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ABSTRACT To facilitate future research in unsupervised induction of syntactic structure and to standardise best-practices, we propose a tagset that consists of twelve universal part-of-speech categories. In addition to the tagset, we develop a mapping from 25 different treebank tagsets to this universal set. As a result, when combined with the original treebank data, this universal tagset and mapping produce a dataset consisting of common parts-of-speech for 22 different languages. We highlight the use of this resource via three experiments, that (1) compare tagging accuracies across languages, (2) present an unsupervised grammar induction approach that does not use gold standard part-of-speech tags, and (3) use the universal tags to transfer dependency parsers between languages, achieving state-of-the-art results. To download full research paper, click on the link below. If you need implementation of this research paper or any variant of this, feel free contact us on contact@codersarts.com.

Introduction : - Bus Booking App Focuses on the Customers with an easy and simple way to book the bus for their favorite journeys online. With a privileged customer you get an option to choose from the VIP seat and pre order food in the bus . Technical Implementation : · Sign up | Login via Phone number and social media . · Easy access to user wallet and saved card details for one clicks payments . · Option to choose the seat from the bus layout . · Option to book online from your favourites list of Buses . · Option to filter the bus based on date , time, reviews and pricing . · Go premium and get the benefits of a VIP user . · Custom dashboards for bus companies where they add buses , define routes and manage bookings , payments, etc. Challenges : - Client wants a comprehensive online bus booking system where their customers can do the booking online easily & save their time of joining a long queue to buy ticket. They also want to include advanced features such as, hotel room booking during the stay at destination, Seat booking at their shop floor by their counter operators, agent login for seat booking are few major necessities. They asked for options to collect the cash online using the payment gateway method as well as direct cash. The customers must be able to take the print out of their e-tickets. At the back end, the administrator of the web application should be able to manage the trips, ticket rates, coach seating, discounts and also can generate trip sheet with passenger details. The passenger details must possess passport details, age eligibility, etc. For this case, I use Design thinking (DT) because it’s easy to understand and implement for a novice like me. Design thinking is more like a foundation of any other process, anyone can do it. Other process like Design sprint is just made it more systematical for better production. You can see the detail of every DT process below : Ø Empathize : - This stage is all about understanding your users, like their behavior, character, and habit. So to get very well understanding I created a user persona card. And for validating, I used the interview method. Based on that fact, I need to make sure how train & bus user habits, and behaves. It is important for me to create a design with the best user experience. Like what is the facility that bus user likes? How do they buy a ticket? And many more. Ø Problems : - After I got that interview result I analyze it as soon as possible and then I found this pain points from their answer. 1. There is no fixed schedule for the bus (esp., short route). 2. The bus could be stuck in a traffic jam. 3. The bus usually overloads. 4. Can’t buy the ticket online for some routes. 5. The bus is not safe for some people due to overload. 6. The bus staying too long on terminals. 7. The bus air conditioner isn’t working. 8. Someone is foreigner, he doesn’t know much about any destination. How Might We: 1. How might we make the user know about the bus schedule? 2. How might we make the bus not overload? 3. How might we make the booking process easier for the user? 4. How might we make the bus not stuck in a traffic jam? 5. How might we make the bus air conditioner work? 6. How might we make the bus safe for the user? 7. How might we make the bus not staying too long on the terminal? 8. How might we make the user know the recommended destination? Well, now I have known the problem let’s move to ideate section. Ø Ideation : - Basically, almost all of the problem could be solved using online ticketing/booking services like Traveloka, Redbus, etc. But here is my solution to solve all the problems. · Make easy to use the application, and the interface should be user-friendly. · Show the list of the available bus. · Show recommended destination whenever user type in the search field. · Show the bus police number. · Give a rating feature to the bus. · Payment with any method. Ø Prototyping : - Honestly, this is the part I liked the most! In this part, I could explore many things about UI design. Login and register - I only provided for login method with Google account. Since it’s much easier to use so the user only needs to fill their phone number for registration(if needed). Home screen and search - Search field — since I want this application to be easy to use, I put the search card on the bottom part of the screen. Based on the mobile heatmap, the user's thumb is more likely to rest on the bottom-left of the screen. Destination, Passenger, and Departure date — on the search arrival or destination, I put recommendation places based on their location. Search result — to make the user easy to find departure time, I place the departure and arrival time on the left side. It is because our eyes tend to search from left to right. And then I put bus rating on the right side, this is to give information about the bus service qualities based on user rating. Bus detail - After the user selects the bus, the detail appeared containing information such as departure-arrival, facilities, bus picture, and price. And then they need to choose a seat before proceeding to order and payment. Ordering and payment - Because of this application principles are easy and fast, the user order form is auto-filled based on their Google account information. And then they could choose many payment methods. Ticket detail and history - After the user completing their payment, they will be redirected to ticket detail. Rating and review— The apps will detect their trip whether it is completed or not. When the current time is the same as their arrival time , app will ask for their rating and review about your bus and then the ticket detail will be shown. Ø Testing I wanted to validate if my solution works well. So I tried to test this prototype to some of my friends. I ask them to use this application based on the given scenario: 1. Try login using an existing Google account 2. Try to buy a bus ticket. 3. Gives a rating and review about your trip. from the result, Peoples don't found any problem and could successfully do the scenario given. Even the test run smoothly, It is still far from safe to say this prototype is easy to use. I need to test it on many age ranges. 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There the different types of visualization techniques are used in machine learning, in this blog we will learn 5 top important visualization libraries that are mostly used by a data scientist to visualizing the data. Matplotlib Seaborn ggplot pyplot pygal Visualization with Matplotlib Matplotlib is a free open source plotting library for creating static, animated, and interactive visualizations in Python. It also supports the mathematics extension using NumPy. Installing matplotlib using "pip" pip install matplotlib After this need to import this using: import matplotlib.pyplot as plt Examples: Plotting line graph using matplotlib: plot line using one variable(y coordinate): #plotting the line using one variable import matplotlib.pyplot as plt plt.plot([1, 2, 3, 4]) plt.show() Here the value of y is given and x is generated automatically and it starts from 0 with interval 0.5. If both coordinates are given then: plt.plot([1, 2, 3, 4], [1, 4, 9, 16]) Style formating This is other arguments except for x, y, which used for styling the graph: "ro", "-b", etc plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'ro') plt.axis([0, 6, 0, 20]) plt.show() Here "ro", means "red: o" shape, "-b", show "-" in blue color. Histogram using matplotlib You may use the below syntax to plot histogram using matplotlib. Syntax: import matplotlib.pyplot as plt x = [value1, value2, value3,....] plt.hist(x, bins = number of bins) plt.show() Example: import matplotlib.pyplot as plt x = [1,1,2,3,3,5,7,8,9,10, 10,11,11,13,13,15,16,17,18,18, 18,19,20,21,21,23,24,24,25,25 ] plt.hist(x, bins=10) plt.show() Creating a Bar Chart Using Matplotlib: Below the syntax which is used to creating the bar chart #ploting histogram using matplotlib import matplotlib.pyplot as plt plt.bar(xAxis,yAxis) plt.title('title name') plt.xlabel('xAxis name') plt.ylabel('yAxis name') plt.show() Example: import matplotlib.pyplot as plt Country = ['USA','Canada','Germany','UK','France'] GDP_Per_Capita = [45000,42000,52000,49000,47000] plt.bar(Country, GDP_Per_Capita) plt.title('Country Vs GDP Per Capita') plt.xlabel('Country') plt.ylabel('GDP Per Capita') plt.show() Visualization with Seaborn Seaborn as a library is used in Data visualizations from the models built over the dataset to predict the outcome and analyse the variations in the data. Seaborn Line Plots depict the relationship between continuous as well as categorical values in a continuous data point format. first, need to install seaborn using pip: pip install seaborn then after import, it using: import seaborn Syntax to creating a line chart using seaborn: seaborn.lineplot(x, y, data) Where: x: variable for the x-axis y: variable for the y-axis data: data values Example: import pandas as pd import seaborn as sns Year = [1990, 1994, 1998, 2002, 2006, 2010, 2014] Profit = [10, 62.02, 48.0, 75, 97.5, 25, 66.6] data_plot = pd.DataFrame({"Year":Year, "Profit":Profit}) sns.lineplot(x = "Year", y = "Profit", data=data_plot) plt.show() you can also create more other visualization like bar, histogram, scatter plot, etc using seaborn. Visualization with "ggplot" This is a visualization package that is used in R programming. Let suppose data is like: ##   baby_wt  income mother_age     smoke gestation mother_wt ## 1     120 level_1         27 nonsmoker       284       100 ## 2     113 level_4         33 nonsmoker       282       135 ## 3     128 level_2         28    smoker       279       115 ## 4     108 level_1         23    smoker       282       125 ## 5     132 level_2         23 nonsmoker       245       140 ## 6     120 level_2         25 nonsmoker       289       125 Plot1: Simple Bar-plot (Showing distribution of baby’s weight) ggplot(data = Birth_weight,aes(x=baby_wt))+geom_bar() The above code has three parts: data: It is the name of the data-set aes: This is where we provide the aesthetics, i.e. the “x-scale” which will be showing the distribution of “baby_wt”(baby weight) geometry: The geometry which we are using is bar plot and it can be invoked by using geom_bar() function. Plot2: Simple Bar-plot (Showing distribution of mother’s age) ggplot(data = Birth_weight,aes(x=mother_age))+geom_bar() Visualization Using "Pygal" Pygal specializes in allowing the user to create SVGs. Besides the scalability of an SVG, you can edit them in any editor and print them in very high-quality resolution. Installing "pygal" using "pip" pip install Pygal Import "pygal" import pygal Creating the variable to create the graph: import pygal bar_chart=pygal.Bar()() Adding some values like "title", etc. import pygal bar_chart = pygal.Bar() bar_chart.title = " ratio" Example: import pygal bar_chart = pygal.Bar() bar_chart.title = "Ratio" bar_chart.add("add1", [0.94]) bar_chart.add("add2", [1.05]) bar_chart.add("add3", [1.10]) bar_chart.render_in_browser() where render_in_browser() is used to render the graph in the web - browser. Contact us to get help related to data visualization in machine learning using below contact details: contact@codersarts.com

Turtle graphics is a built-in python module that provides a canvas and a turtle (cursor) to let you show your creativity. The turtle moves around the canvas and draws as directed. The canvas can be thought of as a graph having the origin(0,0) at its very centre. The centre is called home. That way we can assume that the canvas is divided into four quadrants. The turtle is a cursor that moves over the canvas following the instructions from the user. Initially it rests at home. When given a command turtle.forward(20), it moves 20 units in the direction in which it is pointing while drawing a line. When given a command turtle.left(90), it will rotate 90 degrees in the left direction while still being in-place. By using many other commands as mentioned above we can design many shapes and images easily. Check out the documentation for Turtle Graphics here: https://docs.python.org/3/library/turtle.html#turtle.goto Below are some of the commands that we will be using in the program code: turtle.reset(): It deletes the drawings of the turtle and sends the turtle back to home and sets everything to default. turtle.write(arg, move=False, align="left", font=("Arial", 8, "normal")): It writes the string passed in arg on the screen. The text can be formatted with align (“left”, “centre” or right”) and font(style,size,(“normal”,”bold”,”italic”)). If move is true, the pen is moved to the bottom-right corner of the text. By default, move is False. turtle.pencolor(): It sets the pencolor. It allows four types of arguments. turtle.pensize(width): It sets the thickness of the line drawn. turtle.penup() or turtle.pu(): It pulls the pen up and doesn’t draw while moving. turtle.pendown() or turtle.pd(): It pulls the pen down and draws while moving. turtle.goto(x,y) : It moves turtle to an absolute position specified by values of x and y coordinates without changing the turtle’s orientation. turtle.forward(distance) or turtle.fd(distance): It moves the turtle forward in the direction which it is pointing to by the specified distance. turtle.backward(distance) or turtle.bk(distance): It moves the turtle backward (opposite to the direction in which it is pointing) by the specified distance without changing its orientation turtle.right(angle) or turtle.rt(angle): It rotates the turtle to its right by the specified angle. turtle.left(angle) or turtle.rt(angle): It rotates the turtle to its left by the specified angle. turtle.circle(radius, extent=None, steps=None): It draws a circle with a given radius and extent. If extent is not given, it draws the entire circle. Now, using the above commands we will make the turtle write our name. It's like guiding a blind-folded person to reach his/her destination. It is better to plan out beforehand on a sheet of paper keeping the coordinates in mind to get a result which looks uniform. Moving ahead there are two ways to write a text using the turtle. The first method is to use turtle.write() function. It is the easier way. First we import the turtle library. import turtle Then, we set the colour and style of the text. We use turtle.write() and pass the string containing name. turtle.color('purple') style = ('Courier', 90, 'normal') turtle.write('PRATIBHA', font=style, align='center') turtle.hideturtle() This will print the name string on the turtle screen. The output is given below: To delete the drawing by the turtle use: turtle.reset() The second method requires a lot of planning and hence is a bit tedious, but all the more fun. We guide the turtle to draw. First we import the turtle library. import turtle Then we assign the turtle a new name, say 't'. t = turtle.Turtle() Then we set the size and color of the pen and move the turtle (without drawing i.e. wit t.penup() ) to a specific point from where we will start drawing. t.reset() t.pencolor('purple') t.pensize(5) t.penup() t.goto(-300,200) From here on, we will move the turtle forward, backward, right or left in such a way that we get the desired output. Below is an example to draw the letters 'P' and 'R', though various other codes can be used to produce the same result. #p t.pendown() t.fd(20) t.circle(-30, 180) t.fd(20) t.rt(90) t.fd(60) t.bk(60) t.lt(180) t.fd(60) t.penup() t.goto(-230,200) #R t.pendown() t.lt(90) t.fd(20) t.circle(-30,180) t.fd(20) t.rt(90) t.fd(60) t.bk(60) t.lt(180) t.fd(60) t.bk(60) t.lt(45) t.fd(80) t.rt(45) t.penup() t.goto(-160,200) This way we write all the other letters of the name. The output for the full name is given below: We can draw many figures, whether easy or complex using the combination of above commands. Try your hand at it, it is fun.

Cryptography is the process of converting ordinary plain text into an incomprehensible text (secret code) and vice-versa. The algorithm that is used to transform a plain text into a secret code (and vice-versa) is called Cipher. Julius Caesar, a celebrated Roman general, politician and scholar, used to correspond with his men via cryptic messages. Only he and his men knew the key to decode the messages. This ensured that his enemies won’t be able to understand the messages if by any chance they get their hands on it. The method used by Julius Caesar to conceal his messages is called Caesar Cipher. It is one of the earliest and simplest method of encoding. In this cipher, each letter (or ‘symbol’ in terms of cryptography) is shifted according to a key in a cyclic manner. The key is any number from 1 to 26, as there are 26 alphabets in English language. So, with a key equal to 2, A becomes C, B becomes D, Z becomes B and so on. An example of Caesar cipher with key equal to 3 is: Plain_text: FROG PRINCE Encrypted_text: IURJ SULQFH We now have a basic idea of Caesar Cipher. We can see that we only need a string and a key to perform encryption in python. We will write a program that asks the user to provide a file containing the plain text and the key and then encrypts the plain text and stores the result in another file. Before moving forward, there are some prerequisites that should be known, which are as follows: To represent each letter as a number called an ordinal (so that we can modify each letter to get a new letter by performing mathematical operations) we use ASCII codes. ASCII stands for American Standard Code for Information Interchange; it connects each character to a number between 32 and 126. ( Modern computers use UTF-8 instead of ASCII. But UTF-8 is backwards compatible with ASCII, so the UTF-8 ordinals for ASCII characters are the same as ASCII’s ordinals. ) The capital letters ‘A’ to ‘Z’ has ASCII value 65 to 90. The lowercase letters ‘a’ to ‘z’ has ASCII value 97 to 122. The numeric digits ‘0’ to ‘9’ has ASCII value 48 to 57. The function ord() is used to get the ASCII value of the character. >> ord('A') 65 >> ord('p') 112 >> ord(' ') 32 >> ord('2') 50 The function chr() is used to get the character from the corresponding ordinal value. >>chr(73) 'I' >>chr(55) '7' >>chr(33) '!' ENCRYPTION Let us define an algorithm to write the program code: Ask the user for the name of the file containing the plaint ext and convert the text of the file into a string. Ask the user for the value of key. Create an empty string to store the result. Identify each character in the string. Convert each character to its corresponding ASCII value using the function ord(). If the character is a space or comma or full stop, add it to the empty string. If the character is numeric, add the key to ord(character) and find how many position away it is from 48 (i.e. ‘A’) and then add it to 48. This ensures that the conversion remains cyclic. If the character is uppercase, add the key to ord(character) and find how many position away it is from 65 (i.e. ‘A’) and then add it to 65. This ensures that the conversion remains cyclic. If the character is not upper case, add the key to ord (character) and find how many position away it is from 97 (i.e. ‘a’) and then add it to 97. This ensures that the conversion remains cyclic. Convert the ordinal back to a letter character using the function char (). Add the character to the empty string to store the cryptic text. Repeat until the end of string. Store the result into a file called encrypted_text.txt. The program code is given below: def encrypt(): inp=input('Enter the name of the file :') key=int(input('Enter the key :')) handle=open('{}.txt'.format(inp),'r') lines=handle.readlines() for line in lines: line=line.strip() handle.close() cipher = '' for char in line: if char == ' ' or char =='.' or char ==',': cipher = cipher + char elif char.isnumeric(): cipher = cipher + chr((ord(char) + key - 48) % 10 + 48) elif char.isupper(): cipher = cipher + chr((ord(char) + key - 65) % 26 + 65) else: cipher = cipher + chr((ord(char) + key - 97) % 26 + 97) handle_out=open("encrypted_text.txt",'w') handle_out.write(cipher) handle_out.close() print('\nPlain text is: '+line) print('Cryptic text is: '+cipher) return cipher The output of the above code is: >>encrypt() Enter the name of the file :plain_text Enter the key :2 Plain text is: Give me a red pen of any colour. Cryptic text is: Ikxg og c tgf rgp qh cpa eqnqwt. Out[2]: 'Ikxg og c tgf rgp qh cpa eqnqwt.' DECRYPTION Similarly, we can write the function for decryption which asks the user for the name of the file containing cryptic text and the key and then decrypts the text. The program code for decryption is as follows. def decipher(): # Enter encypted_text as the file name. inp=input('Enter the name of the encrypted file.') key=int(input('Enter the key')) handle=open('{}.txt'.format(inp),'r') lines=handle.readlines() for line in lines: line=line.strip() handle.close() decipher = '' for char in line: if char == ' ' or char =='.' or char ==',' or char =='\''or char ==':' or char == '!': decipher = decipher + char elif char.isnumeric(): decipher = decipher + chr((ord(char) - key - 48) % 10 +48) elif char.isupper(): decipher = decipher + chr((ord(char) - key - 65) % 26 +65) else: decipher = decipher + chr((ord(char) - key - 97) % 26 +97) print('\nEncrypted text is: '+line) print('Decrypted text is: '+decipher) return decipher The output of the above code is: >>decipher() Enter the name of the encrypted file.encrypted_text Enter the key2 Encrypted text is: Ikxg og c tgf rgp qh cpa eqnqwt. Decrypted text is: Give me a red pen of any colour. Out[63]: 'Give me a red pen of any colour.' We can also achieve decryption by using encrypt () function, for that we provide the name of the file containing the encrypted text and the negative value of the key. Here is the example. >> encrypt() Enter the name of the file :encrypted_text Enter the key :-2 Encrypted text is: Ikxg og c tgf rgp qh cpa eqnqwt. Decrypted text is: Give me a red pen of any colour. Out[66]: 'Give me a red pen of any colour.' Have fun creating your own secret messages!

Data Structure in MapReduce Key-value pairs are the basic data structure in MapReduce: Keys and values can be: integers, float, strings, raw bytes They can also be arbitrary data structures The design of MapReduce algorithms involves: Imposing the key-value structure on arbitrary datasets E.g., for a collection of Web pages, input keys may be URLs and values may be the HTML content In some algorithms, input keys are not used (e.g., wordcount), in others the uniquely identify a record Keys can be combined in complex ways to design various algorithms Recall of Map and Reduce Map Reads data (split in Hadoop, RDD in Spark) Produces key-value pairs as intermediate outputs Reduce Receive key-value pairs from multiple map jobs aggregates the intermediate data tuples to the final output MapReduce in Hadoop Data stored in HDFS (organized as blocks) Hadoop MapReduce Divides input into fixed-size pieces, input splits Hadoop creates one map task for each split Map task runs the user defined map function for each record in the split Size of a split is normally the size of a HDFS block Data locality optimization Run the map task on a node where the input data resides in HDFS This is the reason why the split size is the same as the block size The largest size of the input that can be guaranteed to be stored on a single node If the split spanned two blocks, it would be unlikely that any HDFS node stored both blocks MapReduce in Hadoop Map tasks write their output to local disk (not to HDFS) Map output is intermediate output Once the job is complete the map output can be thrown away Storing it in HDFS with replication would be overkill If the node of the map task fails, Hadoop will automatically rerun the map task on another node Reduce tasks don’t have the advantage of data locality Input to a single reduce task is normally the output from all mappers Output of the reduce is stored in HDFS for reliability The number of reduce tasks is not governed by the size of the input, but is specified independently More Detailed MapReduce Dataflow When there are multiple reducers, the map tasks partition their output: One partition for each reduce task The records for every key are all in a single partition Partitioning can be controlled by a user-defined partitioning function Shuffle Shuffling is the process of data redistribution To make sure each reducer obtains all values associated with the same key. It is needed for all of the operations which require grouping E.g., word count, compute avg. the score for each department, Spark and Hadoop have different approaches implemented for handling the shuffles. Shuffle In Hadoop Happens between each Map and Reduce phase Use the Shuffle and Sort mechanism Results of each Mapper are sorted by the key Starts as soon as each mapper finishes Use combiner to reduce the amount of data shuffled Combiner combines key-value pairs with the same key in each par This is not handled by the framework! Shuffle in Spark Triggered by some operations Distinct, join, repartition, all * By,*ByKey I.e., Happens between stages Hash shuffle Sort shuffle Tungsten shuffle sort More on https://issues.apache.org/jira/browse/SPARK-7081 Hash Shuffle Data are hash partitioned on the map side Hashing is much faster than sorting Files created to store the partitioned data portion # of mappers X # of reducers Use consolidateFiles to reduce the # of files From M * R => E*C/T *R Pros: Fast No memory overhead of sorting Cons: Large amount of output files (when # partition is big) Sort Shuffle For each mapper 2 files are created Ordered (by key) data Index of beginning and ending of each ' Merged on the fly while being read by reducers Default way Fallback to hash shuffle if # partitions is small Pros Smaller amount of files created Cons Sorting is slower than hashing Map Reduce in Spark Transformation Narrow transformation Wide transformation Action The job is a list of Transformations followed by one Action Only action will trigger the 'real' execution I.e., lazy evaluation Transformation = Map? Action = Reduce? combineByKey RDD([K, V]) to RDD([K, C]) K: key, V: value, C: combined type Three parameters (functions) createCombiner What is done to a single row when it is FIRST met? V => C mergeValue What is done to a single row when it meets a previously reduced row? C, V => C In a partition mergeCombiners What is done to two previously reduced rows? C, C => C Across partitions The Efficiency of MapReduce in Spark Number of transformations Each transformation involves a linearly scan of the dataset (RDD) Size of transformations Smaller input size => less cost on linearly scan Shuffles data transferring between partitions is costly especially in a cluster! Disk I/O Data serialization and deserialization Network I/O Number of Transformations (and Shuffles) rdd = sc.parallelize(data) data: (id, score) pairs Bad design maxByKey= rdd.combineByKey(…) sumByKey = rdd.combineByKey(…) sumMaxRdd maxByKey.join(sumByKey) Good design sumMaxRdd=rdd.combineByKey(…) Size of Transformation rdd=sc.parallelize(data) data: (word, pairs) Bad design countRdd= rdd.reduceByKey(…) fileteredRdd countRdd.filter(…) Good design fileteredRdd = countRdd.filter(…) countRdd = fileteredRdd.reduceByKey(…) Partition rdd=sc.parallelize(data) data: (word, pairs) Bad design countRdd= rdd.reduceByKey(…) countBy2ndCharRdd=countRdd.map(…).reduceByKey(…) Good design paritionedRdd data.partitionBy(…) countBy2ndCharRdd=paritionedRdd.map(…).reduceByKey(…) How to Merge Two RDDs? Union Concatenate two RDDs Zip Pair two RDDs Join Merge based on the keys from 2 RDDs Just like join in DB Union How do A and B union together? What is the number of partitions for the union of A and B? Case 1: Different partitioner: Note: default partitioner is None Case 2: Same partitioner: Zip Key-Value pairs after A.zip(B) Key: tuples in A Value: tuples in B Assumes that the two RDDs have The same number of partitions The same number of elements in each partition E.g., 1 to 1 map Join E.g., A.*Join(B) join All pairs with matching Keys from A and B leftOuterJoin Case 1: in both A and B Case 2: in A but not B Case 3: in B but not A rightOuterJoin Opposite to leftOuterJoin fullOuterJoin Union of leftOuterJoin and rightOuterJoin MapReduce of “Strips" Map a sentence into stripes ForAll term u in sent s do: H u = new dictionary ForAll term v in Neighbors(u) do: H u (v) H u (v)+1 Reduce by key and merge the dictionaries element wise sum of dictionaries “Stripes” Analysis Advantages Far less sorting and shuffling of key value pairs Disadvantages More difficult to implement Underlying object more heavyweight Fundamental limitation in terms of size of event space Pairs vs. Stripes The pair's approach Keeps track of each pair of co-occur terms separately Generates a large number of key-value pairs (also intermediate) The benefit from combiners is limited, as it is less likely for a mapper to process multiple occurrences of a pair of words The stripe approach Keeps track of all terms that co-occur with the same term Generates fewer and shorted intermediate keys The framework has less sorting to do Greatly benefits from combiners, as the keyspace is the vocabulary More efficient, but may suffer from a memory problem MapReduce in Real World: Search Engine Information retrieval (IR) Focus on textual information (= text/document retrieval) Other possibilities include image, video, music,...... Boolean Text retrieval Each document or query is treated as a bag ” of words or terms. Word sequence is not considered Query terms are combined logically using the Boolean operators AND, OR, and NOT. E.g., ((data AND mining) AND (NOT text)) Retrieval Given a Boolean query, the system retrieves every document that makes the query logically true Exact match Contact us to get help related to "map-reduce assignment help", "map-reduce project help", "map-reduce homework help" or other project topics related to MapReduce at: contact@codersarts.com

What is topic modeling? Topic modeling is a method for unsupervised classification of documents, similar to clustering on numeric data, which finds some natural groups of items (topics) even when we’re not sure what we’re looking for. LDA MODEL: In more detail, LDA represents documents as mixtures of topics that spit out words with certain probabilities. It assumes that documents are produced in the following fashion: when writing each document, you Decide on the number of words N the document will have (say, according to a Poisson distribution). Choose a topic mixture for the document (according to a Dirichlet distribution over a fixed set of K topics). For example, assuming that we have the two food and cute animal topics above, you might choose the document to consist of 1/3 food and 2/3 cute animals. Generate each word w_i in the document by: First picking a topic (according to the multinomial distribution that you sampled above; for example, you might pick the food topic with 1/3 probability and the cute animals topic with 2/3 probability). Using the topic to generate the word itself (according to the topic’s multinomial distribution). For example, if we selected the food topic, we might generate the word “apple” with a 30% probability, “mangoes” with 15% probability, and so on. Assuming this generative model for a collection of documents, LDA then tries to backtrack from the documents to find a set of topics that are likely to have generated the collection. Let's Understand by some implementation: For this, The google Colab platform is used. from google.colab import drive drive.mount('/content/drive') O/p: Drive already mounted at /content/drive Here in the first step itself, Mount/IMport your Google Drive to the colab. In the above piece of code, the drive has already mounted. loc = '/content/drive/My Drive/ldaa/lda/data.csv' Give the location of the CSV file. The next step is importing the libraries: from sklearn.feature_extraction.text import CountVectorizer from sklearn.decomposition import LatentDirichletAllocation as LDA import numpy as np import pandas as pd import re import matplotlib.pyplot as plt import seaborn as sns sns.set_style('whitegrid') The libraries like Numpy, Pandas, matplotlib, seaborn has been imported with this from sklearn.feature_extraction and sklearn. Decomposition CountVectorizer and LDA have been Imported. Step:- 2 df = pd.read_csv(loc) print(df.head()) print(df.columns) # Remove punctuation df['processed'] = df['text'].map(lambda x: re.sub('[,\.!?]', '', x)) # Convert the titles to lowercase df['processed'] = df['processed'].map(lambda x: x.lower()) print(df['processed'].head()) cv = CountVectorizer(stop_words='english') # Fit and transform the processed titles cv_data = cv.fit_transform(df['processed']) def print_topics(model, count_vectorizer, n_top_words): words = count_vectorizer.get_feature_names() for topic_idx, topic in enumerate(model.components_): print("\nTopic #%d:" % topic_idx) print(" ".join([words[i] for i in topic.argsort()[:-n_top_words - 1:-1]])) In this step on the first line itself tells us that the file has been read through pandas. Then the preprocessing steps such as Removing Punctuations, Removing stopwords are done, and then in the next step, the data has been encoded into a vectorized format. In the next step, we have to import guidelda library import guidedlda This libary is not pre-installed in google colab so we have to first install it on colab. By this below syntax ! pip install guidelda top= ['glassdoor_reviews','tech_news','room_rentals','sports_news','automobiles'] let's analyze a list top with some words. vocab = cv.vocabulary_ Create a vocabulary and store it on vocab. word2id=dict((v,idx)foridx,vinenumerate(vocab)) Then put the vocab in a dictionary. model = guidedlda.GuidedLDA(n_topics=5, n_iter=100, random_state=7, refresh=20) Then load a model with LDA Library with the parameter of n_topics=5, with iterations of 100 and the random_state=7 model.fit(cv_data) INFO:guidedlda:n_documents: 6248 INFO:guidedlda:vocab_size: 22671 INFO:guidedlda:n_words: 228181 INFO:guidedlda:n_topics: 5 INFO:guidedlda:n_iter: 100 /usr/local/lib/python3.6/dist-packages/guidedlda/utils.py:55: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`. if sparse and not np.issubdtype(doc_word.dtype, int): INFO:guidedlda:<0> log likelihood: -2559963 INFO:guidedlda:<20> log likelihood: -1975941 INFO:guidedlda:<40> log likelihood: -1927156 INFO:guidedlda:<60> log likelihood: -1901394 INFO:guidedlda:<80> log likelihood: -1891037 INFO:guidedlda:<99> log likelihood: -1888564 model.fit(cv_data, seed_topics=top, seed_confidence=0.15) INFO:guidedlda:n_documents: 6248 INFO:guidedlda:vocab_size: 22671 INFO:guidedlda:n_words: 228181 INFO:guidedlda:n_topics: 5 INFO:guidedlda:n_iter: 100 /usr/local/lib/python3.6/dist-packages/guidedlda/utils.py:55: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`. if sparse and not np.issubdtype(doc_word.dtype, int): INFO:guidedlda:<0> log likelihood: -2559963 INFO:guidedlda:<20> log likelihood: -1975941 INFO:guidedlda:<40> log likelihood: -1927156 INFO:guidedlda:<60> log likelihood: -1901394 INFO:guidedlda:<80> log likelihood: -1891037 INFO:guidedlda:<99> log likelihood: -1888564 doc_topic = model.transform(cv_data) doc_topic[0] In this step, we have printed the first row. O/p: array([2.19152497e-01, 3.02792582e-03, 7.77457222e-01, 2.06843509e-04, 1.55511855e-04]) let's print the length of the document topic. len(doc_topic) O/p: 6248 doc_topic[20] O/p: array([2.62679941e-01, 5.61569637e-04, 2.60673256e-03, 7.32870579e-01, 1.28117723e-03]) Let's create a data frame on doc_topic. proba = pd.DataFrame(doc_topic) proba.columns = top Then merge this Proba data frame with the left_index op =df.merge(proba,left_index= True,right_index =True) Let's Check the head part op.head() Then Save the file to the drive. op.to_csv('/content/drive/My Drive/ldaa/lda/op2.csv') So in this way, we can build the LDA Model. For code Refer this link: https://github.com/kapuskaFaizan/NLP-jupyter_notebook/blob/master/LDAGuided_2.ipynb Thank You For Reading! Happy Learning.

Table of content: Word count output the number of occurrences for each word in the dataset. Naïve solution: word_count (D): H = new dict For each w in D: H[w]+= 1 For each w in H: print(w, H[w]) How to speed up? Make use of multiple workers There are some problems… Data reliability Equal split of data Delay of worker Failure of worker Aggregation the result We need to handle them all! In the traditional way of parallel and distributed processing. MapReduce MapReduce is a programming framework that: allows us to perform distributed and parallel processing on large data sets in a distributed environment no need to bother about the issues like reliability, fault tolerance etc offers the flexibility to write code logic without caring about the design issues of the system MapReduce consists of Map and Reduce Map Reads a block of data Produces key-value pairs as intermediate outputs Reduce Receive key-value pairs from multiple map jobs aggregates the intermediate data tuples to the final output A Simple MapReduce Example Pseudo Code of Word Count Map(D): for each w in D: emit(w, 1) Reduce(t, counts): # e.g., bear, [1, 1] sum = 0 for c in counts: sum = sum + c emit (t, sum) Advantages of MapReduce Parallel processing Jobs are divided to multiple nodes Nodes work simultaneously Processing time reduced Data locality Moving processing to the data Opposite from the traditional way Motivation of Spark MapReduce greatly simplified big data analysis on large, unreliable clusters. It is great at one pass computation. But as soon as it got popular, users wanted more: more complex, multi-pass analytics (e.g. ML, graph) more interactive ad hoc queries more real-time stream processing Limitations of MapReduce As a general programming model: more suitable for one-pass computation on a large dataset hard to compose and nest multiple operations no means of expressing iterative operations As implemented in Hadoop: all datasets are read from disk, then stored back on to disk all data is (usually) triple replicated for reliability Data Sharing in Hadoop MapReduce Slow due to replication, serialization, and disk IO Complex apps, streaming, and interactive queries all need one thing that MapReduce lacks: Efficient primitives for data sharing What is Spark? Apache Spark is an open-source cluster computing framework for real-time processing. Spark provides an interface for programming entire clusters with implicit data parallelism fault tolerance Built on top of Hadoop MapReduce extends the MapReduce model to efficiently use more types of computations Spark Features Polyglot Speed Multiple formats Lazy evaluation Real-time computation Hadoop integration Machine learning Spark Eco-System: Spark Architecture Master Node takes care of the job execution within the cluster Cluster Manager allocates resources across applications Worker Node executes the tasks Spark Resilient Distributed Dataset(RDD) RDD is where the data stays RDD is the fundamental data structure of Apache Spark is a collection of elements Dataset can be operated on in parallel Distributed fault-tolerant Resilient Features of Spark RDD In-memory computation Partitioning Fault tolerance Immutability Persistence Coarse-grained operations Location stickiness Create RDDs Parallelizing an existing collection in your driver program Normally, Spark tries to set the number of partitions automatically based on your cluster Referencing a dataset in an external storage system HDFS, HBase, or any data source offering a Hadoop InputFormat By default, Spark creates one partition for each block of the file RDD Operations Transformations functions that take an RDD as the input and produce one or many RDDs as the output Narrow Transformation Wide Transformation Actions RDD operations that produce non RDD values. returns the final result of RDD computations Narrow and Wide Transformations Narrow transformation involves no data shuffling map flatMap filter sample Wide transformation involves data shuffling sortByKey reduceByKey groupByKey join Action Actions are the operations which are applied on an RDD to instruct Apache Spark to apply computation and pass the result back to the driver collect take reduce forEach sample count save Lineage RDD lineage is the graph of all the ancestor RDDs of an RDD Also called RDD operator graph or RDD dependency graph Nodes: RDDs Edges: dependencies between RDDs Fault tolerance of RDD All the RDDs generated from fault-tolerant data are fault-tolerant. If a worker falls, and any partition of an RDD is lost the partition can be recomputed from the original fault-tolerant dataset using the lineage the task will be assigned to another worker DAG in Spark DAG is a direct graph with no cycle Node: RDDs, results Edge: Operations to be applied on RDD On the calling of Action, the created DAG submits to DAG Scheduler which further splits the graph into the stages of the task DAG operations can do better global optimization than other systems like MapReduce DAG, Stages, and Tasks DAG Scheduler splits the graph into multiple stages Stages are created based on transformations The narrow transformations will be grouped together into a single-stage Wide transformation defines the boundary of 2 stages DAG scheduler will then submit the stages into the task scheduler The number of tasks depends on the number of partitions The stages that are not interdependent may be submitted to the cluster for execution in parallel Lineage vs. DAG in Spark They are both DAG (data structure) Different end nodes Different roles in Spark Contact us to get help by our Big data experts at: contact@codersarts.com

Apache Hadoop is an open-source software framework that Stores big data in a distributed manner Processes big data parallelly Builds on large clusters of commodity hardware Based on Google’s papers on Google File System (2003) and MapReduce (2004) Hadoop is: Scalable to Petabytes or more easily(Volume) Offering parallel data processing(Velocity) Storing all kinds of data(Variety) Hadoop offers: Redundant, Fault-tolerant data storage (HDFS) Parallel computation framework (MapReduce) Job coordination/scheduling (YARN) Programmers no longer need to worry about: Where the file is located? How to handle failures & data lost? How to divide computation? How to program for scaling? Hadoop Ecosystem Core of Hadoop: Hadoop distributed file system ( MapReduce YARN (Yet Another Resource Negotiator) (from Hadoop v2.0) Additional software packages: Pig Hive Spark HBase ........ The Master-Slave Architecture of Hadoop Hadoop Distributed File Systems(HDFS) HDFS is a file system that follows master slave architecture allows us to store data over multiple nodes(machines) allows multiple users to access data. just like file systems in your PC HDFS supports distributed storage distributed computation horizontal scalability Vertical Scaling vs. Horizontal Scaling HDFS Architecture NameNode NameNode maintains and manages the blocks in the DataNodes (slave nodes). Master node Functions: records the metadata of all the files FsImage: file system namespace since our name node is started It records all changes EditLogs: all the recent modifications e.g for the past 1 hour records each change to the metadata regularly checks the status of data nodes keeps a record of all the blocks in HDFS if the DataNode fails, handle data recovery DataNode A commodity hardware stores the data Slave node Functions stores actual data performs the read and write requests reports the health to NameNode (heartbeat) NameNode vs. DataNode If NameNode failed… All the files on HDFS will be lost there’s no way to reconstruct the files from the blocks in DataNodes without the metadata in NameNode In order to make NameNode resilient to failure back up metadata in NameNode (with a remote NFS mount) Secondary NameNode Secondary NameNode Take checkpoints of the file system metadata present on NameNode It is not a backup NameNode! Functions: Stores a copy of FsImage file and Editlogs Periodically applies Editlogs to FsImage and refreshes the Editlogs If NameNode is failed, File System metadata can be recovered from the last saved FsImage on the Secondary NameNode NameNode vs. Secondary NameNode Blocks Block is a sequence of bytes that stores data Data stores as a set of blocks in HDFS Default block size is 128M eta B ytes (Hadoop 2.x and 3.x) x), the default size in hadoob is 64 metab bytes A file is spitted into multiple blocks Why Large Block Size? HDFS stores huge datasets If block size is small (e.g., 4KB in Linux), then the number of blocks is large: too much metadata for NameNode too many seeks affect the read speed read speed=seek time+transfer time tranfer time=total size of file/transportation speed harm the performance of MapReduce too We don’t recommend using HDFS for small files due to similar reasons. If DataNode Failed… Commodity hardware fails If NameNode hasn’t heard from a DataNode for 10mins, The DataNode is considered dead… HDFS guarantees data reliability by generating multiple replications of data each block has 3 replications by default replications will be stored on different DataNodes if blocks were lost due to the failure of a DataNode, they can be recovered from other replications the total consumed space is 3 times the data size It also helps to maintain data integrity (whether The data stored is correct or not File, Block and Replica A file contains one or more blocks Blocks are different Depends on the file size and block size A block has multiple replicas Replicas are the same Depends on the preset replication factor Replication Management Each block is replicated 3 times and stored on different DataNodes Why default replication factor 3? If 1 replicate DataNode fails, block lost Assume # of nodes N = 4000 # of blocks R = 1,000,000 Node failure rate FPD = 1 per day u expect to see 1 machine fail per day If one node fails, then R/N = 250 blocks are lost E(# of losing blocks in one day) 250 Let the number of losing blocks follows Poisson distribution, then Pr[# of losing blocks in one day >= 250] = 0.508 If you are looking more about Hadoop or looking for project assignment help related to Hadoop, big data, spark, etc. then you can send the details of your requirement at below contact id: contact@codersarts.com

Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Q1. HDFS Let N be the number of DataNodes and R be the total number of blocks in the DataNodes. Assume the replication factor is 5, and k out of N DataNodes have failed simultaneously. 1. Write down the formula of Li(k,N) for i 2 {1, . . . , 5}, where Li(k,N) is the number of blocks that have lost i replicas. 2. Let N = 500, R = 20, 000, 000, and k = 200. Compute the number of blocks that cannot be recovered under this scenario. You need to show both the steps and the final result to get full credit. Q2. Spark Consider the following PySpark code snippet: raw_data = [("Joseph", "Maths", 83), ("Joseph", "Physics", 74), ("Joseph", "Chemistry", 91), ("Joseph", "Biology", 82), ("Jimmy", "Maths", 69), ("Jimmy", "Physics", 62), ("Jimmy", "Chemistry", 97), ("Jimmy", "Biology", 80), ("Tina", "Maths", 78), ("Tina", "Physics", 73), ("Tina", "Chemistry", 68), ("Tina", "Biology", 87), ("Thomas", "Maths", 87), ("Thomas", "Physics", 93), ("Thomas", "Chemistry", 91), ("Thomas", "Biology", 74)] rdd_1 = sc.parallelize(raw_data) rdd_2 = rdd_1.map(lambda x:(x[0], x[2])) rdd_3 = rdd_2.reduceByKey(lambda x, y:max(x, y)) rdd_4 = rdd_2.reduceByKey(lambda x, y:min(x, y)) rdd_5 = rdd_3.join(rdd_4) rdd_6 = rdd_5.map(lambda x: (x[0], x[1][0]+x[1][1])) rdd_6.collect() 1. Write down the expected output of the above code snippet. 2. List all the stages in the above code snippet. 3. What makes the above implementation inefficient? How would you modify the code and improve the performance? Q3: LSH Consider a database of N = 1, 000, 000 images. Each image in the database is pre-processed and represented as a vector o 2 Rd. When a new image comes as a query, it is also processed to form a vector q 2 Rd. We now want to check if there are any duplicates or near-duplicates of q in the database. Specifically, an image o is a near duplicate to q if cos((o, q))  0.9. We want to find any near-duplicate with a probability of no less than 99%. We now design an LSH scheme using SimHash to generate candidate near duplicates. Assume that for query q, there are 100 images that are near-duplicate to q. 1. Assume k = 5, how many tables does the LSH scheme require (i.e., L) to ensure that we can find any near-duplicate with probability no less than 99%? 2. Consider image o with cos((o, q)) < 0.8, k = 5 and L = 10. What is the maximum value of the probability of o to become a false positive of query q? You need to show the intermediate steps along with the final result to get full credit. If you need a solution to these questions or other help related to Big Data, HDFS, SPARK, LSH, etc., then you can contact us at the given link: Codersarts You can send the details of your requirements at: contact@codersarts.com

Computer vision is a sub-field of Artificial Intelligence that helps machines to understand visual representation by analyzing digital images. This is achieved by training different deep learning algorithms on image datasets(or video) to recognize different patterns in the image pixels. There by giving them ability to see and accurately identify objects and classify them into a certain category. In simpler words computer vision includes a set of algorithms which help machines see the real world and make decisions based on their vision. Transfer learning is an active research topic in the field of machine learning. As the name suggests transfer learning is the art of conserving the amount of knowledge gained in solving one problem and then applying this saved knowledge to a new problem and doing a better job at it. This is a very important break through in the field machine learning and data sciences as data is the only fuel that powers machine learning algorithm. With the help of transfer learning even if the data is less we can still acquire the best results.This reduces the need for data related to the specific task we are dealing with. This reduces the need for data related to the specific task we are dealing with. For this an example would be suppose a model which was earlier used for recognising bicycles will now recognise bikes. The image above clearly show how Transfer Learning actually works. We can see that in the second step same model which was used to create solution for task 1 has being transferred to solve task two. This way the model before starting on problem second already has some prior knowledge about it. Also it should be kept in mind that Data2 in this case represents a very small amount of new data that the model hasn't seen yet, kinda like the test set for this. Different research teams across the world have come up with multiple state of art models for the purpose of performing transfer learning only and the best thing about this is these research groups have made most of there models as open source, so anybody with slight understanding of the subject matter can download these models and you it for there own purposes. Transfer learning can also be applied to NLP problems just as easily it is applied to computer vision problems, but that is beyond the scope of this post so we will leave that aside for now. Some of the most prominent pre-trained model used for transfer learning in computer vision are listed below : Alexnet vgg16-19 ResNet GoogLeNet SqeezeNet InceptionV3, ResNet, MobileNet, Xception, InceptionResNetV2 Densenet Facenet Deep Transfer Learning Deep learning as attained enormous amount of fame in the past decade. With the help of this technique we are now able to deal with complex problems which seemed to be impossible to the previously. The only draw back the previously used deep learning model had was the needed data in large quantities in order to over come the problems at hand. However with the advent of transfer learning , deep learning has just pasted it biggest milestone. There are many deep learning models with top-of-class performance that have been developed and tested across domains such as computer vision and natural language processing. These models are trained on large data sets once the training is complete the model weights and state are saved. These same models could then be used in collaboration with a new simple model in order to get better results on lesser amounts of data. The pre-trained model in this case becomes more of a feature extractor then a classifier or regressor. Once the features are successfully extracted then a new model with simple architecture is then used. This new model takes input the features extracted by the pre-trained model and makes predictions. The following image shows the clear criterion between a traditional deep learning techniques and Transfer Learning techniques. One should notice in the above image that the target variable in case of transfer learning, contains a label set which is a sub set of the label set the pre trained model was originally trained on. Some of the most commonly used frameworks for transfer learning include : Pytorch Keras Tensorflow theano Pillow numpy pandas openCV jupyter notebook Few applications of computer vision with transfer learning are given below: Real world Simulations gaming Image Classification Zero Short Translation Text classification Sentiment Analysis Face Recognition Object Recongnition Gesture Recognition Object tracking Feel free to contact us on contact@codersarts.com in order to get any kind of assignment help | project guidance on the above mentioned topics.

Scope of UX DESIGN in INDIA In 1940, Toyota developed its famous human-centered production system. This represents a key step in UX history as it really brought attention to the importance of how humans interact with machines. In 1955, Henry Dreyfuss, an American industrial engineer who was known for designing and improving the usability of some of the most iconic consumer products like the Hoover vacuum cleaner, the tabletop telephone, and the Royal Typewriter Company’s Quiet DeLuxe model. During the 1960s Walt Disney was often considered as the key player to play a vital role in UX history. Building Disneyland to provide its users with a great real-world experience is nothing but the true User Experience. Disney is also considered as one among those who introduced UX designing. UI/UX designs are two different things often used interchangeably. Both are crucial for an IT product/service. But in reality, they are very different from each other. UI (User Interface) design is a part of the User Experience (UX) process Career Scopes in UI/UX Design: Career Scopes in UI/UX Design: When someone asks me “what’s the scope of the UI/UX Design in India?” My simple answer would be: “MASSIVE” Before 2013 India was not that aggressive when it comes to the digital presence. But from the last 5 years, we saw things changed drastically. In today’s digital world, a brand that does not exist on the web practically doesn’t exist for the customers. And when on the web, what becomes the face of the brand? Its website or Mobile App. So App or website will practically represent the brand and it’ll build connections between the consumer and the brand, so having a good user experience becomes very crucial. Hence, the scope for UI/UX professionals in this field is immense .How to become a UI/UX designer in India: However, a bachelor’s degree in Design (B.Des), especially with a specialization in UI/UX or Interaction Design will give you a thorough grounding in the design principles and UI/UX basics needed to step into this field. Alternatively, you could pursue an undergraduate degree in Computer Science (with a specialization in Human-Computer Interaction), Media Science (with a specialization in Interactive Media) or Liberal Arts (Major in Psychology with Minor/Electives such Visual Arts, Design or Marketing).In addition to the degree, you will need to gain proficiency in design software such as Adobe Photoshop, Corel Draw, InDesign, etc. The understanding of your work and the years of your experience help you get the spotlight and make career advancement. Additional knowledge of web programming languages like HTML, XML, CSS, and Java will also be helpful too. Skills and Tools required for A Career in UI/UX Design: As mentioned earlier, UI and UX designers have clearly different roles. They both need information from each other to complete their tasks. For example, For example, the UI designer needs customer data and prototypes from the UX designer. On the other hand, the UX designer needs to understand the design limitations of the UI designer before creating those prototypes. As of yet, there is no defined path to become a UI/UX designer. However, a bachelor’s degree in design (B.Des), especially with a specialisation in UI/UX or Interaction Design will give you a thorough grounding in the design principles and UI/UX basics needed to step into this field. Some professionals also enter this field after pursuing computer science courses. In addition to the degree, you will need to gain proficiency in design software such as Adobe Photoshop, Corel Draw, InDesign, etc. The understanding of your work and the years of your experience help you get the spotlight. Additional knowledge of web programming languages like HTML, XML, CSS, and Java will also be helpful too. Institutes that offer courses in UI/UX Design A few colleges in India that provide a bachelor’s in graphic, interface and industrial design are: Ø NID (National Institute of Design), Ahmedabad Ø Industrial Design Centre, Mumbai Ø Pearl Academy, Multiple Locations Ø NIFT, New Delhi Ø MAEER’S MIT Institute of Design, Pune Ø Indian School of Design and Innovation, Mumbai Ø DOD (Department of Design), IIT Guwahati A few institutes abroad that offer UI/UX design courses are: Ø Springboard’s User Experience Design Ø Interaction Design Foundation Ø University of California at San Diego’s extension school offers an online distance learning Certificate in User Experience Design. Ø NYC’s Pratt Institute offers a Certificate Program in UX/UI Mobile Design Ø Integrated Digital Media program at NYU’s Tandon School of Engineering. In addition to these, a lot of short-term courses and certifications are also available online through which can learn the basics or advanced skills in UI/UX design. What is the future like for UI/UX designers in India? With an abundance of businesses in the marketplace, companies need to stay at the top of their game to stand a chance to survive in this competitive market. A well-executed user interface and experience design gives them that edge. By utilizing talent in the field of UI/UX design, JHUK companies are building user-friendly apps and websites to meet user’s needs. On some websites, it is much easier to locate and buy a product of your choosing, while on others it is harder. Which one are you likely to use the next time? That is the difference UI/UX design creates in enhancing a user’s experience on a website. Having realized this, companies are allocating greater resources and budgets to focus on the UI/UX of their websites and apps, and that need will be met by UI/UX designers. Thus, the future holds a lot of potential for anyone with right zeal, creativity and eye for detail. So, if you feel you have what it takes, it’s time that you pause and think about UI/UX design as your career. Probably you had been sitting and surfing websites looking for an appropriate career and it was right in front of you! Conclusion: UX as a field has grown exponentially in the last 10 years and it continues to grow as one of the high Job generating industries in the coming years. Study more, gather more knowledge and be sure whether you’re the right fit for this industry or not.