Data science certifiction course

Data science certifiction course
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ProIct's Data Science Training lets you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using R. Data Science Training encompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introduction to Deep Learning. Throughout this Data Science Course, you will implement real-life use-cases on Media, Healthcare, Social Media, Aviation and HR.


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. 
 
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
Learning Objectives - In this module, you will learn about different statistical techniques and terminologies used in data analysis. 
 
Topics:
  • What is Statistical Inference?
  • Terminologies of Statistics
  • Measures of Centers
  • Measures of Spread
  • Probability
  • 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. 
 
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
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. 
 
Topics:
  • What is Machine Learning?
  • Machine Learning Use-Cases
  • Machine Learning Process Flow
  • Machine Learning Categories
  • Supervised Learning algorithm: Linear Regression and Logistic Regression
 
Hands-On/Demo:
  • 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. 
 
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
Learning Objectives - Learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data. 
 
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
Learning Objectives - In this module, you should learn about association rules and different types of Recommender Engines. 
 
Topics:
  • 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
 
Hands-On/Demo:
  • 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. 
 
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
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. 
 
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
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. 
 
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

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.

If you have a Windows system, you should have:
  • Microsoft Windows 7 or newer (32-bit and 64-bit)
  • Microsoft Server 2008 R2 or newer
  • Intel Pentium 4 or AMD Opteron processor or newer
  • 2 GB memory
  • 1.5 GB minimum free disk space
  • 1366 x 768 screen resolution or higher
 
If you have a MAC system, you should have:
  • iMac/MacBook computers 2009 or newer
  • OSX 10.10 or newer
  • 5 GB minimum free disk space
  • 1366 x 768 screen resolution or higher
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.
To help you in this endeavor, we have added a resume builder tool in your LMS. Now, you will be able to create a winning resume in just 3 easy steps. You will have unlimited access to use these templates across different roles and designations. All you need to do is, log in to your LMS and click on the "create your resume" option.
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.
All the instructors at  are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by  for providing an awesome learning experience.
You no longer need a credit history or a credit card to purchase this course. Using ZestMoney, we allow you to complete your payment with a EMI plan that best suits you. It's a simple 3 step procedure:
  • Fill your profile: Complete your profile with Aadhaar, PAN and employment details.
  • Verify your account: Get your account verified using netbanking, ekyc or uploading documents
  • Activate your loan: Setup automatic repayment using NACH to activate your loan