Review Each Of The Following Records Analyzing The: Fill & Download for Free

Some of the good Big Data use-cases that can be implemented are:Project #1: Analyze social bookmarking sites to find insightsData: It comprises of the information gathered from sites like the front page of the internet, StumbleUpon which are bookmarking sites and allow you to bookmark, review, rate, search various links on any reddit, StumbleUpon, etc.Problem Statement: Analyze the data in the Hadoop ecosystem to:Fetch the data into HDFS and analyze it with the help of MapReduce, Pig and Hive to find the top rated links based on the user comments, likes etc.Using MapReduce, convert the semi-structured format (XML data) into a structured format and categorize the user rating as positive and negative for each of the thousand links.Push the output into HDFS and then feed it into PIG, which splits the data into two parts: Category data and Rating data.Write a fancy Hive Query to analyze the data further and push the output into a relational database (RDBMS) using Sqoop.Project #2: Customer Complaints AnalysisData: Publicly available dataset, containing a few lakh observations with attributes like; CustomerId, Payment Mode, Product Details, Complaint, Location, Status of the complaint, etc.Problem Statement: Analyze the data in the Hadoop ecosystem to:Get the number of complaints filed under each productGet the total number of complaints filed from a particular locationGet the list of complaints grouped by location which has no timely responseProject #3: Tourism Data AnalysisData: The dataset comprises attributes like City pair (combination of from and to), adults traveling, seniors traveling, children traveling, air booking price, car booking price, etc.Problem Statement: Find the following insights from the data:Top 20 destinations people frequently travel to, based on the given data we can find the most popular destinations where people travel frequently, based on the specific initial number of trips booked for a particular destinationTop 20 locations from where most of the trips start based on booked trip countTop 20 high air-revenue destinations, i.e the 20 cities that generate high airline revenues for travel, so that the discount offers can be given to attract more bookings for these destinations.Project #4: Airline Data AnalysisData: Publicly available dataset which contains the flight details of various airlines such as Airport id, Name of the airport, Main city served by airport, Country or territory where the airport is located, Code of Airport, Decimal degrees, Hours offset from UTC, Timezone, etc.Problem Statement: Analyze the airlines data to:Find list of airports operating in the countryFind the list of airlines having zero stopsList of airlines operating with code shareWhich country (or) territory has the highest number of airportsFind the list of active airlines in the United StatesProject #5: Analyze Loan DatasetData: Publicly available dataset which contains complete details of all the loans issued, including the current loan status (Current, Late, Fully Paid, etc.) and latest payment information.Problem Statement:Find the number of cases per location and categorize the count with respect to the reason for taking a loanand display the average risk score.Project #6: Analyze Movie RatingsData: Publicly available data from sites like rotten tomatoes, IMDB, etc.Problem Statement: Analyze the movie ratings by different users to:Get the user who has rated the most number of moviesGet the user who has rated the least number of moviesGet the count of total number of movies rated by user belonging to a specific occupationGet the number of underage usersProject #7: Analyze YouTube dataData: It is about the YouTube videos and contains attributes such as VideoID, Uploader, Age, Category, Length, views, ratings, comments, etc.Problem Statement:Identify the top 5 categories in which the most number of videos are uploaded, the top 10 rated videos, and the top 10 most viewed videos.Project #8: US ElectionIndustry: GovernmentProblem Statement:In the US Primary Election 2016, Hillary Clinton was nominated over Bernie Sanders from Democrats and on the other hand, Donald Trump was nominated from Republican Party to contest for the presidential position. As an analyst, you have been tasked to understand different factors that led to the winning of Hillary Clinton and Donald Trump in the primary elections based on demographic features to plan their next initiatives and campaigns.Project #9: Design a system to replay the real time replay of transactions in HDFS using Spark.Project #10: Instant CabsIndustry: TransportationProblem Statement:A US cab service start-up (i.e. Instant cabs) wants to meet the demands in an optimum manner and maximize the profit. Thus, they hired you as a data analyst to interpret the available Uber’s data set and find out the beehive customer pick-up points & peak hours for meeting the demand in a profitable manner.Project #11: Drop-page of signal during RoamingIndustry: Telecom IndustryProblem Statement:You will be given a CDR (Call Details Record) file, you need to find out top 10 customers facing frequent call drops in Roaming. This is a very important report which telecom companies use to prevent customer churn out, by calling them back and at the same time contacting their roaming partners to improve the connectivity issues in specific areas.I have mentioned just few of them. There are many more projects that can be implemented. You can go through this video to get an idea.Edureka provides a good list of Hadoop Tutorial videos. I would recommend you to go through this Hadoop tutorial video playlist as well as Hadoop Tutorial blog series, where you can find multiple examples with their explanations. Your learning should be aligned with Hadoop Certification.

Some of the good Big Data use-cases that can be implemented are:Project #1: Analyze social bookmarking sites to find insightsData: It comprises of the information gathered from sites like the front page of the internet, StumbleUpon which are bookmarking sites and allow you to bookmark, review, rate, search various links on any reddit, StumbleUpon, etc.Problem Statement: Analyze the data in the Hadoop ecosystem to:· Fetch the data into HDFS and analyze it with the help of MapReduce, Pig and Hive to find the top rated links based on the user comments, likes etc.· Using MapReduce, convert the semi-structured format (XML data) into a structured format and categorize the user rating as positive and negative for each of the thousand links.· Push the output into HDFS and then feed it into PIG, which splits the data into two parts: Category data and Rating data.· Write a fancy Hive Query to analyze the data further and push the output into a relational database (RDBMS) using Sqoop.Project #2: Customer Complaints AnalysisData: Publicly available dataset, containing a few lakh observations with attributes like; CustomerId, Payment Mode, Product Details, Complaint, Location, Status of the complaint, etc.Problem Statement: Analyze the data in the Hadoop ecosystem to:· Get the number of complaints filed under each product· Get the total number of complaints filed from a particular location· Get the list of complaints grouped by location which has no timely responseProject #3: Tourism Data AnalysisData: The dataset comprises attributes like City pair (combination of from and to), adults traveling, seniors traveling, children traveling, air booking price, car booking price, etc.Problem Statement: Find the following insights from the data:· Top 20 destinations people frequently travel to, based on the given data we can find the most popular destinations where people travel frequently, based on the specific initial number of trips booked for a particular destination· Top 20 locations from where most of the trips start based on booked trip count· Top 20 high air-revenue destinations, i.e the 20 cities that generate high airline revenues for travel, so that the discount offers can be given to attract more bookings for these destinations.Project #4: Airline Data AnalysisData: Publicly available dataset which contains the flight details of various airlines such as Airport id, Name of the airport, Main city served by airport, Country or territory where the airport is located, Code of Airport, Decimal degrees, Hours offset from UTC, Timezone, etc.Problem Statement: Analyze the airlines data to:· Find list of airports operating in the country· Find the list of airlines having zero stops· List of airlines operating with code share· Which country (or) territory has the highest number of airports· Find the list of active airlines in the United StatesProject #5: Analyze Loan DatasetData: Publicly available dataset which contains complete details of all the loans issued, including the current loan status (Current, Late, Fully Paid, etc.) and latest payment information.Problem Statement:· Find the number of cases per location and categorize the count with respect to the reason for taking a loanand display the average risk score.Project #6: Analyze Movie RatingsData: Publicly available data from sites like rotten tomatoes, IMDB, etc.Problem Statement: Analyze the movie ratings by different users to:· Get the user who has rated the most number of movies· Get the user who has rated the least number of movies· Get the count of total number of movies rated by user belonging to a specific occupation· Get the number of underage usersProject #7: Analyze YouTube dataData: It is about the YouTube videos and contains attributes such as VideoID, Uploader, Age, Category, Length, views, ratings, comments, etc.Problem Statement:· Identify the top 5 categories in which the most number of videos are uploaded, the top 10 rated videos, and the top 10 most viewed videos.Project #8: US ElectionIndustry: GovernmentProblem Statement:In the US Primary Election 2016, Hillary Clinton was nominated over Bernie Sanders from Democrats and on the other hand, Donald Trump was nominated from Republican Party to contest for the presidential position. As an analyst, you have been tasked to understand different factors that led to the winning of Hillary Clinton and Donald Trump in the primary elections based on demographic features to plan their next initiatives and campaigns.Project #9: Instant CabsIndustry: TransportationProblem Statement:A US cab service start-up (i.e. Instant cabs) wants to meet the demands in an optimum manner and maximize the profit. Thus, they hired you as a data analyst to interpret the available Uber’s data set and find out the beehive customer pick-up points & peak hours for meeting the demand in a profitable manner.Project #10: Drop-page of signal during RoamingIndustry: Telecom IndustryProblem Statement:You will be given a CDR (Call Details Record) file, you need to find out top 10 customers facing frequent call drops in Roaming. This is a very important report which telecom companies use to prevent customer churn out, by calling them back and at the same time contacting their roaming partners to improve the connectivity issues in specific areas.Edureka provides a good list of Hadoop Tutorial videos. I would recommend you to go through this Hadoop tutorial video playlist as well as Hadoop Tutorial blog series. Your learning should be aligned with Hadoop Certification.

Hi, I advise you to start with CRO and A/B testing. What for? With the help of conversion rate optimization, I learn what is the quantity and quality of website traffic, learn users' behavior on the website, analyze how they move throughout the website with the help of video recording, analyze the pros and cons of advertisement strategy and so on.According to own experience, I can identify the main reasons why the users can leave your online store:The problem of trust. Why should I send an advance payment for the goods? What are the terms and conditions of delivery? The site has no reviews, maybe this seller is unreliable? How to contact the seller? Your client is looking for all this information in your online store. What for? He needs to know that you are a reliable seller, so if the site does not have this data, he will go to your competitors’.Convenience. The site is inconvenient to use and the user does not understand what each button is designated for. Every 10 seconds he tries to withstand the attacks of pop-ups, but non-convenient filtering and sorting of goods does not work correctly.Price. Why is everything so expensive? After all, the sites of your competitors are similar, and the products are cheaper. It is better to order at your competitors’ store. Why do customers have such thoughts? You do not tell them about your uniqueness and the benefits that they receive from you.After understanding the reasons for falling conversions you can build the strategy of their optimization. Note! No one will give you a standard for solving your problems. The methods of increasing conversions depend on your specific business, the product you sell and the problems you have on the website.After building a strategy, start testing your hypothesis. The most important goal of A/B testing is to give you understand if it is convenient for a user to deal with your online store. Basing on the test results, you will see the weaknesses of your site and figure out what needs to be improved that it worked better.Before experimenting with your online store, make sure that Google Analytics can track your conversions and that Google Tag Manager is properly configured and tracks the events and actions you need. What are A/B tests in an online store? A/B tests are a way to test hypotheses for improving a page, attracting and retaining customers.You can achieve the following results:improvement of your online store usability;smaller numbers of users who leave your site;acceleration of the process of making purchasing decisions.How to Conduct a Split Test or A/B Test?The idea of type a or b personality test is very simple. The site users are randomly divided into segments. One of the segments remains unchanged - this is the “A” control segment; we assess the effect of the changes made on the basis of the data for this segment. Users from the “B” segment are shown the modified version of the site. After we compare the results and analyze the data obtained. It's simple, isn’t it? Let's analyze the details of this process.eCommerce A/B Testing IdeasThere are several options for testing the hypothesis.AB testing - there are 2 versions of this page: Checklist and Modified version. We can correct the page in simple editors such as VWO, Optimizely, Google Optimize.Multivariate test - there are several options for the control page.Split test - it is a completely different page design, which is valid on page/v2How and where do we work with A/B tests:first of all, we determine the user traffic and conversion rate. Then, using a special calculator, we look for how long we need to conduct the experiment. At this link, you can find more information about the calculator: CXL;next, using Google Optimize, or Visual Website Optimizer (but it is on a fee basis, so most often it uses Google), we create a test;I check the results using a Chi-square calculator. If the value of p <0.05, then with 95% confidence we can say that thanks to these changes, sales or user involvement have grown. The greater the value is, the more difficult it is to determine the reason for the victory of one or another option.Most importantly, the A/B testing allows you to validate the hypotheses and reduces the risk of financial losses spent on a redesign.What data can be tested, read here: 7 A/B Testing Ideas to Boost Conversions