نوشته های sandra

Spring Summer Collection 2020 - Best Summer Outfithttps://files.virgool.io/upload/users/47214/posts/mqpqsdyr1w5q/oz6dttmizz5q.jpegs for Women

sandra — Tue, 10 Mar 2020 11:48:43 +0330

Fashion trends change swiftly, and it's essential that we keep up with the latest trends. Season drives fashion to change. Now it's spring and afterwards, summer. Are you looking for a wardrobe upgrade? Then this season paves the way to it.Multicoloured White TunicThis striped tunic is made unique with contrasting cloth materials to make you stand out from the crowd! The full sleeves and front buttons make it something that you really feel like trying out. Pair it with jeans that you often wear or skinny leggings to complete the ensemble.Full Sleeves Printed ShirtFloral print never goes out of style. So that gives you enough reason to choose a printed shirt in full sleeves. Floral shirt is an assured thing that finds its way into your wardrobe. This white shirt comes out with full-cuff sleeves, buttons and a blueprint in the middle, and while wearing it, you will find that it's much loose-fitting and snug.Belted Shirt DressLight green dress features a waist belt, half-sleeves and front buttons to make it the first pick of anyone! Pair it with casual shoes, and you're done. You can wear it while hanging out with your friends or wander in style during a trip to your favourite location or even waiting for your flight!Full Sleeves Long TunicAre you a tunic lover? Lay your hands on this super comfy tunic and much loose-fitting. What makes this tunic beautiful is its rounded neck, full-sleeves and side pockets. By choosing this tunic top, you have something that makes your wardrobe up-to-the-minute to make you style inspiration.Most Video Production Companies choose Summer winter collections for their Models.Wide Belted trouserThis trouser is defined by its wide-leg cut and waist belt, which stands just above the ankle. This trouser is obviously a standout material, make it formal wear, or for a night out, the trouser is sure to draw attention. Pair it with a t-shirt for an informal outing or pair it with a blazer for an official look.Sleeveless Mint Maxi DressGet ready to transition your wardrobe with this chic maxi dress, sleeveless and with a round collar. This maxi dress shouts out simplicity at the peak. Stand high in matching heels or a matching fur jacket in the chilly season. With an attractive shade of white and blue intermingling with each other, tie your hair up in a bun, and you're done!Belted Sleeveless OverallThis top is sure to bring you praises, featuring an overall, sleeveless and a waist belt, and buttons that you can throw open or choose not to. Throw your hair open, and you're all set for a beach party! Even pair it with a stole and be a star among colleagues!Half Sleeves White DressLet them discover the fashion icon in you. But how? Yes, there is obviously away! Choose this pure white dress with half-sleeves and comes with a round neck to win your beholder's heart. And this doesn't end here! A ruffle detail in the middle is the highlight of this dress. Be that fashion inspiration among your loved ones wearing this perfect dress!You can see these modes of dresses in many fashion shows. Even many Explainer Video Production companies try these outfits for their models to get the original look.Full Sleeves Shift DressThis dress stands out with its colour, and ruff high neck, wearing it you will definitely turn heads. Notice the sleeves- call it bishop sleeves! This outfit will be perfect for a quick makeover, paired it up with accessory like a stiletto.With these outfits, get ready to jazz-up and be that fashion statement. Replace those clothing that you're tired of wearing with these astonishing clothes to grab anyone's attention. The spring-summer 2020 is the best time for it, and you will love wearing it again and again!

How Web scraping and Big Data Analytics can be used to impact the Media and Entertainment industry

sandra — Wed, 25 Sep 2019 11:36:16 +0330

Every industry in the world is moving towards data-driven decision making, then one of the most popular and highest grossing industries definitely has employed analytics and data sciences in its operations. We are talking about the media and entertainment (M&E) industry. If you are associated with the industry in any way and your question is how to use analytics to maximize the potential in this industry across all domains, you are at the right place!The increased technological and digital revolutions have caused a data-enabled revolution within the video and media production companies. You can see a perfect convergence of digital and analytical solutions in this industry.Before we move on to how analytics can be employed in the media industry, let us understand its needs and priorities. The trends of media and entertainment industry are primarily driven by the changing needs of the digitally savvy audiences and their preferences. Most of us now want to use entertainment means not only to recreate and relax but also to stay on top of advancements, world events and our respective interest areas- all this while on the move!Our fast-paced lives have pushed the entertainment industry to alter their content and the war of delivering it to us rapidly. While the primary intent of the industry lies in developing the best content for the audience, the Video Production Company also tries to diversify into various channels for broadcasting information and entertainment content. Analytics can in fact steer media and entertainment towards burgeoning growth rates.How can data analytics impact Video Production Company and Entertainment Sector?If we just look at some facts and numbers, we can see a staggering amount of data from social media and online media being pulled and processed. As of 2017, Facebook is reported to collect and process over 500 TB of data on a daily basis. The search business market leader, Google handles 3.5 Billion requests every day. Amazon is another member of the big data league that receives around 152 Million customers’ purchase data daily. Having said that, big data is definitely an essential tool for the entertainment and media industry.There are three main areas where big data has the potential to disrupt the status quo and stimulate economic growth within the media and entertainment sectors:Products and Services: The industry can use analytics to design content. You can derive quantitative insights about the sentiment of the audience by analyzing large and heterogeneous datasets. You can also use analytics to analyze the content the competitors are running and use the insights to tweak your own content. That sounds a useful asset to have for news channels, is it not?Customers and Suppliers: If you are associated with ambitious video production companies, you know how important it is to know about the preferences, profiles and attitudes of the customer. This not only enables stronger relationships but also higher loyalty. Data and analytics can help you do that too!Infrastructure and Process: Data-driven models often help companies automate and improve the efficiency of certain processes. Furthermore, by establishing pipelines to pull and process various kinds of datasets, media production companies can do advanced analysis and gain an edge over the rest of the market.Some critical applications of data analytics in the media and entertainment industry are:Data journalism– This application uses analytics to derive insight, discover exciting stories, and generate excellent content. By enhancing the value of these insights, you can improve the quality of journalism and thus, enhance the brand value.Social Media Analysis– Using batch and real-time analysis of social media data like tweets, images, comments and status updates to identify trends and content that can be fed as an input for creating services for the clients.Cross-sell of products– Applications like recommendation engines of content using collaborative filtering, content-based filtering, and hybrids of both approaches can create additional points for a media platform. Everyone here knows about Netflix, don’t we?How do we obtain this data?Most of the data being spoken about in the analysis above is customer-centric or can be pulled from various media platforms. All this can be done using one tool- Web Data Scraping! Firms like Datahut can scrape data from multiple sources and store them in a structured file format. Social Media crawling, social listening, data scraping and web aggregators are not just jargons thrown around in the technical data-driven community. These are some of the tools and techniques used to scrape data from media platforms and websites.To make it further accessible for you, we can assure that learning the basics of data gathering and analytics is not difficult! You have a lot of resources that can help you understand the technological aspects. For faster and efficient results, we at Datahut can offer you scalable solutions and services to convert all unstructured web data into structured data files.A lot of this data, like Google search trends, is also available to the public for analyses and research. This data can either be pulled using APIs (application program interfaces) or be extracted using methods like web scraping.How has the media industry used data analytics?Online media platforms like Netflix rely on data analytics and data-driven decision-making tools for almost all their operations. One of the most popular use-cases is where Netflix monitors reviews, trends and customer sentiments to gain an edge over the competitors. They have used this data in the past to gain complete partnership rights for a leading political drama show.The online content platform has been able to crunch the vast amount of viewership data to study their viewers. Using this data gives them an in-depth and fine-grained analysis of viewers’ habits over millions of viewings of shows. Some fundamental insights that Netflix has obtained are the attributes or qualities that cause a particular show to be popular or how long viewers had viewed similar programs, through seasons and individual shows.Warner Bros. had partnered with Accenture’s Datamart team, Aprimo to use software applications along with sales data in order to obtain quick access to actionable, accurate reports to support spend decisions, accumulate knowledge and experience. These applications can also help them apply actionable insights to refine subsequent movie marketing campaigns with past ones, thus improving invoice collection efficiencies.In the music industry, a lot of artists and production houses use data on the audience’s listening preferences and sequences to design albums and market their creations effectively. Music platforms can also use data to generate insights on how to optimize the playlist for the maximum impact at live events.What can Datahut do for you?Datahut has partnered with a lot of companies in the media industry to scrape data and help them with real-world business problems. We helped a customer gather data from forums that contain review and discussions about leading shows like Quantico. This data is then used to analyze the viewers’ sentiments and get a better understanding of peoples’ perception of the shows. This, as mentioned above is a vital tool for not only designing content but also for strategic marketing decisions like what shows and production houses to partner with, what shows to host and even what suggestions to give the viewer for his next or alternative watch.If you are a content creator, such data can also be used directly by you. Imagine being a music creator and having the insights on what your targeted audience appreciated and did not like about your last album. It’ll certainly help you target certain strata of customers for your content and invest more resources to popularize the music among those listeners?One significant observation that Datahut has had while working with our customers in the media and lifestyle sector was that the scope of data and analytics is not only limited to the conventional content-hosting platforms or artists. One of our customers wanted to use data and analytics to monitor fashion news websites and blogs to learn about upcoming trends, market preferences, and customer needs. Some particular keywords and phrases for instance, “denim jackets” or “cold-shoulder”, if gaining popularity would be of great interest to the customer.However, here we will have to pull data and the associated insights immediately before the virality of the keyword reduces. Since the fashion industry, just like the media industry is highly volatile and fast-paced, data scraping for such use-cases should be automated, regularized and reported immediately. In a different case study, a customer wanted to make a library of all the news content their competitors are posting. If you want to get an understanding of when the competitors updated or omitted something later and why; this serves as a significant use-case.We at Datahut have also helped our customers extract a significant component of alternative data, viz. web data, to predict market performance and design pricing and investment strategies. We helped a customer extract customer review data to, in turn, design a predictive analysis model that would predict how the Netflix stock is going to perform in the near future. Interesting, is it not?What else can be done?Although the above examples reflect the industry trends at scale, the possibilities are not limited to these. Even online blogs and platforms that host articles can employ analytics to make vital strategic decisions. If you are a writer, who publishes content on platforms like Medium, would you not want to gain insights on what your followers or the readers think of your content? While the comments and reviews would give you a direct insight on the same, the sparsity and lack of such direct feedback can be covered up with stats of your articles. Metrics like the ratio of the number of readers to the number of views, basically tells you if your articles’ theme intersects with the readers’ interests.Several other entertainment and lifestyle companies can use data to design advertising campaigns, discount programs, and marketing campaigns. This data can also be used for designing dynamic pricing campaigns.The Weather Channel (TWC), a privately owned weather business co-owned by IBM uses Big Data and inquisitive analytics to observe and study customer behavior in specific weather conditions. It has been able to fabricate a marketplace, WeatherFX where sellers can advertise their products that show higher chances of selling in a particular weather scenario. Companies like BookMyShow predict a movies’ performance to design discount coupons and promotional offers for the audience.

Datahut Partners With Freetext to Help Brands Improve Their Online Reputation and Sales

sandra — Thu, 27 Jun 2019 08:59:06 +0430

Datahut, an enterprise-grade web data extraction platform, and Freetext, a deep learning based text analysis platform, today announces a new strategic partnership to help brands improve their online reputation and sales. This partnership will combine Datahut’s data extraction capabilities with Freetext’s platform for mining deep meaningful insights from unstructured textual feedback data. The collaboration between Datahut’s expertise in acquiring large scale structured data from the web and the Freetext.AI’s cutting edge NLP and Machine Learning technology will provide greater value to businesses looking for data-driven actionable insights from unstructured textual feedback data. Consumer feedback data is often scattered at various distributed locations, internally within the organization as well as on the web. This feedback, in the form of user reviews, chats, emails, and surveys, can be used to improve customer satisfaction, draw effective business roadmaps and improve the existing product based on consumer feedback. Commenting on the partnership Tony Paul, co-founder and Head of Sales at Datahut said.This partnership will help brands unlock new insights about customer feedback and improve their brand equity and sales. It is the only solution available in the market that can get deep insights at huge scale across multiple online channels, helping brands understand what to do next to improve their reputation and sales Dyuti Barma, co-founder at FreeText said,In the internet age, the voice of the customer is stronger than ever before. It is imperative that businesses not only collect unbiased customer feedback but also understand and appropriately act on it.The partnership between FreeText and Datahut shall provide businesses with all the tools they need to listen to consumer feedback from multiple online sources, derive deep actionable insights and build a truly customer-centric company.Read more about why this partnership makes sense and why brands should use it :About FreetextFreeText is a new age text analysis platform that leverages cutting-edge technology in Natural Language Processing (NLP) and Machine Learning to extract meaning and insights from unstructured textual feedback data such as user reviews, chats, emails, surveys, support tickets, etc.Freetext converts unstructured textual customer feedback from multiple sources into data-driven actionable insights and trends. These insights help inform product roadmaps and drive real business impact – reducing churn, improving customer satisfaction, reducing customer support costs – all based on the real feedback of customers.

Boost E-commerce Growth and Brand Reputation Using Customer Feedback Data

sandra — Thu, 27 Jun 2019 08:48:29 +0430

Once only leveraged by enormous corporations, big data today has become an indispensable driving force for growth and operations in small and medium businesses. Easy access to big data technologies and the growth of analytical tools has spurted out opportunities for harnessing a variety of data-based insights not thought of before. Customer feedback data is being used for e-commerce growth and for improving brand reputation. By continuously tracking various forms of online consumer data, brands are now able to combine online consumer footprints with intelligent analytics tools to make off of their brand perception and improve online brand reputation by using data to intelligently influence buyers. Remember how your Uber app would offer you discounts in a specific city the moment it learns through your location data. Studies find that for an enterprise, customer analytics are essential for improving customer experiences and hence brand reputation across all marketing, sales and service channels. Recently, a study conducted by Harvard Business Review Analytic Services in collaboration with SAS, Intel and Accenture Applied Intelligence suggests the following statistics:70% of enterprises have raised their expenditure on real-time customer analytics solutions over the past year.By using customer analytics 58% of enterprises are witnessing a significant increase in customer retention and loyalty.60% of enterprises use real-time customer analytics to improve customer experience across touchpoints and devices.44% of enterprises are gaining new customers and increasing revenue as a result of adopting and integrating customer analytics into their operations.These statistics suggest that global enterprises are rapidly embracing the role of customer analytics in improving their business operation and create a future roadmap for their company.Customer Reviews influence Purchasing DecisionsToday, reviews hold the greatest power in a buyer’s purchasing decision making. Whether it purchasing a car or a new mobile phone, renting Airbnbs or getting cab services, or even from finding a diner to hunting the right tour for an expedition, users go through reviews before making any expenditures.Research suggests that nearly 92% of e-commerce buyers look into the reviews of a product online before making a purchasing decision. Moreover, 72% of all buyers irrespective of online or offline shopping, still browse through reviews online before making a purchase in a physical store. The statistical evidence suggests that reviews and ratings largely affects a buyer’s purchasing instincts, and the presence of negative reviews on a product affects the brand equity of a company negatively. The average star rating of a product is one of the most intuitive indicators of the overall quality of the product for the consumer. Even if a product has a large number of reviews written online, a typical consumer may/may not have the time to go through all of them. In most cases, the average star rating serves as a quick summary of how past buyers have perceived the brand.Moreover, the average star rating determines how highly the product shows up in e-commerce organic search results. This also improves the product’s SEO standing in overall Google search results. Therefore, to improve their online reputation and brand equity, brands need to not only strive to raise the average star ratings of their products but also the rating of the overall brand. This improves consumer perception, leads to a higher level of trust leading up to faster sales conversions. This raises your e-commerce search rankings and in turn improving your online visibility.Real-time monitoring of your product’s online reviews helps you identify the larger pain points and act on it.Review Analytics: The Ultimate Tool to multiply Sales and Revenue Once extracted and analysed well, review data can work wonders for you. Freetext.ai’s Text Analysis tool not only enables brands to look at their own products but also monitor how competitor’s products are doing compared to theirs. Three actionable ways to use review data for improving sales and revenue generation are:1.Transform your marketing strategies When it comes to devising new marketing strategies, online review data can act as a fuel for streamlining your strategy. Listening to what people have to say can help you focus on the customer’s pain points and widen your customer base. According to a recent survey on consumer reviews, nearly 85% of consumers rely on online reviews as much as personal recommendations. Image courtesy: HARVARD BUSINESS REVIEW ANALYTIC SERVICES REAL-TIME ANALYTICS: THE KEY TO UNLOCKING CUSTOMER INSIGHTS & DRIVING THE CUSTOMER EXPERIENCEMarketers can use this data backed decision making to come up with better marketing prospects. Acting on negative reviews, brands can identify trends causing the negative reviews and show improvement. Thereby winning back customers. After all, framing your rectification as “Here’s what we’ve changed after hearing you out” makes for a good marketing comeback. Review data collected demographically and analyzed can reveal in which demographic markets your product is performing well and in which aren’t. Data suggesting underperformance in a regional store can prompt you to reconsider your branding techniques and launch a different marketing campaign there to improve sales.2. Enhanced decision making Your review data can enable you to make better decisions for your business. This data will help you identify the positive and negative trends surrounding your product, get a sense of the impact created by your product, and help you create a clearer roadmap for the future of your company. Integration of this data will not only enable you to streamline your marketing strategies but also help you formulate major business decisions. 3. Improved Customer Service Reviews essentially pinpoint your customer’s issues with your brand and put your revenue and sales targets at risk. Responding immediately to negative reviews can make or break your business.Once negative review data is gathered, find out the major areas in your product or service where consumers are facing challenges. Give immediate response to show them you’re trying your best to remedy.Use this as an opportunity to gain back the distraught customer’s trust Responding to your customer’s negative feedback the right way can not only make your turn a negative experience into a positive one but also gain you positive testimonials useful for your future marketing efforts. How Datahut and Freetext.ai can help you get the most out of Review data?Datahut and Freetext.ai have collaborated to provide companies with enhanced review data analytics. With Datahut’s expertise at large scale web data extraction coupled with Freetext.ai’s AI analytics tool for extracting sentiment analysis, organizations will now be able to achieve an in-depth insight into their product/brand’s perception online.Most organizations track essential KPIs like Net Promoter Score (NPS), Customer Satisfaction Scores (CSAT), and Average Star Ratings. While these KPIs give a general idea about a brand’s overall performance and customer satisfaction, they are not enough to delve deep into customer pain points. Using Datahut’s review data feeds and Freetext.ai’s AI-powered data analysis tools, brands will now be able to track:1. Average Star Ratings over a period of time When shopping on an e-commerce portal, buyers are tempted to check out the product’s star rating. While an average star is an intuitive indicator of a product’s perception by its buyers, the average star rating available online are ratings at the snapshot level i.e rating of a product at that point of time. A product with a low current average star rating may have started off as a high rated product. Thus, realizing what caused the product to underperform over a period of time is essential to build remedial development strategies2. Track Sentiment Score per area of focus Sentiment Analysis is a subfield of Natural Language Processing (NLP) that creates a system to identify and extract opinions from within the text. Besides identifying the opinion, these systems extract attributes like:Polarity: if the writer expresses a positive or negative opinion,Subject: the topic of discussion,Opinion holder: the person, or entity that expresses the opinion.Often times, brands focus on tracking an overall sentiment score for their products. However, an overall sentiment score is not an accurate indicator of a product’s brand equity. For example, an XYZ speaker is a top recommended item on e-commerce stores. However, it’s sales have been dwindling over the past few months. While the speaker may be offering competitive features in its category, there are many factors which may attribute to the negative online image of the speaker. These can be sound quality, packaging, delivery time, battery life, wireless connectivity, returns etc. Sentiment analysis over an area of focus enables brands to identify pain points in their product’s perception at a granular level and take remedial action accordingly.3. Product/Brand’s rank in the category Review data of your product and your competitors can be analysed to assess your product’s overall standing/rank in its category. Categorizing similar polarity reviews can even help brands assess in which aspects their product performance lags behind their competitor’s. For example, a brand’s speaker may be a top performing model in terms of sound quality but may garner negative reviews for shorter battery life than its counterparts. Accounting for such lackings timely can help brands gain a competitive advantage Datahut and Freetext.ai are here to bring you deeper and actionable insights from your brand’s online review data. Wish to leverage review analytics with us? To know more, contact Datahut

A Complete List of Online Resources to Learn Essential Data Science Skills

sandra — Fri, 14 Jun 2019 09:29:56 +0430

It is predicted that data science, big data, and machine learning will hold the top spot for hot professions of the future. Data has now become an integral tool for decision making. All human activities generate huge volumes of data every day. And with this, acquiring data science skills now holds an even important position in decision making and studying behavioral impacts in various day to day applicationsAs a result, the need for data sciences and analytics is constantly growing and evolving. This calls for learning new and innovative ways to deal with the huge volumes of data that keep streaming into an ecosystem.A lot of organizations are incorporating data analytics into their regular operations. They are creating a strong muscle in the field of data sciences and are hence, setting benchmarks for the rest of the market. To help you and your peers get started on a learning track for data science skills, we have compiled a list of the resources you can use. How can one gain Data Science skills?While the role of a data scientist is lucrative and interesting, it needs some basic skills and the urge to learn and develop within you. While there are several academic programs that provide formal training on several data science skills, there are various open-source learning resources to gain knowledge and experience in the field.Some of the resources to get learn good Data Science skills from are:A. Open-Source Data Science blogs:R bloggersR-Bloggers is a blog and content aggregator that contains articles on various statistical algorithms, data manipulations, and visualization techniques. Moreover, the blog site hosts various tutorials on how to execute different algorithms on R, one of the most popular programming languages used in Data Sciences. It was started by Tal Galili and has now become a reliable resource for both advanced and beginner-level data scientists. You will find articles and blogs on multiple R systems, commands, libraries and packages on the site. Would you also not like content on how to implement data science algorithms from scratch? R-Bloggers can help you with various snippets of R-codes and clarifications on widely faced problems in R programming. Since it is an open-source forum, people can contribute to it in all forms.Towards Data ScienceTowards Data Science has become a large community of 70,000 unique daily visitors on an average as of July 2018. It is yet another online blog and content forum that hosts articles on machine learning, data science, visualization tools, and programming techniques. Thecontent is hosted on an online publishing platform, Medium and covers even the most niche technologies and nascent use-cases of data science. A highly able team checks and validates the quality, authenticity, and readability of the content put up on the forum. You can also apply to be a contributor on Towards Data Science by sending the team a copy of the CV and the link to a sample write-up on Medium. This blog site can help people stay on top of all developments in the Data Science industry. They can also find help on how to execute certain algorithms and solve some real-world data science problems.Machine Learning (Theory)Machine Learning is a blog site started and managed by John Langford, the Director of Learning at Microsoft Research. This collaborative machine learning blog covers a wide range of topics like mathematics, information theory, predictive analytics and statistics among many others. Furthermore, Langford uses this forum to share his knowledge, expertise and personal insights on learning theory, neuroscience, economics and other unconventional areas of application of data science. The forum covers conferences and related events, thus facilitating accelerated learning within the community.Miscellaneous Data Science BlogsYou can also explore several other blogging platforms and sites that cover various aspects of data science and analytics. Edwin Chen, a data scientist with experience at Dropbox, Microsoft, and Clarium Capital Management writes his blog to convey his thoughts on the subject.We feel FastML, Data Mining Blog and Statistical Modelling, Causal Inference, and Social Science are some popular blogs for data science and analytical learning. Datahut also has a blog site that hosts various informative articles about the application of data science in industries like retail, manufacturing, finance and even security.B. MOOCs and Courses on Data ScienceCoursera Coursera has one of the most exhaustive collections of online courses on any subject. You can start learning the fundamentals of the science and even the more advanced concepts. The MOOC platform has a huge global outreach and courses on industrial applications of data science (Social Media Data Analytics, Materials Data Sciences and Informatics, Data Science in Stratified Healthcare and Precision Medicine). We have also listed a few courses you can get started with:– Machine Learning (by Stanford University): The course covers concepts like regression, classification and neural networks. Besides this, it also delves deeper into the concepts of data mining. engineering and preprocessing. This course gives a holistic training in data sciences.– Mathematics for Machine Learning (by Imperial College London): This is a collection of 3 courses that cover mathematical concepts like linear algebra, multivariate analysis, and Principal Component Analysis. Since data science relies heavily on mathematics, it is necessary that individuals learn the fundamentals before implementing the algorithms.– Data Science Specialization (by Johns Hopkins University): This specialization comprises of 9 courses and a capstone project on various subjects. While it covers basic topics like R programming, it also provides training on Practical Machine Learning. Hence, it is often deemed as a good starting point for data science beginners.Coursera offers several other courses for studying data science and analytics. You can explore these courses and sign up for them using a few simple steps.DatacampIf you are a beginner, Datacamp is a great place to learn to programme in R and Python. With its step-wise approach, you can take one problem at a time and solve/ debug incomplete lines of code. Since the prices of these courses are affordable, people from various age groups and professions can pursue these courses. Datacamp also offers regular discounts on various features and courses. You can thus, pursue multiple tracks with around 20 courses each to build and strengthen the programming muscle. Some of the popular learning tracks are Data Scientist with Python, Quantitative Analysis with R, Data Manipulation with Python and Data Visualization with R.Multiple other learning sitesThere are several other websites that offer courses in data science, analytics, programming, and even industrial case studies. Udacity, Udemy, edX and Khan Academy are a few options available currently. Since most of these sites are also available as mobile applications, one can pursue these courses on the go. These courses can help people improve their data science skills easily.C. Practice sites for Data Science projectsKaggle Kaggle is touted as one of the best platforms for developing and testing ones’ data science skills. Most data scientists use Kaggle as a forum for applying machine learning and statistical algorithms to real-world industrial problems.It is a community-based forum where people contribute snippets of codes and datasets for others to learn from. Furthermore, any individual can think of a project from scratch, create data for the same and host for others to participate. Since there is immediate feedback from the peers on all activities, Kaggle facilitates accelerated learning and growth in the field of data science.HackerearthHackerearth has practice exercise and challenges hosted by a lot of leading companies and industry players. Individuals who want to put their data science skills to use can easily sign up for any of these competitions. While a lot of these competitions are designed for the purpose of hiring individuals, one can also participate in them just for improving the problem-solving skills. With real-world problems and sample datasets, these challenges can help you develop the problem solving, programming and mathematical skills.Multiple other practice resourcesAlthough theoretical training is necessary for any subject, practical experience is mandatory for you to acquire and grow the data science skills. Websites like Dataquest provide a platform for gaining hands-on experience in the field. A user can work on data, write codes and build projects on this forum. Datastock facilitates that by providing cleaned and ready-to-use datasets across verticals like eCommerce, healthcare and travel industry.Datahut has the capability to scrape usable information from any website and store them in a structured format for convenient usage. An individual can use these datasets to perform analysis and gain insights. Although these services are paid, they can be used to create a good portfolio for a data scientist which may guarantee him a lucrative opportunity in the data science world.D. Online Data Science CommunitiesBesides courses, practice sites and reading material, there are a lot of channels for community-based learning. Although data science is a relatively new field, it has grown rapidly. You can learn from people who have now mastered the field. There a lot of forums which can help you connect to a data science community. Analytics Vidhya is one such ecosystem. People here learn, compete, teach and give feedback to their peers- all in the field of data science and analytics. Stack Overflow is an online develop community where people post questions on various programming languages and algorithms.People in the community then answer these questions the best to their knowledge, debate on the same and provide the best possible learning experience to each other. This is the fastest way to learn and solve some problems in the data science world.E. Books on Data ScienceAre you someone who likes learning from books instead of online resources? Don’t worry! We have you covered too. There are several industry experts and statisticians who have published books in the field. Some of these books focus on a particular programming language like R, Python or SQL. However, several others talk about how to use data and information to derive actionable predictions or insights. Predictive Analytics by Eric Siegel is a good book most data scientists have come across. Machine Learning Yearning by Andrew Ng is another great recommendation for people who want to work on their data science skills. Individuals can explore various options and decide which book suits them the best. However, there are various open-source resources which can solve the purpose equally well.SummaryWhile all the above resources can help one to get information on the field, the prerequisite is the intent and the interest. You can use a few or all the resources to learn as much as he/she wants to.Universities across the globe have also designed certificate courses and formal programs for data science and analytics. While the field is nascent, it is also growing at a rapid rate. It would be best for individuals to join the bandwagon now before it is too late.Originally published article - https://blog.datahut.co/a-complete-list-of-online-resources-to-learn-essential-data-science-skills/ Know more about some online resources which may help in equipping with essentials Data Science skills? Let us know in the comments below.

Challenges That Make Amazon Data Scraping So Painful

sandra — Mon, 03 Jun 2019 11:14:48 +0430

The e-commerce industry is a rapidly growing and evolving sector. The face of this industry has changed almost every year since it was first incepted in the early 1990s. With the growing number of digital buyers, the digital retail industry has shown a growth rate of above 20% in the last 3 years.This growing industry demands sophisticated analytical techniques to predict market trends, study customer temperament or even get a competitive edge over the myriad of players in this sector. To augment the strength of these analytical techniques, you need high-quality reliable data. This data is called alternative data and can be derived from multiple sources. Some of the most prominent sources of alternative data in the e-commerce industry are customer reviews, product information, and even geographical data. E-commerce websites are a great source for a lot of these data elements. It is no news that Amazon has been at the forefront of the e-commerce industry, for quite some time now. Amazon has been on the cutting edge of collecting, storing and analyzing a large amount of data- be it customer data, product information, data about retailers or even information on the general market trends. Since Amazon is one of the largest e-commerce websites, a lot of analysts and firms depend on the data extracted from here to derive actionable insights.However, Amazon data scraping is not easy! Let us go through a few issues you may face while scraping data from Amazon.Why is it tough to scrape data from Amazon?Before you start Amazon data scraping, you should know that the website discourages scraping in its policy and page-structure. Due to its vested interest in protecting its data, Amazon has basic anti-scraping measures put in place. This might stop your scraper from extracting all the information you need. Besides that, the structure of the page might or might not differ for various products. This might fail your scraper code and logic. The worst part is, you might not even foresee this issue springing up.You might even run into some network errors and unknown responses. Furthermore, captcha issues and IP (Internet Protocol) blocks might be a regular roadblock. You will feel the need to have a database. The lack of one might be a huge issue! You will also need to take care of exceptions while writing the algorithm for your scraper. This will come in handy if you are trying to circumvent issues due to complex page structures, unconventional (non-ASCII) characters, and other issues like funny URLs and huge memory requirements. Let us talk about a few of these issues in detail. We shall also cover how to solve them. Hopefully, this will help you scrape data from Amazon successfully.Amazon can detect Bots and block their IPsSince Amazon prevents web scraping on its pages, it can easily detect if an action is being executed by a scraper bot or through a browser by a manual agent. A lot of these trends are identified by closely monitoring the behaviour of the browsing agent. For example, if your URLs are repeatedly changed by only a query parameter at a regular interval, this is a clear indication of a scraper running through the page. It thus uses captchas and IP bans to block such bots. While this step is necessary to protect the privacy and the integrity of the information, one might still need to extract some data from the Amazon web page. To do so, we have some workarounds for the same. Let us look at some of these:Rotate the IPs through different proxy servers if you need to. You can also deploy a consumer grade VPN service with IP rotation capabilities.Induce random time-gaps and pauses in your scraper code to break the regularity of page triggers.Remove the query parameters from the URLs to remove identifiers linking requests together.Change the scraper headers to make it look like the requests are coming from a browser and not a piece of code.A lot of product pages on Amazon have varying page structuresIf you have ever attempted to scrape product descriptions and scrape data from Amazon, you might have run into a lot of unknown response errors and exceptions. This is because most of your scrapers are designed and customized for a particular structure of a page. It is used to follow a particular page structure, extract the HTML information of the same and then collect the relevant data. However, if this structure of the page changes, the scraper might fail if it is not designed to handle exceptions. A lot of products on Amazon have different pages and the attributes of these pages differ from a standard template. This is often done to cater to different types of products that may have different key attributes and features that need to be highlighted. To address these inconsistencies, write the code so as to handle exceptions. Furthermore, your code should be resilient. You can do this by including ‘try-catch’ phrases that ensure that the code does not fail at the first occurrence of a network error or a time-out error. Since you will be scraping some particular attributes of a product, you can design the code so that the scraper can look for that particular attribute using tools like ‘string matching’. You can do so after extracting the complete HTML structure of the target page. Your scraper might not be efficient enough!Ever got a scraper that has been running for hours to get you some hundred thousands of rows? This might be because you haven’t taken care of the efficiency and speed of the algorithm. You can do some basic math while designing the algorithm. Let us see what you can do to solve this problem! You will always have the number of products or sellers you need to extract information about. Using this data, you can roughly calculate the number of requests you need to send every second to complete your data scraping exercise. Once you compute this, your aim is to design your scraper to meet this condition! It is highly likely that single-threaded, network blocking operations will fail if you want to speed things up! Probably, you would want to create multi-threaded scrapers! This allows your CPU to work in a parallel fashion! It will be working on one response or another, even when each request is taking several seconds to complete. This might be able to give you almost 100x the speed of your original single-threaded scraper! you will need an efficient scraper to crawl through Amazon as there is a lot of information on the site!You might need a cloud platform and other computational aids!A very high-performance machine will be able to speed the process up for you! You can thus avoid burning the resources of your local system! To be able to scrape a website like Amazon, you might need high capacity memory resources! You will also need network pipes and cores with high efficiency! A cloud-based platform should be able to provide these resources to you! You do not want to run into memory issues! If you store big lists or dictionaries in memory, you might put an extra burden on your machine-resources! We advise you to transfer your data to permanent storage places as soon as possible. This will also help you speed the process up.There is an array of cloud services that you can use for reasonable prices. You can avail one of these services using simple steps. It will also help you avoid unnecessary system crashes and delays in the process. Use a database for recording informationIf you scrape data from Amazon or any other retail website, you will be collecting high volumes of data. Since the process of scraping consumes power and time, we advise you to keep storing this data in a database. Store each product or sellers’ record that you crawl as a row in a database table. You can also use databases to perform operations like basic querying, exporting and deduping on your data. This makes the process of storing, analyzing and reusing your data convenient and faster!SummaryA lot of businesses and analysts, especially in the retail and e-commerce sector need to scrape Amazon data. They use this data to make prices comparison, studying market trends across demographics, forecasting product sales, reviewing customer sentiment or even estimating competition rates. This can be a repetitive exercise. If you create your own scraper, it can be a time-consuming, challenging process.However, Datahut can scrape e-commerce product information for you from a wide range of web sources and provide this data in readable file formats like ‘csv’ or other database locations as per client needs. You can then use this data for all your subsequent analyses. This will help you save resources and time. We advise you to conduct thorough research on the various data scraping services in the market. You may then avail the service that suits your requirements the best. and provide this data in readable file formats like ‘csv’ or other database locations as per client needs. You can then use this data for all your subsequent analyses. This will help you save resources and time. We advise you to conduct thorough research on the various data scraping services in the market. You may then avail the service that suits your requirements the best.

Beginner’s guide to Web Scraping with Python lxml

sandra — Sat, 18 May 2019 10:01:43 +0430

Web Scraping with Python is a popular subject around data science enthusiasts. Here is a piece of content aimed at beginners who want to learn Web Scraping with Python lxml library.What is lxml?lxml is the most feature-rich and easy-to-use library for processing XML and HTML in Python programming language. lxml is a reference to the XML toolkit in a pythonic way which is internally being bound with two specific libraries of C language, libxml2, and libxslt. lxml is unique in a way that it combines the speed and XML feature completeness of these libraries with the simplicity of a native Python API.With the continued growth of both Python and XML, there are a plethora of packages out there that help you read, generate, and modify XML files from Python scripts. Compared to most of them, the python lxml package has two big advantages:Performance: Reading and writing even fairly large XML files takes almost imperceptible amount of time.Ease of programming: python lxml library has easy syntax and more adaptive nature than other packages.lxml is similar in many ways to two other earlier packages which are called as parent packages for lxml.ElementTree: This is used to create and parse tree structure of XML nodes.xml.etree.ElementTree: This is now an official part of the Python library. There is a C-language version called cElementTree which may be even faster than lxml for some applications.However, lxml is preferred by most of the python developers because it provides a number of additional features that make life easier. In particular, it supports XPath,which makes it considerably easy to manage more complex XML structures.python lxml library can be used to either create XML/HTML structure using elements, or parse XML/HTML structure to retrieve information from them. This library can be used to get information from different web services and web resources, as these are implemented in XML/HTML format. The objective of this tutorial is throw light on how lxml helps us to get and process information from different web resources.How to install lxml? lxml can be installed as a python package using pip which is a package manager tool for python. Below is the command which is needs to be fired to install it on your system.pip install lxmlpip automatically installs all the dependencies for installing python lxml as well.lxml can be installed as a system package using binary installers depending upon system OS. I would prefer to install it using the former method, as many systems do not have a better and clean way to install this package if the latter is used.How to use lxml? Python is a very easy language to learn but libraries which are written using python are as easy. Getting a clear picture of the function of library is ambiguous. Practical implementation will take us closer to creating an idea of what is the library actually doing. Let us pick few examples and use lxml in practical scenarios. A successful implementation of Web Scraping with Python takes time and practice.As discussed earlier, we can use python lxml to create as well as parse XML/HTML structures.In a first and very basic example, let’s create a html web page structure using python lxml and define some elements and its attributes. So, let us begin!lxml has many modules and one of the module is a etree which is responsible for creating elements and structure using these elements.First, let’s import the “require” module in python. I generally prefer to use Ipython command shell to execute python programs because it gives an extensive and clear command prompt to use python features in a very broad way. Element nodes have multiple properties. For example a text property can be used to set a text value for a node which we can be inferred as an information for the end user. We can also set attributes for any node in the tree structure. As you can see below, I have created a html tree structure using lxml and its etree which can be saved as a html web page as well.We can set attributes for elements.Now, let’s take another example in which we shall see how to parse html tree structure. This process is a part of scraping content from web so you can follow this process if you want to scrap data from the web and process the data further.In this example, let us use requests python module, which is used to send HTTP requests to web URLs. requests module has improved speed and readability when compared to the built-in urllib2 module. So, using requests module is a better choice. Along with requests, html module is made use of from lxml, to parse the response of the request.First, let’s import require modules, In [19]: import requests In [20]: from lxml import html Using requests module, let’s send a get request to cnn.com website to retrieve top news stories. HTTP web server sends the response as a Response<200> object. We store this in a page variable and then use html module to parse it and save the results in a tree. Response object has multiple properties like response headers, contents, cookies etc. We can use python dir() method to see all these object properties. Here, I am using page.content instead of page.text because html.fromstring implicitly expects bytes as input where the page.text provides content in simple text format (ASCII or utf-8, depending upon web server configuration). In [21]: page = requests.get('http://www.cnn.com')In [22]: html_content = html.fromstring(page.content) now contains the whole HTML file in a nice tree structure which we can go over two different ways: XPath and CSSSelect. In this example, we will focus on the former.XPath is a way of locating information in structured documents such as HTML or XML documents. XPath uses path expressions to select nodes or node-sets in an XML document. The node is selected by following a path or steps.The most useful path expressions are listed below: Description Selects all nodes with the name “nodename” Selects from the root node Selects nodes in the document from the current node that match the selection no matter where they are Selects the current node Selects the parent of the current node Selects attributes///...@ExpressionnodenameFollowing are some path expressions and their results:Result Selects all nodes with the name “bookstore” Selects the root element bookstore Note: If the path starts with a slash ( / ) it always represents an absolute path to an element! Selects all book elements that are children of bookstore Selects all book elements no matter where they are in the document Selects all book elements that are descendant of the bookstore element, no matter where they are under the bookstore element Selects all attributes that are named lang/bookstorebookstore/book//bookbookstore//book//@langPath ExpressionbookstoreLets get back to our scraping example. so far we have downloaded and made a tree structure from html web page. We are using XPath to select nodes from this tree structure. As, we want to get top stories, we have to analyse the web page to find the tags that are storing this information. Upon analysis we can see that h3 tag with data-analytic attribute contains this information. Selecting this node allows us to fetch the text of news stories and appropriate web links to read for complete news. Ta da! We have successfully covered scraping using python lxml and requests. We have it stored in memory as a lists. Now we can do all sorts of cool stuff with it: analyze it using Python or save it in a file and share it with the world.We have covered most of the stuff related toWeb Scraping with python lxml module and also understood how can we combine it with other python modules to do some impressive work. Below are few references which can be helpful in knowing more about it.Do share this if you enjoyed reading this blog post on Web Scraping with Python. Write a web scraper on your own and share your experience with us.Referenceslxml – XML and HTML with Pythonlxml.etree TutorialParsing XML and HTML using lxmlOriginally published in Datahut.coRefer: https://blog.datahut.co/beginners-guide-to-web-scraping-with-python-lxml/

Using Location Intelligence to make Smarter Real Estate Investments

sandra — Thu, 16 May 2019 09:56:25 +0430

Real Estate investments can be a tricky ballgame. A typical investor needs to conduct thorough due diligence before making an investment decision in this space. While Alternative data is a big aid to analysis in most industries, Real estate and investment industries are no exception to this. One innovative form of alternative data for the real estate industry is the location data. Investors in the real estate industry are leveraging Location Intelligence for due diligence on existing investments and for evaluating new real estate investment opportunities. What does Location Intelligence mean?Location Intelligence is a sophisticated data handling and manipulation technique that deals with location and geographic data. This method can help you transform your location data into business outcomes and in turn, actionable insights.This data can be extracted in any form: from addresses and latitude/longitude coordinates to existing points, lines, and polygons on a map. You can also tailor readily available spatial data according to your requirements. You can use names of places and administrative units such as countries and states along with territory marks or priorities/ rankings based on various parameters suiting your portfolio and investment needs.You can club Location Intelligence with other analytical tools like Machine Learning and Data Visualization to make this data more usable and sustainable. For example, if you have built a simple forecasting model to identify the highest growing territories (in terms of value) in the next 1 year and then forecast the prices of houses in that particular area, you can basically make more informed real estate investment decisions. It helps that this decision will be backed by data! Now imagine creating an easy-to-use descriptive and beautiful dashboard that lets you monitor the prices of real estate across geographies. Now since we have covered what Location Intelligence is and how you can use it, let us talk about why you should use it.How to use Location Intelligence to make smarter real estate investment decisions?You can answer a lot of basic questions about real estate investments using Location data. Some of them are as follows:Which new areas and neighbourhood hotspots are a lucrative option for potential investment?What are the risks associated that I should identify when making a new investment?Can I use publicly available data to make a more informed real estate investment strategy? If yes, how?Who are my competitors? Can I monitor their decision patterns to drive better insights?A lot of industries use location-based data. However, the real estate vertical relies heavily on it, more than most other industries. This might be partially due to the reason that the subjects of the real estate industry- pieces of land and property can be identified on a map! Moreover, this data helps you monitor circumstances and trends in the environment around a property that can inform its value. You can use location intelligence to provide granular insights on sites along with foot traffic trends surrounding these sites and even the behaviours and demographics of visitors. A lot of this data can be extracted from the web or other sources using scraping. It is no news that real estate value is largely associated with broader social and economic conditions. Thus, if you have a very detailed understanding of a location and the trends around it, you will learn greater insights about its potential. You might find it tough to collect such actionable insights from other data sources. This is because those sources don’t really derive information from accurate foot traffic analysis or real-world actions.You can enhance your understanding of commercial, demographic, and transport data. By doing this, you can identify a real estate investment opportunity very early and ensure that your investment decisions provide you with the most value. Let us look at several applications of Location Intelligence.1. Understand your customer demographicsDemographic data is one of the most important data elements that help you understand and analyze the long term potential of an investment opportunity. You can use it to visualize trends in the population income, education, and cost of living and even their behavioural traits, professional preferences, etc.This will give you better information about not only the people who live in a location but also how that neighbourhood will evolve years after you decide to put your money in it. This can also help you ensure that you are aligning your investment choices with your long term wants and needs.2. Real Estate Site SelectionIf you use customer demographics along with data on property prices and some forecasts, you might be able to use Location Intelligence for selecting lucrative sights. Let us look at an example. Below is a map of Manhattan. Let us analyze the possibilities of a good investment for a mid-sized investor who wants to grow his portfolio in the long term (say next 10 years).If we look at the areas ‘Upper East Side’, ‘Carnegie Hall’, ‘Tribeca’ and ‘West Village’ among a few others, they show higher wealth concentrations as compared to others. They also have a lower density of public buildings and housing which also implies the scope of growth and development with even small scale projects.What this means is, if you are a mid-sized investor with capital enough to buy plots of land and build small scale housing projects, you might be looking at tremendous growth in the next few years. Let us also look at a heat map of the property values in these areas. Among the 4 areas identified above, we see ‘Tribeca’ and ‘West Village’ show a medium to high growth of property value. Using these multiple data sources can not only help you narrow down the areas of interest but also help you make more confident data-backed decisions. You could also incorporate foot traffic analysis into the overall real estate investment strategy. This would help you gain a better understanding of where your target consumers visit most frequently. You could then analyze these trends and movement patterns around specific areas of interest. Let us say an investor shows interest in a particular shopping plaza, he or she could use and analyze the relevant historical location data to learn the number of visitors, length of each visit, time and frequency of their visit and other demographic information.Thus, you can use location intelligence and go beyond general population data to find areas with an adequate number of potential consumers with ideal purchasing trends and income patterns. If you invest in an area that already has frequent and regular visitors and shoppers, this would ensure the right kind of foot traffic, thus making it a success for everyone involved in the investment.3. Identify emerging neighbourhood trendsOpen source datasets like taxi drop-offs and fares in localities can be used to understand how the public transport connectivity is, in a particular community. This information can then be clubbed with data from social networking sites like Twitter and Foursquare to understand what the most happening hang-out spots are. You can also use it to understand what sites are the best for building specific facilities. like bookstores, shopping marts or even hospitals.Location Intelligence is a powerful tool as it enables real estate companies to identify and study neighbourhood trends. They use it to forecast or predict if an area is worth investing or not, before anyone else. You can use analytical tools for the same. For example, location intelligence can help you detect if a community is growing and developing.You would also be able to observe if the area trends are inconsistent with the investment thesis or if the foot traffic patterns imply lower consumer visit rates. In this case, if you are an investor, you might be able to sell out of their investment while the market still has a viable value. Most other data sources that provide this information generally, have a long lag time. In that case, it is often too late by the time the report comes in. BY this time, either your competitors have also learned about the data or the market value has changed. Location data is a real-time source of actionable information and thus, enables firms to act early and swiftly.4. Study competitor behaviour using Location IntelligenceYou can access Location Intelligence to conduct a thorough study of real estate competitor analysis. You can do so by geofencing and keeping an eye on your competitors’ locations to get a relative sense of their foot traffic patterns and loyalty levels. In this pursuit, you may discover a popular competitor’s location and invest in property in its vicinity to capitalize on some of that foot traffic and reroute some of that capital to your own business.This article is originally published in https://blog.datahut.co/using-location-intelligence-to-make-smarter-real-estate-investments/