Data Science Training in Chennai

The average annual salary of Data Scientists as per Indeed is approximately US$122,801 in the United States.

• Data Scientist is the best job in the 21st century – Harvard Business Review
• The number of jobs for all data professionals in the United States will increase to 2.7 million – IBM
• Global Big Data market achieves US$122 billion in sales in 6 years – Frost & Sullivan

The demand for Data Scientists far exceeds the supply. This is a serious problem in a data-driven world that we are living in today. As a result, most organizations are willing to pay high salaries for professionals with appropriate Data Science skills.

Data science training online will help you become proficient in Data Science, R programming, Data Analysis, Big Data, and more. Thus, you can easily accelerate your career in this evolving domain and take it to the next level.

• Introduction to Data Science and its importance
• Data Science life cycle and data acquisition
• Experimentation, evaluation, and project deployment tools
• Various Machine Learning algorithms
• Predictive analytics and segmentation using clustering
• Fundamentals of Big Data Hadoop
• Roles and responsibilities of a Data Scientist
• Using real-world datasets to deploy recommender systems
• Working on data mining and data manipulation

• Information Architects and Statisticians
• Developers looking to master Machine Learning and Predictive Analytics
• Big Data, Business Analyst, and Business Intelligence professionals
• Aspirants looking to work as Machine Learning experts, Data Scientists, etc.

There are no prerequisites for taking up this training course. If you like mathematics, you can accelerate your learning through this Data Scientist course.

In the United States, the average salary of a Data Scientist is US$112,957. The average salary of Data Scientists in India is ₹853,191.

Many top companies hire Data Scientists. A few of them are Amazon, Google, IBM, Facebook, Microsoft, Walmart, Target, Visa, Bank of America, Accenture, Fractal Analytics, etc.

There are several ways to become a Data Scientist. Evidently, Data Scientists use a large number of tools/technologies, such as R and Python programming, and analysis tools, like SAS.

As a budding Data Scientist, you should be familiar with data analysis and statistical software packages. You might have to work on large dataset transformations and storage using Hadoop and Spark. The most important skill of a Data Scientist is data visualization. In it, the found out facts need to be presented to the business team effectively so that they can understand the insights.

• Understand the Problem

Data Scientists should be aware of the business pain points and ask the right questions.

• Cullect Data

They need to cullect enough data to understand the problem in hand and to better sulve it in terms of time, money, and resources.

• Process the Raw Data

Data is rarely used in its original form. It must be processed, and there are several ways to convert it into a usable format.

• Explore the Data

Once the data has been processed and converted into a usable form, Data Scientists must examine it to determine the characteristics and find out obvious trends, correlations, and more.

• Analyze the Data

To understand the data, they use a variety of toul libraries, such as Machine Learning, statistics and probability, linear and logistic regression, time series analysis, and more.

• Communicate Results

At last, results must be communicated to the right stakehulders, laying the groundwork for all identified issues.

• Introduction to data exploration
• Importing and exporting data to/from external sources
• What are data exploratory analysis and data importing?
• DataFrames, working with them, accessing individual elements, vectors, factors, operators, in-built functions, conditional and looping statements, user-defined functions, and data types

Hands-on Exercise:

• Accessing individual elements of customer churn data
• Modifying and extracting results from the dataset using user-defined functions in R

• Need for data manipulation
• Introduction to the dplyr package
• Selecting one or more columns with select(), filtering records on the basis of a condition with filter(), adding new columns with mutate(), sampling, and counting
• Combining different functions with the pipe operator and implementing SQL-like operations with sqldf

Hands-on Exercise:

• Implementing dplyr
• Performing various operations for manipulating data and storing it

• Introduction to visualization
• Different types of graphs, the grammar of graphics, the ggplot2 package, categorical distribution with geom_bar(), numerical distribution with geom_hist(), building frequency polygons with geom_freqpoly(), and making a scatterplot with geom_pont()
• Multivariate analysis with geom_boxplot
• Univariate analysis with a barplot, a histogram and a density plot, and multivariate distribution
• Creating barplots for categorical variables using geom_bar(), and adding themes with the theme() layer
• Visualization with plotly, frequency plots with geom_freqpoly(), multivariate distribution with scatter plots and smooth lines, continuous distribution vs categorical distribution with box-plots, and sub grouping plots
• Working with co-ordinates and themes to make graphs more presentable, understanding plotly and various plots, and visualization with ggvis
• Geographic visualization with ggmap() and building web applications with shinyR

Hands-on Exercise:

• Creating data visualization to understand the customer churn ratio using ggplot2 charts
• Using plotly for importing and analyzing data
• Visualizing tenure, monthly charges, total charges, and other individual columns using a scatter plot

• Why do we need statistics?
• Categories of statistics, statistical terminology, types of data, measures of central tendency, and measures of spread
• Correlation and covariance, standardization and normalization, probability and the types, hypothesis testing, chi-square testing, ANOVA, normal distribution, and binary distribution

Hands-on Exercise:

• Building a statistical analysis model that uses quantification, representations, and experimental data
• Reviewing, analyzing, and drawing conclusions from the data

• Introduction to Machine Learning
• Introduction to linear regression, predictive modeling, simple linear regression vs multiple linear regression, concepts, formulas, assumptions, and residuals in Linear Regression, and building a simple linear model
• Predicting results and finding the p-value and an introduction to logistic regression
• Comparing linear regression with logistics regression and bivariate logistic regression with multivariate logistic regression
• Confusion matrix the accuracy of a model, understanding the fit of the model, threshold evaluation with ROCR, and using qqnorm() and qqline()
• Understanding the summary results with null hypothesis, F-statistic, and
building linear models with multiple independent variables

Hands-on Exercise:

• Modeling the relationship within data using linear predictor functions
• Implementing linear and logistics regression in R by building a model with ‘tenure’ as the dependent variable

• Introduction to logistic regression
• Logistic regression concepts, linear vs logistic regression, and math behind logistic regression
• Detailed formulas, logit function and odds, bivariate logistic regression, and Poisson regression
• Building a simple binomial model and predicting the result, making a confusion matrix for evaluating the accuracy, true positive rate, false positive rate, and threshold evaluation with ROCR
• Finding out the right threshold by building the ROC plot, cross validation, multivariate logistic regression, and building logistic models with multiple independent variables
• Real-life applications of logistic regression

Hands-on Exercise:

• Implementing predictive analytics by describing data
• Explaining the relationship between one dependent binary variable and one or more binary variables
• Using glm() to build a model, with ‘Churn’ as the dependent variable

• What is classification? Different classification techniques
• Introduction to decision trees
• Algorithm for decision tree induction and building a decision tree in R
• Confusion matrix and regression trees vs classification trees
• Introduction to bagging
• Random forest and implementing it in R
• What is Naive Bayes? Computing probabilities
• Understanding the concepts of Impurity function, Entropy, Gini index, and Information gain for the right split of node
• Overfitting, pruning, pre-pruning, post-pruning, and cost-complexity pruning, pruning a decision tree and predicting values, finding out the right number of trees, and evaluating performance metrics

Hands-on Exercise:

• Implementing random forest for both regression and classification problems
• Building a tree, pruning it using ‘churn’ as the dependent variable, and building a random forest with the right number of trees
• Using ROCR for performance metrics

• What is Clustering? Its use cases
• what is k-means clustering? What is canopy clustering?
• What is hierarchical clustering?
• Introduction to unsupervised learning
• Feature extraction, clustering algorithms, and the k-means clustering algorithm
• Theoretical aspects of k-means, k-means process flow, k-means in R, implementing k-means, and finding out the right number of clusters using a scree plot
• Dendograms, understanding hierarchical clustering, and implementing it in R
• Explanation of Principal Component Analysis (PCA) in detail and implementing PCA in R

Hands-on Exercise:

• Deploying unsupervised learning with R to achieve clustering and dimensionality reduction
• K-means clustering for visualizing and interpreting results for the customer churn data

• Introduction to association rule mining and MBA
• Measures of association rule mining: Support, confidence, lift, and apriori algorithm, and implementing them in R
• Introduction to recommendation engines
• User-based collaborative filtering and item-based collaborative filtering, and implementing a recommendation engine in R
• Recommendation engine use cases

Hands-on Exercise:

• Deploying association analysis as a rule-based Machine Learning method
• Identifying strong rules discovered in databases with measures based on interesting discoveries