This Data Science training will prepare the students to handle the multidisciplinary field and be ready for the industry.

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- Learn Your Favourite Skill In Your Spare Time
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- Watch Complex Topics As Many Times As Possible.
- Gain Industry-Oriented Skills.
- Solve Your Doubts With Real-Time Expert Trainers.

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- Get The Guidance To Clear The Certification Exam

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- Train Your Workforce With The Latest Skills.
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Become a Data Scientist by joining experts designed **Data Science Training From India**. 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 Sience 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.

- Introduction to Data Science and its role in this modern world.
- Data acquisition and data science lifecycle.
- Project deployment, evaluation, and experimentation tools.
- Clustering for predictive segmentation and analytics
- Introduction to different machine learning algorithms.
- Hadoop integration with R
- Roles and responsibilities of Data scientists
- Working on data manipulation, data structures, and data mining.
- Building recommender systems with real-world data sets

Following are the professionals who can enhance their skills by joining this online Data Science training.

- Statisticians
- Information Architects
- Big Data, Business Analys
- Business Intelligence professionals.
- Software developers looking to gain skills of Machine Learning and Predictive Analytics.
- People who wish to work as machine learning and data science experts.

Following are the various job roles available for a SCCM professional:

- Data Science is named as the sexiest job of the 21st century
- Increased dependency on data has created a huge demand for the Data Science field.
- There is a huge demand for skilled data professionals and there are not enough data scientists in the market today
- Frost & Sullivan survey reveals that the Big Data market will reach $122 billion in sales in the coming 6 years.

Following are some of the top companies which are hiring Data science professionals

- Amazon
- Google, IBM
- Microsoft, Wal-Mart
- Bank of America
- Accenture
- Mu-Sigma
- Fractal Analytics and more.

- Recap of Demo
- Introduction to Types of Analytics
- Project life cycle

- Installation of Python IDE
- Anaconda and Spyder
- Working with Python and some basic commands & Examples
- Introduction to R and RStudio with some basics
Various graphical techniques to understand data
- Bar plot
- Histogramr
- Box plots
- Scatter plot

- The various Data Types namely continuous, discrete, categorical, count, qualitative, quantitative and its identification and application. Further classification of data in terms of Nominal, Ordinal, Interval and Ratio types
- Random Variable and its definition
- Probability and Probability Distribution – Continuous probability distribution / Probability density function and Discrete probability distribution / Probability mass function

- Various sampling techniques
- Measure of central tendency
- Mean / Average
- Median
- Mode

- Measure of Dispersion
- Variance
- Standard Deviation
- Range

- Expected value of probability distribution
- Measure of Skewness
- Measure of Kurtosis
- Normal Distribution
- Standard Normal Distribution / Z distribution
- Z scores and Z table
- QQ Plot / Quantile-Quantile plot

- Sampling Variations
- Central Limit Theorem
- Sample size calculator
- T-distribution / Student's-t distribution
- Confidence interval
- Population parameter - Standard deviation known
- Population parameter - Standard deviation unknown

Introduced to Hypothesis testing, various Hypothesis testing Statistics, understand what is Null Hypothesis, Alternative hypothesis and types of hypothesis testing

- Type I and Type II errors
- ANOVA
- Chi-Square test

- Supervised Learning
- Classifier
- Regression

- Unsupervised Learning
- Clustering

- Simple Logistic Regression
- Multiple Logistic Regression
- Confusion matrix
- False Positive, False Negative
- True Positive, True Negative
- Sensitivity, Recall, Specificity, F1

- Receiver operating characteristics curve (ROC curve)

- Network Topology
- Support Vector Machines

- Concept with a business case

- ARMA (Auto-Regressive Moving Average), Order p and q
- ARIMA (Auto-Regressive Integrated Moving Average), Order p, d and q

- Scatter Diagram
- Correlation Analysis
- Principles of Regression
- Ordinary least squares
- Simple Linear Regression
- Understanding Overfitting (Variance) vs Underfitting (Bias)
- LINE assumption
- Collinearity (Variance Inflation Factor)
- Linearity
- Normality

- Multiple Linear Regression

- o Lasso and Ridge Regressions

- Logit and Log Likelihood
- Category Baselining
- Modeling Nominal categorical data

- Hierarchial Clustering / Agglomerative Clustering
- K-Means Clustering

- Why dimension reduction
- Advantages of PCA
- Calculation of PCA weights
- 2D Visualization using Principal components
- Basics of Matrix algebra
- SVD – Decomposition of matrix data

- Definition of a network (the LinkedIn analogy)
- Introduction to Google Page Ranking

- What is Market Basket / Affinity Analysis
- Measure of association
- Support
- Confidence
- Lift Ratio

- Apriori Algorithm
- Sequential Pattern Mining

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.

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

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.

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.

- Any die has six sides from 1-6. There is no way to get seven equal outcomes from a single rolling of a die. If we roll the die twice and consider the event of two rolls, we now have 36 different outcomes.
- To get our 7 equal outcomes we have to reduce this 36 to a number divisible by 7. We can thus consider only 35 outcomes and exclude the other one.
- A simple scenario can be to exclude the combination (6,6), i.e., to roll the die again if 6 appears twice.
- All the remaining combinations from (1,1) till (6,5) can be divided into 7 parts of 5 each. This way all the seven sets of outcomes are equally likely.

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:

- Estimating the accuracy of sample statistics by using subsets of accessible data or drawing randomly with replacement from a set of data points
- Substituting labels on data points when performing significance tests
- Validating models by using random subsets (bootstrapping, cross-validation)

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 )

- Selection bias
- Under coverage bias
- Survivorship bias

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.

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:

- Python would be the best option because it has Pandas library that provides easy to use data structures and high-performance data analysis tools.
- R is more suitable for machine learning than just text analysis.
- Python performs faster for all types of text analytics.

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.

Eigenvalue can be referred to as the strength of the transformation in the direction of eigenvector or the factor by which the compression occurs.

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.

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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.

- You can view the recorded sessions sent to you on a regular basis.
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Yes, you will gain lifetime access to course material once you join SK Trainings.

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.

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Join **Data Science Training From Hyderabad** 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 Trainings 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 Training Online** at SK Trainings.

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