Data Science Course Online Training and Certification

1. Introduction to Data Science and its importance
2. Data Science life cycle, Data acquisition
3. Experimentation, evaluation and project deployment tools
4. Various Machine Learning algorithms
5. Predictive analytics and segmentation using clustering
6. Fundamentals of Big Data Hadoop integration with R
7. Roles and responsibilities of a Data Scientist
8. Using real-world data sets to deploy recommender systems
9. Working on data mining, data structures, and data manipulation.

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

There are no prerequisites for taking this training course. If you like mathematics, you can accelerate your Data Science learning through the Data Science course. Furthermore, you will also get a free MS Excel self-study course.

• Data Scientists are 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 per year – IBM
• Global Big Data market achieves  $122 billion in sales in six years – Frost & Sullivan

The demand for Data Scientists far exceeds its 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 the highest salary for professionals with appropriate Data Science skills.

Data Science online courses 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.

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.

A few of the many top companies that hire data scientists include Amazon, Google, IBM, Facebook, Microsoft, Wal-Mart, Target, Visa, Bank of America, Mu-Sigma, Accenture, Fractal Analytics and more.

There are several ways to become a data scientist. Evidently, data scientists use a large number of tools/technologies, such as the R and Python programming languages, and analysis tools, such as SAS, etc.

As a budding data scientist, you should be familiar with data analysis and statistical software packages. You also need to understand Big Data Hadoop and Spark. Now, when you transform data into business intelligence, you need to understand various visualization and reporting tools. In addition, you must be firmly rooted in all aspects of visualizations, charts, maps, and reports so that the average person can understand the insights behind the data.

This Data Science online course includes real-world  industry-based projects, which will help you in gaining hands-on experience and prepare you for challenging Data Science roles.

Intellipaat follows a rigorous certification process. To become a certified Data Scientist, you must meet the following criteria:
Online Instructor-led Course

1. 1. Successful completion of all projects, which will be evaluated by trainers
   2. Scoring minimum 60% in the Data Science quiz conducted by Intellipaat

Self-paced Course

1. 1. Completing all course videos in our LMS
   2. Scoring minimum 60% in the Data Science quiz conducted by Intellipaat

• Understand the Problem/li>

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

• Collect Data

Data Scientists need to collect enough data to understand the problem at hand and to better solve 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 form that can be used later, the data scientist must further examine the data to determine the characteristics of the data and find out more about obvious trends, correlations, and more.

• Analyze the Data

Data scientists use a variety of tool libraries in their repositories, such as machine learning, statistics and probability, linear and logistic regression, time series analysis, and more to understand the data.

• Communicate the Results

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

What is Data Science, significance of Data Science in today’s digitally-driven world, applications of Data Science, lifecycle of Data Science, components of the Data Science lifecycle, introduction to big data and Hadoop, introduction to Machine Learning and Deep Learning, introduction to R programming and R Studio.

Hands-on Exercise – Installation of R Studio, implementing simple mathematical operations and logic using R operators, loops, if statements and switch cases.

Introduction to data exploration, importing and exporting data to/from external sources, what is data exploratory analysis, data importing, dataframes, working with dataframes, accessing individual elements, vectors and factors, operators, in-built functions, conditional, looping statements and user-defined functions, matrix, list and array.

Hands-on Exercise – Accessing individual elements of customer churn data, modifying and extracting the results from the dataset using user-defined functions in R.

Need for Data Manipulation, Introduction to dplyr package, Selecting one or more columns with select() function, Filtering out records on the basis of a condition with filter() function, Adding new columns with the mutate() function, Sampling & Counting with sample_n(), sample_frac() & count() functions, Getting summarized results with the summarise() function, Combining different functions with the pipe operator, Implementing sql like operations with sqldf.

Hands-on Exercise – Implementing dplyr to perform various operations for abstracting over how data is manipulated and stored.

Introduction to visualization, Different types of graphs, Introduction to grammar of graphics & ggplot2 package, Understanding categorical distribution with geom_bar() function, understanding numerical distribution with geom_hist() function, building frequency polygons with geom_freqpoly(), making a scatter-plot with geom_pont() function, multivariate analysis with geom_boxplot, univariate Analysis with Bar-plot, histogram and Density Plot, multivariate distribution, Bar-plots for categorical variables using geom_bar(), adding themes with the theme() layer, visualization with plotly package & building web applications with shinyR, frequency-plots with geom_freqpoly(), multivariate distribution with scatter-plots and smooth lines, continuous vs categorical with box-plots, subgrouping the plots, working with co-ordinates and themes to make the graphs more presentable, Intro to plotly & various plots, visualization with ggvis package, geographic visualization with ggmap(), building web applications with shinyR.

Hands-on Exercise – Creating data visualization to understand the customer churn ratio using charts using ggplot2, Plotly for importing and analyzing data into grids. You will visualize tenure, monthly charges, total charges and other individual columns by using the scatter plot.

Why do we need Statistics?, Categories of Statistics, Statistical Terminologies,Types of Data, Measures of Central Tendency, Measures of Spread, Correlation & Covariance,Standardization & Normalization,Probability & Types of Probability, Hypothesis Testing, Chi-Square testing, ANOVA, normal distribution, binary distribution.

Hands-on Exercise – Building a statistical analysis model that uses quantifications, representations, experimental data for gathering, reviewing, analyzing and drawing conclusions from data.

Introduction to Machine Learning, introduction to Linear Regression, predictive modeling with Linear Regression, simple Linear and multiple Linear Regression, concepts and formulas, assumptions and residual diagnostics in Linear Regression, building simple linear model, predicting results and finding p-value, introduction to logistic regression, comparing linear regression and logistics regression, bivariate & multi-variate logistic regression, confusion matrix & accuracy of model, threshold evaluation with ROCR, Linear Regression concepts and detailed formulas, various assumptions of Linear Regression,residuals, qqnorm(), qqline(), understanding the fit of the model, building simple linear model, predicting results and finding p-value, understanding the summary results with Null Hypothesis, p-value & F-statistic, building linear models with multiple independent variables.

Hands-on Exercise – Modeling the relationship within the data using linear predictor functions. Implementing Linear & Logistics Regression in R by building model with ‘tenure’ as dependent variable and multiple independent variables.

Introduction to Logistic Regression, Logistic Regression Concepts, Linear vs Logistic regression, math behind Logistic Regression, detailed formulas, logit function and odds, Bi-variate logistic Regression, Poisson Regression, building simple “binomial” model and predicting result, confusion matrix and Accuracy, true positive rate, false positive rate, and confusion matrix for evaluating built model, threshold evaluation with ROCR, finding the right threshold by building the ROC plot, cross validation & multivariate logistic regression, building logistic models with multiple independent variables, real-life applications of Logistic Regression.

Hands-on Exercise – Implementing predictive analytics by describing the data and explaining the relationship between one dependent binary variable and one or more binary variables. You will use glm() to build a model and use ‘Churn’ as the dependent variable.

What is classification and different classification techniques, introduction to Decision Tree, algorithm for decision tree induction, building a decision tree in R, creating a perfect Decision Tree, Confusion Matrix, Regression trees vs Classification trees, introduction to ensemble of trees and bagging, Random Forest concept, implementing Random Forest in R, what is Naive Bayes, Computing Probabilities, Impurity Function – Entropy, understand the concept of information gain for right split of node, Impurity Function – Information gain, understand the concept of Gini index for right split of node, Impurity Function – Gini index, understand the concept of Entropy for right split of node, overfitting & pruning, pre-pruning, post-pruning, cost-complexity pruning, pruning decision tree and predicting values, find the right no of trees and evaluate performance metrics.

Hands-on Exercise – Implementing Random Forest for both regression and classification problems. You will build a tree, prune it by using ‘churn’ as the dependent variable and build a Random Forest with the right number of trees, using ROCR for performance metrics.

What is Clustering & it’s Use Cases, what is K-means Clustering, what is Canopy Clustering, what is Hierarchical Clustering, introduction to Unsupervised Learning, feature extraction & clustering algorithms, k-means clustering algorithm, Theoretical aspects of k-means, and k-means process flow, K-means in R, implementing K-means on the data-set and finding the right no. of clusters using Scree-plot, hierarchical clustering & Dendogram, understand Hierarchical clustering, implement it in R and have a look at Dendograms, Principal Component Analysis, explanation of Principal Component Analysis in detail, PCA in R, 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 & Market Basket Analysis, measures of Association Rule Mining: Support, Confidence, Lift, Apriori algorithm & implementing it in R, Introduction to Recommendation Engine, user-based collaborative filtering & Item-Based Collaborative Filtering, implementing Recommendation Engine in R, user-Based and item-Based, Recommendation 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.

Introducing Artificial Intelligence and Deep Learning, what is an Artificial Neural Network, TensorFlow – computational framework for building AI models, fundamentals of building ANN using TensorFlow, working with TensorFlow in R.

What is Time Series, techniques and applications, components of Time Series, moving average, smoothing techniques, exponential smoothing, univariate time series models, multivariate time series analysis, Arima model, Time Series in R, sentiment analysis in R (Twitter sentiment analysis), text analysis.

Hands-on Exercise – Analyzing time series data, sequence of measurements that follow a non-random order to identify the nature of phenomenon and to forecast the future values in the series.

Introduction to Support Vector Machine (SVM), Data classification using SVM, SVM Algorithms using Separable and Inseparable cases, Linear SVM for identifying margin hyperplane.

What is Bayes theorem, What is Naïve Bayes Classifier, Classification Workflow, How Naive Bayes classifier works, Classifier building in Scikit-learn, building a probabilistic classification model using Naïve Bayes, Zero Probability Problem.

Introduction to concepts of Text Mining, Text Mining use cases, understanding and manipulating text with ‘tm’ & ‘stringR’, Text Mining Algorithms, Quantification of Text, Term Frequency-Inverse Document Frequency (TF-IDF), After TF-IDF.

The Market Basket Analysis (MBA) case study

This case study is associated with the modeling technique of Market Basket Analysis where you will learn about loading of data, various techniques for plotting the items and running the algorithms. It includes finding out what are the items that go hand in hand and hence can be clubbed together. This is used for various real world scenarios like a supermarket shopping cart and so on.

Logistic Regression Case Study

In this case study you will get a detailed understanding of the advertisement spends of a company that will help to drive more sales. You will deploy logistic regression to forecast the future trends, detect patterns, uncover insights and more all through the power of R programming. Due to this the future advertisement spends can be decided and optimized for higher revenues.

Multiple Regression Case Study

You will understand how to compare the miles per gallon (MPG) of a car based on the various parameters. You will deploy multiple regression and note down the MPG for car make, model, speed, load conditions, etc. It includes the model building, model diagnostic, checking the ROC curve, among other things.

Receiver Operating Characteristic (ROC) case study

You will work with various data sets in R, deploy data exploration methodologies, build scalable models, predict the outcome with highest precision, diagnose the model that you have created with various real world data, check the ROC curve and more.

Project 1: Market Basket Analysis

Domain: Inventory Management

Problem Statement: As a new manager in the company, you are assigned the task of increasing cross selling

Topics: Association Rule Mining, Data Extraction, Data Manipulation

Highlights:

• Performing association rule mining
• Understanding where to implement Apriori Algorithm
• Setting association rules with respect to confidence

Project 2: Credit Card Fraud Detection

Domain: Banking

Problem Statement: Analysis of probability of being involved in a fraudulent operation

Topics: Algorithms, V17 Predictor, Data Visualization, R Language

Highlights:

• Understanding working with the credit card dataset
• Performing data analysis on various labels in the data
• Making use of V17 as predictor and using V14 for analysis
• Plotting score performance with respect to variables

Project 3: Data Cleaning using Census Dataset

Domain: Government

Problem Statement: Performing Data Cleansing operation on a raw dataset

Topics: Data Analysis, Data preprocessing, Cleaning Ops, Data Visualization, R Language

Highlights:

• Understanding working with the census dataset
• Changing around various with respect to a label to perform analysis
• Creation of functions to eliminate values which are not required
• Verifying the completion of data cleansing operation

Project 4: Loan Approval Prediction

Domain: Banking

Problem Statement: Prediction of approval rate of a loan by using multiple labels

Topics: Data Analysis, Data preprocessing, Cleaning Ops, Data Visualization, R Language

Highlights:

• Performing Data Preprocessing
• Building a model and applying PCA
• Building a Naïve Bayes model on the training dataset
• Prediction of values after performing analysis

Project 5: Book Recommendation System

Domain: E-Commerce

Problem Statement: Creating a model, which can recommend books, based on user interest

Topics: Data Cleaning, Data Visualization, User Based Collaborative Filtering

Highlights:

• Finding the most popular books using various techniques
• Creating a Book Recommender model using User Based Collaborative Filtering

Project 6: Netflix Recommendation System

Domain: E-Commerce

Problem Statement: Simulating the Netflix Recommendation System

Topics: Data Cleaning, Data Visualization, Distribution, Recommender Lab

Highlights:

1. Working with raw data
2. Using the Recommender Lab library in R
3. Making use of real data from Netflix 

Project 7: Creating a Pokemon Game using Machine Learning

Domain: Gaming

Problem Statement: Creating a game engine for Pokemon using Machine Learning

Topics: Decision Tress, Regression, Data Cleaning, Data Visualization

Highlights:

1. Predicting which Pokemon will win based Attack vs Defense
2. Finding whether a Pokemon is legendary using Decision Trees
3. Understanding the dynamics of decision making in Machine Learning

Case Study 1:  Introduction to R Programming

Problem Statement: Working with various operators in R

Topics: Arithmetic Operators, Relational Operators, Logical Operators

Highlights:

1. Working with Arithmetic Operators
2. Working with Relational Operators
3. Working with Logical Operators

Case Study 2: Solving Customer Churn using Data Exploration

Problem Statement: Understanding what to do to reduce customer churn using Data Exploration

Topics: Data Exploration

Highlights:

1. Extracting Individual columns
2. Creating and applying filters to manipulate data
3. Using loops for redundant operations

Case Study 3: Creating Data Structures in R

Problem Statement: Implementing various Data Structures in R for various scenarios

Topics: Vectors, list, Matrix, Array

Highlights:

1. Creating and Implementing Vectors
2. Understanding Lists
3. Using Arrays to store Matrices
4. Creating and implementing Matrices

Case Study 4: Implementing SVD in R

Problem Statement: Understanding the use Single Value Decomposition in R by making use of the MovieLense Dataset

Topics: 5-fold cross validation, Real Rating Matrix

Highlights:

1. Creating a custom  recommended movie set for each user
2. Creating User Based Collaborative Filtering Model
3. Creating RealRatingMatrix for Movie recommendation

Case Study 5: Time Series Analysis

Problem Statement: Performing TSA and understanding concepts of ARIMA for a given scenario

Topics: Time Series Analysis, R Language, Data Visualization, ARIMA model

Highlights:

• Understand how to fit an ARIMA model
• Plotting PACF charts and finding optimal parameters
• Building the ARIMA model
• Prediction of values after performing analysis