Data Science Training

• 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.

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

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

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.

• 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

Basic Statistics

• 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

Advanced Statistics

• 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

High-Level overview of Machine Learning

• Supervised Learning
  • Classifier
  • Regression

• Unsupervised Learning
  • Clustering

Supervised - Classifiers

• 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

Supervised - Regression

• 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

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.

• 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)

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.

• Selection bias
• Under coverage bias
• Survivorship bias

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:

• 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.

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.