This Data Science training will prepare the students to handle the multidisciplinary field and be ready for the industry.
Become a Data Scientist by joining experts designed Data Science training. This Data Science course will provide you end to end skills to manage real-world data science operations. In training our experienced trainer will help you learn the concepts such as data analysis, connecting R with Hadoop framework, R statistical computing, Machine Learning algorithms, Naïve Bayes, K-Means Clustering, business analytics, etc. During the data science online course you will also work with real-time project implementation processes. Get certified in Data Science certification course by joining SK trainings.
Data science is a multidisciplinary field which uses scientific methods, algorithms, processes, and systems to gain insights from the structured, semi-structured and unstructured data. The main intention behind data science is to get the hidden insights out of large sets of data thereby helping the corporates, governments in taking valid decisions. It uses various methods and strategies drawn from diversified fields such as Statistics, Mathematics, Information Science and Computer Science.
SK trainings has designed this online data science course to make you fundamentally strong in the areas such as Statistical Methods, Data Analytics, Data Acquisition, project life cycle, Machine Learning, and much more. Get the best data science certification training from SK trainings.
Following are the professionals who can enhance their skills by joining this online Data Science training.
Following are the various job roles available for a SCCM professional:
The average compensation received by a data scientist in India and US is ₹853,191.& US$112,957 respectively.
As such there are no special qualifications required to take up this online data science training. You can join directly and start learning this course. It is an added advantage if you are good at mathematics.
Following are some of the top companies which are hiring Data science professionals
Yes, you will receive a Data Science course completion certificate form SK trainings at the end of the training. This certificate is valid across all the top organizations and simplifies your job search.
Introduced to Hypothesis testing, various Hypothesis testing Statistics, understand what is Null Hypothesis, Alternative hypothesis and types of hypothesis testing
Selection bias is a kind of error that occurs when the researcher decides who is going to be studied. It is usually associated with research where the selection of participants isn’t random. It is sometimes referred to as the selection effect. It is the distortion of statistical analysis, resulting from the method of collecting samples. If the selection bias is not taken into account, then some conclusions of the study may not be accurate.
The types of selection bias include:
1. Sampling bias: It is a systematic error due to a non-random sample of a population causing some members of the population to be less likely to be included than others resulting in a biased sample.
2. Time interval: A trial may be terminated early at an extreme value (often for ethical reasons), but the extreme value is likely to be reached by the variable with the largest variance, even if all variables have a similar mean.
3. Data: When specific subsets of data are chosen to support a conclusion or rejection of bad data on arbitrary grounds, instead of according to previously stated or generally agreed criteria.
4. Attrition: Attrition bias is a kind of selection bias caused by attrition (loss of participants) discounting trial subjects/tests that did not run to completion.
In the wide-format, a subject’s repeated responses will be in a single row, and each response is in a separate column. In the long-format, each row is a one-time point per subject. You can recognize data in wide format by the fact that columns generally represent groups.
It is a hypothesis testing for a randomized experiment with two variables A and B. The goal of A/B Testing is to identify any changes to the web page to maximize or increase the outcome of interest. A/B testing is a fantastic method for figuring out the best online promotional and marketing strategies for your business. It can be used to test everything from website copy to sales emails to search ads An example of this could be identifying the click-through rate for a banner ad.
Probability of not seeing any shooting star in 15 minutes is
= 1 – P( Seeing one shooting star ) = 1 – 0.2 = 0.8 Probability of not seeing any shooting star in the period of one hour = (0.8) ^ 4 = 0.4096 Probability of seeing at least one shooting star in the one hour = 1 – P( Not seeing any star ) = 1 – 0.4096 = 0.5904
Point Estimation gives us a particular value as an estimate of a population parameter. Method of Moments and Maximum Likelihood estimator methods are used to derive Point Estimators for population parameters.
A confidence interval gives us a range of values which is likely to contain the population parameter. The confidence interval is generally preferred, as it tells us how likely this interval is to contain the population parameter. This likeliness or probability is called Confidence Level or Confidence coefficient and represented by 1 — alpha, where alpha is the level of significance.
When you perform a hypothesis test in statistics, a p-value can help you determine the strength of your results. p-value is a number between 0 and 1. Based on the value it will denote the strength of the results. The claim which is on trial is called the Null Hypothesis.
Low p-value (≤ 0.05) indicates strength against the null hypothesis which means we can reject the null Hypothesis. High p-value (≥ 0.05) indicates strength for the null hypothesis which means we can accept the null Hypothesis p-value of 0.05 indicates the Hypothesis could go either way. To put it in another way,
High P values: your data are likely with a true null. Low P values: your data are unlikely with a true null.
There are two ways of choosing the coin. One is to pick a fair coin and the other is to pick the one with two heads.
Probability of selecting fair coin = 999/1000 = 0.999 Probability of selecting unfair coin = 1/1000 = 0.001 Selecting 10 heads in a row = Selecting fair coin * Getting 10 heads + Selecting an unfair coin P (A) = 0.999 * (1/2)^5 = 0.999 * (1/1024) = 0.000976 P (B) = 0.001 * 1 = 0.001 P( A / A + B ) = 0.000976 / (0.000976 + 0.001) = 0.4939 P( B / A + B ) = 0.001 / 0.001976 = 0.5061 Probability of selecting another head = P(A/A+B) * 0.5 + P(B/A+B) * 1 = 0.4939 * 0.5 + 0.5061 = 0.7531
Resampling is done in any of these cases:
In the case of two children, there are 4 equally likely possibilities BB, BG, GB and GG; where B = Boy and G = Girl and the first letter denotes the first child. From the question, we can exclude the first case of BB. Thus from the remaining 3 possibilities of BG, GB & BB, we have to find the probability of the case with two girls. Thus, P(Having two girls given one girl) = 1 / 3
Sensitivity is commonly used to validate the accuracy of a classifier (Logistic, SVM, Random Forest etc.).
Sensitivity is nothing but “Predicted True events/ Total events”. True events here are the events which were true and model also predicted them as true.
Calculation of seasonality is pretty straightforward.
Seasonality = ( True Positives ) / ( Positives in Actual Dependent Variable )
To combat overfitting and underfitting, you can resample the data to estimate the model accuracy (k-fold cross-validation) and by having a validation dataset to evaluate the model.
It is a theorem that describes the result of performing the same experiment a large number of times. This theorem forms the basis of frequency-style thinking. It says that the sample means, the sample variance and the sample standard deviation converge to what they are trying to estimate.
Regularisation is the process of adding tuning parameter to a model to induce smoothness in order to prevent overfitting. This is most often done by adding a constant multiple to an existing weight vector. This constant is often the L1(Lasso) or L2(ridge). The model predictions should then minimize the loss function calculated on the regularized training set.
In statistics, a confounder is a variable that influences both the dependent variable and independent variable.
For example, if you are researching whether a lack of exercise leads to weight gain, lack of exercise = independent variable weight gain = dependent variable. A confounding variable here would be any other variable that affects both of these variables, such as the age of the subject.
Selection bias occurs when the sample obtained is not representative of the population intended to be analysed.
TF–IDF is short for term frequency-inverse document frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus. It is often used as a weighting factor in information retrieval and text mining.
The TF–IDF value increases proportionally to the number of times a word appears in the document but is offset by the frequency of the word in the corpus, which helps to adjust for the fact that some words appear more frequently in general.
We will prefer Python because of the following reasons:
Cluster sampling is a technique used when it becomes difficult to study the target population spread across a wide area and simple random sampling cannot be applied. Cluster Sample is a probability sample where each sampling unit is a collection or cluster of elements.
For eg., A researcher wants to survey the academic performance of high school students in Japan. He can divide the entire population of Japan into different clusters (cities). Then the researcher selects a number of clusters depending on his research through simple or systematic random sampling.
Eigenvectors are used for understanding linear transformations. In data analysis, we usually calculate the eigenvectors for a correlation or covariance matrix. Eigenvectors are the directions along which a particular linear transformation acts by flipping, compressing or stretching.
Eigenvalue can be referred to as the strength of the transformation in the direction of eigenvector or the factor by which the compression occurs.
Cross-validation is a model validation technique for evaluating how the outcomes of statistical analysis will generalize to an independent dataset. Mainly used in backgrounds where the objective is forecast and one wants to estimate how accurately a model will accomplish in practice.
The goal of cross-validation is to term a data set to test the model in the training phase (i.e. validation data set) in order to limit problems like overfitting and get an insight on how the model will generalize to an independent data set.
It is a traditional database schema with a central table. Satellite tables map IDs to physical names or descriptions and can be connected to the central fact table using the ID fields; these tables are known as lookup tables and are principally useful in real-time applications, as they save a lot of memory. Sometimes star schemas involve several layers of summarization to recover information faster.
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We make sure that you are never going to miss a class at SK trainings. If you do so you can choose from either of the below two options.
The industry trainers who are working with us are highly qualified and possess a minimum of 10-12 years of experience in the IT field. We follow a critical procedure while selecting a trainer which include profile selection, screening, technical evaluation and validating presentation skills. The trainers who get top ratings by students are given priority and continue to teach with us.
You need not worry about anything. Once you join SK trainings, you will get lifetime assistance from our support team and they are available 24/7 to assist you.
Online training is an interactive session where you and the trainer are going to connect through the internet at a specific time on a regular basis. They are interactive sessions and you can interact with trainers and ask your queries.
Yes, you will be eligible for two types of discounts. One is when you join as a group and the other is when you are referred by our old student or learner.
Yes, you will gain lifetime access to course material once you join SK trainings.
Our trainer will provide you server access and help you install the tools on your system required to execute the things practically. Moreover, our technical team will be there for you to assist during the practical sessions.
Yes, Sk trainings accepts the course fee on an instalment basis to make the students feel convenient.
SK trainings is one of the top online training providers in the market with a unique approach. We are one-stop solutions for all your IT and Corporate training needs. Sk trainings has a base of highly qualified, real-time trainers. Once a student commits to us we make sure he will gain all the essential skills required to make him/her an industry professional.
Till now SK trainings has trained thousands of aspirants on different tools and technologies and the number is increasing day by day. We have the best faculty team who works relentlessly to fulfill the learning needs of the students. Our support team will provide 24/7 assistance.
SK trainings offers two different modes of training to meet student requirements. Either you can go for Instructor led-live online classes or you can take high-quality self-paced videos. Even if you go with self-paced training videos you will avail all the facilities offered for the live sessions students.
Yes, each course offered by the SK trainings is associated with Two live projects. During the training, students are introduced to the live projects implementation process.
Yes, absolutely you are eligible for this. All you need to do is pay the extra amount and attend live sessions.
You must experience the course before enrolling.
Join Data Science training and learn from the top Data Science experts at SK trainings. You will gain all the knowledge from our expert trainers that is needed for building and managing virtual machines. The experts at SK training will provide in-depth knowledge. You will come across Data Science concepts such as data analysis, connecting R with Hadoop framework, R statistical computing, Machine Learning algorithms, Naïve Bayes, K-Means Clustering, business analytics, etc. Not only that you will get hands on-experience by implementing real-world projects. Get the Data Science certification by enrolling in the Data Science online training at SK trainings.Get Certified
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