Data science is a "concept to unify statistics, data analysis and their related methods" to "understand and analyze actual phenomena" with data. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science from the subdomains of machine learning, classification, cluster analysis, data mining, databases, and visualization. The Data Science Certification Training enables you to gain knowledge of the entire Life Cycle of Data Science, analyzing and visualizing different data sets, different Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes. 

After the completion of the course, you should be able to:

·        Gain insight into the 'Roles' played by a Data Scientist

·        Analyze several types of data using R

·        Describe the Data Science Life Cycle

·        Work with different data formats like XML, CSV etc.

·        Learn tools and techniques for Data Transformation

·        Discuss Data Mining techniques and their implementation

·        Analyze data using Machine Learning algorithms in R

·        Explain Time Series and it’s related concepts

·        Perform Text Mining and Sentimental analyses on text data

·        Gain insight into Data Visualization and Optimization techniques

·        Understand the concepts of Deep Learning

Data science incorporates tools from multi disciplines to gather a data set, process and derive insights from the data set, extract meaningful data from the set, and interpret it for decision-making purposes. The disciplinary areas that make up the data science field include mining, statistics, machine learning, analytics, and some programming. Data mining applies algorithms in the complex data set to reveal patterns which are then used to extract useable and relevant data from the set. Statistical measures like predictive analytics utilize this extracted data to gauge events that are likely to happen in the future based on what the data shows happened in the past. Machine learning is an artificial intelligence tool that processes mass quantities of data that a human would be unable to process in a lifetime. Machine learning perfects the decision model presented under predictive analytics by matching the likelihood of an event happening to what actually happened at the predicted time.

The course is designed for all those who want to learn about the life cycle of Data Science, which would include acquisition of data from various sources, data wrangling and data visualization. Applying Machine Learning techniques in R language, and wish to apply these techniques on different types of Data. 

The following professionals can go for this course:

1. Developers aspiring to be a 'Data Scientist'

2. Analytics Managers who are leading a team of analysts 

3. Business Analysts who want to understand Machine Learning (ML) Techniques

4. Information Architects who want to gain expertise in Predictive Analytics

5. 'R' professionals who want to captivate and analyze Big Data

7. Analysts wanting to understand Data Science methodologies

The system requirement for Python course is a system with Intel i3 processor or above, minimum 3GB RAM (4GB recommended) and an operating system can be of 32bit or 64 bit.

Goal - Get an introduction to Data Science in this Module and see how Data Science helps to analyze large and unstructured data with different tools.

Objectives - At the end of this Module, you should be able to:

• Define Data Science

• Discuss the era of Data Science

• Describe the Role of a Data Scientist

• Illustrate the Life cycle of Data Science

• List the Tools used in Data Science

• State what role Big Data and Hadoop, R, Spark and Machine Learning play in Data Science

Topics:

• What is Data Science?

• What does Data Science involve?

• Era of Data Science

• Business Intelligence vs Data Science

• Life cycle of Data Science

• Tools of Data Science

• Introduction to Big Data and Hadoop

• Introduction to R

• Introduction to Spark

• Introduction to Machine Learning

Goal - In this Module, you should learn about different statistical techniques and terminologies used in data analysis.

Objectives - At the end of this Module, you should be able to:

• Define Statistical Inference

• List the Terminologies of Statistics

• Illustrate the measures of Center and Spread

• Explain the concept of Probability

• State Probability Distributions

Topics:

• What is Statistical Inference?

• Terminologies of Statistics

• Measures of Centers

• Measures of Spread

• Probability

• Normal Distribution

• Binary Distribution

Goal - Discuss the different sources available to extract data, arrange the data in structured form, analyze the data, and represent the data in a graphical format.

Objectives - At the end of this Module, you should be able to:

• Discuss Data Acquisition techniques

• List the different types of Data

• Evaluate Input Data

• Explain the Data Wrangling techniques

• Discuss Data Exploration

Topics:

• Data Analysis Pipeline

• What is Data Extraction

• Types of Data

• Raw and Processed Data

• Data Wrangling

• Exploratory Data Analysis

• Visualization of Data

Hands-On/Demo: 

•       Loading different types of dataset in R 

•       Arranging the data 

•       Plotting the graphs

Goal - Get an introduction to Machine Learning as part of this Module. You will discuss the various categories of Machine Learning and implement Supervised Learning Algorithms.

Objectives - At the end of this module, you should be able to:

• Define Machine Learning

• Discuss Machine Learning Use cases

• List the categories of Machine Learning

• Illustrate Supervised Learning Algorithms

Topics:

• What is Machine Learning?

• Machine Learning Use-Cases

• Machine Learning Process Flow

• Machine Learning Categories

• Supervised Learning

    o     Linear Regression

    o     Logistic Regression

Hands-On/Demo:

• Implementing Linear Regression model in R

• Implementing Logistic Regression model in R 

Goal - In this module, you should learn the Supervised Learning Techniques and the implementation of various Techniques, for example, Decision Trees, Random Forest Classifier etc. 

Objectives - At the end of this module, you should be able to:

• Define Classification

• Explain different Types of Classifiers such as,

   o   Decision Tree

   o   Random Forest

   o   Naïve Bayes Classifier

   o   Support Vector Machine

Topics:

• What is Classification and its use cases?

• What is Decision Tree?

• Algorithm for Decision Tree Induction

• Creating a Perfect Decision Tree

• Confusion Matrix

• What is Random Forest?

• What is Navies Bayes?

• Support Vector Machine: Classification

Hands-On/Demo:

• Implementing Decision Tree model in R

• Implementing Linear Random Forest in R

• Implementing Navies Bayes model in R

• Implementing Support Vector Machine in R

Goal - Learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data.

Objectives - At the end of this module, you should be able to:

• Define Unsupervised Learning

• Discuss the following Cluster Analysis

    o     K - means Clustering

    o     C - means Clustering

    o     Hierarchical Clustering

Topics:

• What is Clustering & its Use Cases?

• What is K-means Clustering?

• What is C-means Clustering?

• What is Canopy Clustering?

• What is Hierarchical Clustering?

Hands-On/Demo:

• Implementing K-means Clustering in R

• Implementing C-means Clustering in R

• Implementing Hierarchical Clustering in R

Goal - In this module, you should learn about association rules and different types of Recommender Engines.

Objectives - At the end of this module, you should be able to:

• Define Association Rules

• Define Recommendation Engine

• Discuss types of Recommendation Engines

    o     Collaborative Filtering

    o     Content-Based Filtering

• Illustrate steps to build a Recommendation Engine

Topics:

• What is Association Rules & its use cases?

• What is Recommendation Engine & it’s working?

• Types of Recommendation Types

• User-Based Recommendation

• Item-Based Recommendation

• Difference: User-Based and Item-Based Recommendation

• Recommendation Use-case

Hands-On/Demo:

• Implementing Association Rules in R

• Building a Recommendation Engine in R

Goal - Discuss Unsupervised Machine Learning Techniques and the implementation of different algorithms, for example, TF-IDF and Cosine Similarity in this Module. 

Objectives - At the end of this module, you should be able to:

• Define Text Mining

• Discuss Text Mining Algorithms

    o     Bag of Words Approach

    o     Sentiment Analysis

Topics:

• The concepts of text-mining

• Use cases

• Text Mining Algorithms

• Quantifying text

• TF-IDF

• Beyond TF-IDF

Hands-On/Demo:

• Implementing Bag of Words approach in R

• Implementing Sentiment Analysis on twitter Data using R

Goal - In this module, you should learn about Time Series data, different component of Time Series data, Time Series modelling - Exponential Smoothing models and ARIMA model for Time Series forecasting.

Objectives - At the end of this module, you should be able to:

• Describe Time Series data

• Format your Time Series data

• List the different components of Time Series data

• Discuss different kind of Time Series scenarios 

• Choose the model according to the Time series scenario

• Implement the model for forecasting

• Explain working and implementation of ARIMA model

• Illustrate the working and implementation of different ETS models

• Forecast the data using the respective model

 Topics:

• What is Time Series data?

• Time Series variables

• Different components of Time Series data

• Visualize the data to identify Time Series Components

• Implement ARIMA model for forecasting

• Exponential smoothing models

• Identifying different time series scenario based on which different Exponential Smoothing model can be applied

• Implement respective ETS model for forecasting

Hands-On/Demo:

•       Visualizing and formatting Time Series data

•       Plotting decomposed Time Series data plot

•       Applying ARIMA and ETS model for Time Series forecasting

•       Forecasting for given Time period

Goal - Get introduced to the concepts of Reinforcement learning and Deep learning in this Module. These concepts are explained with the help of Use cases. You will get to discuss Artificial Neural Network, the building blocks for artificial neural networks, and few artificial neural network terminologies.

Objectives - At the end of this module, you should be able to:

• Define Reinforced Learning

• Discuss Reinforced Learning Use cases

• Define Deep Learning

• Understand Artificial Neural Network

• Discuss basic Building Blocks of Artificial Neural Network

• List the important Terminologies of ANN’s

Topics:

• Reinforced Learning

• Reinforcement learning Process Flow

• Reinforced Learning Use cases

• Deep Learning

• Biological Neural Networks

• Understand Artificial Neural Networks

• Building an Artificial Neural Network

• How ANN works

• Important Terminologies of ANN’s