Build an online project portfolio with your project code and video explaining your project. This is shared with recruiters.
You will be working on real case studies and solving real world problems. Assignments will be given to get you familiarized with numerous libraries in R which are used by Data Scientists for data analysis.
Once you enroll for a batch, you are welcome to participate in any future batches free. If you have any doubts, our support team will assist you in clearing your technical doubts.
You will get 6 one-on-one meetings with an experienced Data Scientist architect who will act as your mentor.
The most important interview question you will get asked is "What experience do you have?". Through the ProjectPro live classes, you will build projects, that have been carefully designed in partnership with companies.
The same companies that contribute projects to ProjectPro also recruit from us. You will build an online project portfolio, containing your code and video explaining your project. Our corporate partners will connect with you if your project and background suit them.
Every few weeks there is a new technology release in Big Data. We organise weekly hackathons through which you can learn these new technologies by building projects. These projects get added to your portfolio and make you more desirable to companies.
For any doubt clearance, you can use:
In the last module, ProjectPro faculty will assist you with:
R is one of the most prominent and powerful tools - that is used to extract, clean and build models on a huge amount of data and it has been used in all major companies by leading data scientists. It is one of the easiest tools to learn and implement, for data analysis and it is required that one should know R programming in order to get a job in the field of data analysis.
The pre-requisites to learn Data Science in R is pretty straightforward. You need to have a strong aptitude for numbers, basic programming exposure and college level mathematics mastery.
All the faculty are leading Data Scientists in multi national analytics firms. They have all been approved to teach Data Science at ProjectPro, after going through a series of stringent tests. So you can be assured that whatever you are learning is cutting edge and industry relevant.
We will begin the course by covering basic syntax in R programming like - small programs to handle data, basic statistical concepts and then move on to different statistical methods to drive or summarize the data to get conclusions. The next level will be to implement all the statistical concepts in R - to solve data analysis problems. The last level will be implementing machine learning techniques to solve real industry problems.
Big Data is the term used to refer to high volume of data, that can be generated from various sources and in different formats. Big Data are often complex and large enough to be processed by traditional database management techniques. Data Science is term which refers to the discipline of analyzing the data. A data scientist creates knowledge out of the data using traditional and non-traditional tools and techniques.
Business analytics is usually followed by Data Science applications. A Business analyst gathers insight from the previous business performance and results obtained by data analytics.
Suppose you have the following data type:
Name | Value | |
1 | A | 10 |
2 | B | 12 |
3 | C | 11 |
4 | D | 13 |
and so on. The objective is to calculate mean and variance of the data set in R. There are various commands which can used to do the job:
with (df, tapply(Value, Name, function(x) c(mean(x), var(x))))
or
aggregate(Value ~ Name, df, function (x) c(mean(x), var(x)))
or
do.call(rbind, by(df, df$Name, function(x) c(mean(x$Value), var(x$Value))))
or
library(data.table) setDT(df)[, list(Var=var(value), Mean = mean(value), by = Name)]
A linear regression in R can be performed using either lme4 package or the plyr package or the nlme approach. For a data set which has multiple vectors, a mixed linear model will be a better approach.
**
library(nlme) lme(response ~ vector1, random = ~vector1|state1, correlation = corAR1(~vector1))
**
require(base) library(base) attach(data) # data = your data base #state is your label for the states column modell<-by(data, data$state, function(data) lm(y~I(1/var1)+I(1/var2))) summary(modell) **
An activation function converts the weighted inputs of nodes in a Neural Network to its output activation. There are various activation functions used with Neural Networks, below mentioned is a list of few:
Consider a list which contains different length vectors, which needs to be converted to Data.Frame. For example, here is input for a list with two columns with unequal length vectors:
SampleList <- list(A=c(1,2,3),B=c(1,2,3,4,5,6))
There are quite a few ways to accomplish this conversion, some of the notable ones are mentioned below:
data.frame(lapply(SampleList, "length<-", max(lengths(SampleList))))
or
ListToDataFrame <- function(SampleList){ sapply(SampleList, "length<-", max(lenghts(SampleList))) }
Then the following command can be executed for any list by simply calling the function.
ListToDataFrame(SampleList)
Consider a random string input with recurring characters. The objective of this task is to count the consecutive patterns and print them along with the character of the repetitive strings. This can be done in R using the dplyr library. The code for which is mentioned below:
library(dplyr)
library(stringi)
library(tidyr)
//strings input = "Z,Z,Z,Y,X,X,W,W"
data_frame(test_string = input) %>%
group_by(test_string) %>%
do(.$test_stringstring %>%
first %>%
stri_split_fixed(",") %>%
first %>%
rle %>%
unclass %>%
as.data.frame) %>%
summarize(output_string = paste(lengths, values, collapse = " , "))
In simple terms, back-propagation learning for Neural networks is gradient descent method. In this method, random weights are initialized to the nodes of the neural network.
Forward propagation through the layers is done to save the output of each layer. Then an error variable is calculated by computing the difference between desired output and actual output. Then the model is back propagated to find the error at each layer, weights adn bias of each layers are updated to minimize the error at each layer. The same process is repeated till the error variable reaches the threshold.
Multi-layer feed forward networks of Artificial Neural Networks are comparable to Support Vector Machines. The clear benefit for these models over SVM is the fact that these are parametric models with fixed node size, while SVM's are non-parametric. Any artificial Neural Network is made up of multiple hidden layers with variable number of nodes and bias parameters depending upon number of features. On the other hand, an SVM is consisted of a set of support vectors with assigned weights calculated from training set.
One of the key advantages of Neural Networks is that they have multiple outputs, whereas any SVM will only produce one output. Therefore to create an n-ary classifier with SVM, we need to create n SVMs and train them separately; while n-ary classifier using a Neural Network can be trained in a single instance.
SD is a data.table containing the subset of data for every group, excluding the columns of group. It is to be used when grouping by 'i', when keying by 'by', grouping by 'by' and ad hoc 'by'.
.SD stands for 'Subset of Data.Table'. The full stop in the beginning of character SD is to avoid match with any user-defined column name.
Consider a data.table:
DT = data.table(x=rep(c("a","b","c"),each=2), y=c(1,3), v=1:6)
setkey(DT, y)
DT
# y x v
# [1,] 1 a 1
# [2,] 1 b 3
# [3,] 1 c 5
# [4,] 3 a 2
# [5,] 3 b 4
# [6,] 3 c 6
Instead of this, you can use .SD :
DT[, .SD[,paste(x,v, sep="", collapse="_")], by=y]
# y V1
# [1,] 1 a1_b3_c5
# [2,] 3 a2_b4_c6
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Travel to other Axios Locations or Customer Sites as necessary
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