Data Science Certification Training in Kokomo, IN

Data Science refers to the process of extracting valuable information from various structured or unstructured data, e.g., data mining. Data Science aims to explore, sort, and analyze metadata from a variety of sources and use them to draw conclusions, optimize business processes, and support decision-making. Our industry experts have developed the Data Science Certification to help individuals know how Data Science works and how it plays an important role in Artificial Intelligence (AI).

Data Science course accelerates your career in Data Science and provides you with the world-class training and skills required to become successful in this field. The course offers extensive training on the most in-demand Data Science and Machine Learning skills with hands-on exposure to key tools and technologies including Python, R, Tableau, and concepts of Machine Learning. Become a Data Scientist by diving deep into the nuances of data interpretation, mastering technologies like Machine Learning, and mastering powerful programming skills to take your career in Data Science to the next level.

This training session will help the learners become successful Data Scientists and improve their ability to analyze business data to extract meaningful insights.

Key Features:

• 32 hours of online Classroom training
• Real-life case studies
• Lifetime access to Learning Management System (LMS)
• Practical Assignments
• 100% Money Back Guarantee
• 24/7 customer support

Why Data Science?

• Businesses Will Need One Million Data Scientists by 2023 – 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)

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 the Centre 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 a 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 the 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 are 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 are Association Rules & their 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

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 the 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 components of Time Series data, Time Series modeling – Exponential Smoothing models, and the 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 kinds of Time Series scenarios
• Choose the model according to the Time series scenario
• Implement the model for forecasting
• Explain the working and implementation of the 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 the ARIMA model for forecasting
• Exponential smoothing models
• Identifying different time series scenarios based on which different Exponential Smoothing models 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 models for Time Series forecasting
• Forecasting for the given 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 Networks, the building blocks for artificial neural networks, and a 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 the basic Building Blocks of the 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

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Contact us for more information:

Name: Debbie Riel

Email: debbie.riel@learninzone.com

Phone: +1 302.251.9769