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Course Curriculum

Learning Objectives - Get an introduction to Data Science in this module and see how Data Science helps to analyze large and unstructured data with different tools.
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
Learning Objectives - In this module, you will learn about different statistical techniques and terminologies used in data analysis.
What is Statistical Inference?
Terminologies of Statistics
Measures of Centers
Measures of Spread
Normal Distribution
Binary Distribution
Learning Objectives - 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.
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
Learning Objectives - 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.
What is Machine Learning?
Machine Learning Use-Cases
Machine Learning Process Flow
Machine Learning Categories
Supervised Learning algorithm: Linear Regression and Logistic Regression
Implementing Linear Regression model in R
Implementing Logistic Regression model in R
Learning Objectives - In this module, you should learn the Supervised Learning Techniques and the implementation of various techniques, such as Decision Trees, Random Forest Classifier, etc.
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
Implementing Decision Tree model in R
Implementing Linear Random Forest in R
Implementing Navies Bayes model in R
Implementing Support Vector Machine in R
Learning Objectives - Learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data.
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
Learning Objectives - In this module, you should learn about association rules and different types of Recommender Engines.
What is Association Rules & its use cases?
What is Recommendation Engine & it’s working?
Types of Recommendations
User-Based Recommendation
Item-Based Recommendation
Difference: User-Based and Item-Based Recommendation
Recommendation use cases
Implementing Association Rules in R
Building a Recommendation Engine in R
Learning Objectives - Discuss Unsupervised Machine Learning Techniques and the implementation of different algorithms, for example, TF-IDF and Cosine Similarity in this Module.
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
Learning Objectives - In this module, you should learn about Time Series data, different component of Time Series data, Time Series modeling - Exponential Smoothing models and ARIMA model for Time Series Forecasting.
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
Learning Objectives - 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.
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

Course Description

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

Data Science Certification Training is designed by industry experts to make you a Certified Data Scientist. The Data Science course offers:
In-depth knowledge of Data Science Life Cycle and Machine Learning Algorithms
Comprehensive knowledge of various tools and techniques for Data Transformation
The capability to perform Text Mining and Sentimental analyses on text data and gain an insight into Data Visualization and Optimization techniques
The exposure to many real-life industry-based projects which will be executed in RStudio
Projects which are diverse in nature covering media, healthcare, social media, aviation and HR
Rigorous involvement of an SME throughout the Data Science Training to learn industry standards and best practices

Data science is an evolutionary step in interdisciplinary fields like the business analysis that incorporate computer science, modelling, statistics and analytics. To take complete benefit of these opportunities, you need a structured training with an updated curriculum as per current industry requirements and best practices.

Besides strong theoretical understanding, you need to work on various real-life projects using different tools from multiple 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.

Additionally, you need the advice of an expert who is currently working in the industry tackling real-life data-related challenges.

Data Science Training will help you become a Data Science Expert. It will hone your skills by helping you to understand and analyze actual phenomena with data and provide the required hands-on experience for solving real-time industry-based projects.

During this Data Science course, you will be trained by our expert instructors 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
The market for Data Analytics is growing across the world and this strong growth pattern translates into a great opportunity for all the IT Professionals. Our Data Science Training helps you to grab this opportunity and accelerate your career by applying the techniques on different types of Data. It is best suited for:
Developers aspiring to be a 'Data Scientist'
Analytics Managers who are leading a team of analysts
Business Analysts who want to understand Machine Learning (ML) Techniques
Information Architects who want to gain expertise in Predictive Analytics
'R' professionals who wish to work Big Data
Analysts wanting to understand Data Science methodologies

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

Course certification