function, string function, collection function, built-in aggregate function, built-in table generating function, conditional function and functions for text analytics.

End Notes

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