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Data Science Certification Training

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Data Science course helps you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, Naive Bayes using R. You'll learn the concepts of Statistics, Time Series, Text Mining and an introduction to Deep Learning. You'll solve real life case studies on Media, Healthcare, Social Media, Aviation, HR.

Why this course ?

Businesses Will Need One Million Data Scientists by 2018 - KDnuggets

Roles like chief data & chief analytics officers have emerged to ensure that analytical insights drive business strategies - Forbes

The average salary for a Data Scientist is $113k (Glassdoor)


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Course Duration

You will undergo self-paced learning where you will get an in-depth knowledge of various concepts that will be covered in the course.

Real-life Case Studies

Towards the end of the training, you will be working on a project where you will implement the techniques learnt to visualize.

Assignments

Each class has practical assignments which shall be finished before the next class and helps you to apply the concepts taught during

24 x 7 Expert Support

We have 24x7 online support team to resolve all your technical queries, through ticket based tracking system, for the lifetime.

Forum

We have a community forum for all our customers that further facilitates learning through peer interaction and knowledge

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


There is no specific pre-requisite for the course, however, the basic understanding of R can be beneficial. Addilearn offers you a complimentary self-paced course, i.e. "R Essentials" when you enroll in Data Science Certification Training.

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

"You will never lose any lecture. You can choose either of the two options: View the recorded session of the class available in your LMS. You can attend the missed session, in any other live batch."

AddiLearn is committed to provide you an awesome learning experience through world-class content and best-in-class instructors. We will create an ecosystem through this training, that will enable you to convert opportunities into job offers by presenting your skills at the time of an interview. We can assist you in resume building and also share important interview questions once you are done with the training. However, please understand that we are not into job placements.

We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately participation in a live class without enrolment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in the class.