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Real-life case studies Life time access to Learning Management System
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based on the project. 24/7 customer support About Data SCIENCE
CERTIFICATION TRAINING VISTAEDUTECH’s 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. 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 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 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 Topics: • What is Statistical Inference?
• Terminologies of Statistics • Measures of Centers • Measures
of Spread • Probability • 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 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 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 Topics:
• What is Machine Learning? • Machine Learning Use-Cases •
Machine Learning Process Flow • Machine Learning Categories •
Supervised Learning Linear Regression Logistic Regression
Hands-On/Demo: • Implementing Linear Regression model in R •
Implementing Logistic Regression model in R Classification 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 Naïve Bayes Classifier 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 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 K –
means Clustering C – means Clustering 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
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 Collaborative Filtering 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 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 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 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 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 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 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 Why
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26/02/2020 Last update