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Data Science Certification Training in Great Falls, MT

Educera INC
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Endorsed by Curators:
Jan 28 9:00AM - 6:00PM

Key Features:

  • 32 hours of Classroom training
  • 100% Money Back Guarantee
  • Real-life case studies
  • Life time access to Learning Management System (LMS)
  • Practical Assignments
  • Certification: Educera certifies you based on the project.
  • 24/7 customer support

About Data Science Certification Training

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

Who Should Apply?

The training is a best fit for:

  • IT professionals interested in pursuing a career in analytics
  • Graduates looking to build a career in analytics and data science
  • Experienced professionals who would like to harness data science in their fields
  • Anyone with a genuine interest in the field of data science

Data Science Certification Training - Course Agenda

Introduction to Data Science

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


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

Statistical Inference

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


What is Statistical Inference?

Terminologies of Statistics

Measures of Centers

Measures of Spread


Normal Distribution

Binary Distribution

Data Extraction, Wrangling and Exploration

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


Data Analysis Pipeline

What is Data Extraction

Types of Data

Raw and Processed Data

Data Wrangling

Exploratory Data Analysis

Visualization of Data


Loading different types of dataset in R

Arranging the data

Plotting the graphs

Introduction to Machine Learning

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


What is Machine Learning?

Machine Learning Use-Cases

Machine Learning Process Flow

Machine Learning Categories

Supervised Learning

  • Linear Regression
  • Logistic Regression


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,

  • Decision Tree
  • Random Forest
  • Nave Bayes Classifier
  • Support Vector Machine


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


Implementing Decision Tree model in R

Implementing Linear Random Forest in R

Implementing Navies Bayes model in R

Implementing Support Vector Machine in R

Unsupervised Learning

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

  1. K means Clustering
  2. C means Clustering
  3. Hierarchical Clustering


What is Clustering & its Use Cases?

What is K-means Clustering?

What is C-means Clustering?

What is Canopy Clustering?

What is Hierarchical Clustering?


Implementing K-means Clustering in R

Implementing C-means Clustering in R

Implementing Hierarchical Clustering in R

Recommender Engines

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

  1. Collaborative Filtering
  2. Content-Based Filtering

Illustrate steps to build a Recommendation Engine


What is Association Rules & its use cases?

What is Recommendation Engine & its working?

Types of Recommendation Types

User-Based Recommendation

Item-Based Recommendation

Difference: User-Based and Item-Based Recommendation

Recommendation Use-case


Implementing Association Rules in R

Building a Recommendation Engine in R

Text Mining

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

  • Bag of Words Approach
  • Sentiment Analysis


The concepts of text-mining

Use cases

Text Mining Algorithms

Quantifying text


Beyond TF-IDF


Implementing Bag of Words approach in R

Implementing Sentiment Analysis on twitter Data using R

Time Series

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


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


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

Deep Learning

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 ANNs


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 ANNs

Why Educera?

Educera's training is the best and value for time & money invested. We stand out because our customers

  • Get trained at the best price compared to other training providers.
  • Get trained by the best trainer in the industry.
  • Get accesses to course specific learning videos.
  • Get 100% Money back guarantee*.

Training Fee:

Early Bird: Booking at least one month prior to the class start date

Training Venue:

Venue will be confirmed to the classroom participants one week prior to the workshop start date and online participants will get the session attendance link before 4- 5 days of the training start date. Venue is finalized one week prior to the start date so that we can accommodate last minute rescheduling from the participants and we do not incur additional cost for rescheduling or cancellation.

Other Courses:

CAPM | PMI ACP | Lean Six Sigma Green Belt | Lean Six Sigma Black Belt (LSSBB) | Dual Lean Six Sigma Green and Black Belt (LSSGB & LSSBB) | Dual Lean Six Sigma Yellow and Green Belt (LSSYB & LSSGB) | ITIL Foundation | Big Data and Hadoop Developer and Admin | Data Science | Salesforce ADM 201 and App Builder | Tableau

Phone: +1 302-261-9363

Timings: 09.00 AM to 07.00 PM EDT

To know more about our Project Management Professional (PMP) training program,

Email us at or call us at +1 302-261-9363.

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