|Duration of Course||2 Months|
|Mode of Training||Online/ Offline|
This is a complete Data Science boot camp specialization training course from ZaranTech that provides you with detailed learning in Data Science, Data Analytics, project life cycle, data acquisition, analysis, statistical methods and Machine Learning. You will gain expertise to deploy Recommenders using R programming, and you will also learn data analysis, data transformation, experimentation and evaluation.
What will you learn in this Data Science course online training?
Who should take up this Data Science online course?
Big Data, Business Intelligence and Business Analyst Professionals, Information Architects, Statisticians, Developers looking to master Machine Learning and Predictive Analytics and those looking to take up the roles of Data Scientist and Machine Learning Experts
What are the prerequisites for learning Data Science?
There are no particular prerequisites for this training course. If you love mathematics, it is helpful to learn Data Science. You will also get MS Excel self-paced course free with this course.
Why should you take up the Data Scientist certification course online?
The demand for Data Scientists far outstrips the supply of them. This is a serious problem in a data-driven world that we are living in today. Most of the organizations are ready to pay top-dollar salaries for professionals with the right Data Science skills. This Data Science course online will provide you with all skills needed to master Data Science along with Big Data, Data Analytics and R programming. All this means that you can fast-track your career to take on more lucrative and promising job roles and take your career to the next level.
What is the average salary for a Data Scientist in the US?
DSM Job Outlook
Which are the top companies hiring Data Scientist professionals?
Today, every company is hiring Data Scientists. Here are some of the top companies hiring Data Scientists: Google, Amazon, Microsoft, IBM, Facebook, Walmart, Visa, Target, Bank of America and others.
What are the different paths to enter Data Science?
There are multiple paths to becoming a Data Scientist. There are a set of tools that are being extensively used by a Data Scientist like the programming languages of R and Python, along with analytical tools like SAS and others. The person should be well aware of data analytics and statistical packages. He should also be aware of Big Data Hadoop and Spark which can be very useful for a Data Scientist. When the data is converted into business insights, the Data Scientist is supposed to have a good knowledge of various visualization and reporting tools. He should be firmly grounded in various aspects such as coming up with compelling visualizations, charts, maps and reports that can help anybody to understand the data.
How Data Scientists are different from Business Analysts or Data Analysts?
What Data Science projects will you be working in this Data Science training?
This course includes real-life industry-based projects, which will help you in gaining hands-on experience and prepare you for challenging Data Science roles
How is Zaran Tech Data Science Certification awarded?
Zaran Tech follows a rigorous certification process. To become a certified Data Scientist, you must fulfill the following criteria:
Online Instructor-led Course
Successful completion of all projects, which will be evaluated by trainers
Scoring a minimum 60% in the Data Science quiz conducted by Zaran Tech
Completing all course videos in our LMS
Scoring a minimum 60% in the Data Science quiz conducted by Zaran Tech
What does a Data Scientist do?
Understand the Problem
Collect Enough Data
Process the Raw Data
Explore the Data
Analyze the Data
Communicate the Results
Unit 1: Introduction to Data Science with R
Hands-on Exercise – Installation of R Studio, implementing simple mathematical operations and logic using R operators, loops, if statements and switch cases.
Unit 2: Data Exploration
Hands-on Exercise – Accessing individual elements of customer churn data, modifying and extracting the results from the dataset using user-defined functions in R.
Unit 3: Data Manipulation
Hands-on Exercise – Implementing dplyr to perform various operations for abstracting over how data is manipulated and stored.
Unit 4: Data Visualization
Hands-on Exercise – Creating data visualization to understand the customer churn ratio using charts using ggplot2, Plotly for importing and analyzing data into grids. You will visualize tenure, monthly charges, total charges and other individual columns by using the scatter plot.
Unit 5: Introduction to Statistics
Hands-on Exercise – Building a statistical analysis model that uses quantifications, representations, experimental data for gathering, reviewing, analyzing and drawing conclusions from data.
Unit 6: Machine Learning
Hands-on Exercise – Modeling the relationship within the data using linear predictor functions. Implementing Linear & Logistics Regression in R by building model with ‘tenure’ as a dependent variable and multiple independent variables.
Unit 7: Logistic Regression
Hands-on Exercise – Implementing predictive analytics by describing the data and explaining the relationship between one dependent binary variable and one or more binary variables. You will use glm() to build a model and use ‘Churn’ as the dependent variable.
Unit 8: Decision Trees & Random Forest
Hands-on Exercise – Implementing Random Forest for both regression and classification problems. You will build a tree, prune it by using ‘churn’ as the dependent variable and build a Random Forest with the right number of trees, using ROCR for performance metrics.
Unit 9: Unsupervised learning
Hands-on Exercise – Deploying unsupervised learning with R to achieve clustering and dimensionality reduction, K-means clustering for visualizing and interpreting results for the customer churn data.
Unit 10: Association Rule Mining & Recommendation Engine
Hands-on Exercise – Deploying association analysis as a rule-based machine learning method, identifying strong rules discovered in databases with measures based on interesting discoveries.
Unit 11: Introduction to Artificial Intelligence
Introducing Artificial Intelligence and Deep Learning, what is an Artificial Neural Network, TensorFlow – computational framework for building AI models, fundamentals of building ANN using TensorFlow, working with TensorFlow in R.
Unit 12: Time Series Analysis
What is Time Series, techniques and applications, components of Time Series, moving average, smoothing techniques, exponential smoothing, univariate time series models, multivariate time series analysis, Arima model, Time Series in R, sentiment analysis in R (Twitter sentiment analysis), text analysis.Hands-on Exercise - Analyzing time-series data, the sequence of measurements that follow a non-random order to identify the nature of the phenomenon and to forecast the future values in the series.
Unit 13: Support Vector Machine - (SVM)
Introduction to Support Vector Machine (SVM), Data classification using SVM, SVM Algorithms using Separable and Inseparable cases, Linear SVM for identifying margin hyperplane.
Unit 14: Naïve Bayes
What is Bayes theorem, What is Naïve Bayes Classifier, Classification Workflow, How Naive Bayes classifier works, Classifier building in Scikit-learn, building a probabilistic classification model using Naïve Bayes, Zero Probability Problem.
Unit 15: Text Mining
Introduction to concepts of Text Mining, Text Mining use cases, understanding and manipulating text with ‘tm’ & ‘stringR’, Text Mining Algorithms, Quantification of Text, Term Frequency-Inverse Document Frequency (TF-IDF), After TF-IDF.
Case Study 1: The Market Basket Analysis (MBA)
This case study is associated with the modeling technique of Market Basket Analysis where you will learn about loading of data, various techniques for plotting the items and running the algorithms. It includes finding out what are the items that go hand in hand and hence can be clubbed together. This is used for various real world scenarios like a supermarket shopping cart and so on.
Case Study 2: Logistic Regression
In this case study you will get a detailed understanding of the advertisement spends of a company that will help to drive more sales. You will deploy logistic regression to forecast future trends, detect patterns, uncover insights and more all through the power of R programming. Due to this the future advertisement spends can be decided and optimized for higher revenues.
Case Study 3: Multiple Regression
You will understand how to compare the miles per gallon (MPG) of a car based on the various parameters. You will deploy multiple regression and note down the MPG for car make, model, speed, load conditions, etc. It includes the model building, model diagnostics, checking the ROC curve, among other things.
Case Study 4: Receiver Operating Characteristic (ROC)
You will work with various data sets in R, deploy data exploration methodologies, build scalable models, predict the outcome with the highest precision, diagnose the model that you have created with various real-world data, check the ROC curve and more.
Data Science Projects
Project 1 : Augmenting retail sales with Data Science
Industry : RetailProblem Statement : How to deploy the various rules and algorithms of Data Science for analyzing stationary store purchase data.Topics : In this project you will deploy the various tools of Data Science like association rule, Apriori algorithm in R, support, lift and confidence of association rule. You will analyze the purchase data of the stationary outlet for three days and understand the customer buying patterns across products.Highlights:
Association rules for transaction dataAssociation mining with Apriori algorithmGenerating rules and identifying patterns
Project 2 : Analyzing pre-paid model of stock broking
Industry : FinanceProblem Statement : Finding out the deciding factor for people to opt for the pre-paid model of stock broking.Topics : In this Data Science project you will learn about the various variables that are highly correlated in pre-paid brokerage model, analysis of various market opportunities, developing targeted promotion plans for various products sold under various categories. You will also do competitor analysis, the advantages and disadvantages of pre-paid model.Highlights:
Deploying the rules of statistical analysisImplementing data visualizationLinear regression for predictive modeling.
Project 3 : Cold Start Problem in Data Science
Industry : EcommerceProblem Statement : How to build a recommender system without the historical data availableTopics : This project involves understanding of the cold start problem associated with the recommender systems. You will gain hands-on experience in information filtering, working on systems with zero historical data to refer to, as in the case of launching a new product. You will gain proficiency in working with personalized applications like movies, books, songs, news and such other recommendations. This project includes the various ways of working with algorithms and deploying other data science techniques.Highlights:
Algorithms for RecommenderWays of RecommendationTypes of Recommendation -Collaborative Filtering Based Recommendation, Content-Based RecommendationComplete mastery in working with the Cold Start Problem.
Project 4 : Recommendation for Movie, Summary
Industry : EcommerceTopics : This is real world project that gives you hands-on experience in working with a movie recommender system. Depending on what movies are liked by a particular user, you will be in a position to provide data-driven recommendations. This project involves understanding recommender systems, information filtering, predicting ‘rating’, learning about user ‘preference’ and so on. You will exclusively work on data related to user details, movie details and others. The main components of the project include the following:
Recommendation for movieTwo Types of Predictions – Rating Prediction, Item PredictionImportant Approaches: Memory Based and Model-BasedKnowing User Based Methods in K-Nearest NeighborUnderstanding Item Based MethodMatrix FactorizationDecomposition of Singular ValueData Science Project discussionCollaboration FilteringBusiness Variables Overview
Project 5 : Prediction on Pokemon dataset
Industry : GamingProblem Statement : For the purpose of this case study, you are a Pokemon trainer who is on his way to catch all the 800 PokemonsTopics : This real-world project will give you a hands-on experience on the data science life cycle. You’ll understand the structure of the ‘Pokemon’ dataset & use machine learning algorithms to make some predictions. You will use the dplyr package to filter out specific Pokemons and use decision trees to find if the Pokemon is legendary or not.
dplyr package to filter PokemonsDecision Tree algorithmLinear regression algorithm.
Project 6 : Book Recommender System
Industry : E-commerceProblem Statement : Building a book recommender system for readers with similar interestsTopics : This real-world project will give you a hands-on experience in working with a book recommender system. Depending on what books are read by a particular user, you will be in a position to provide data-driven recommendations. You will understand the structure of the data and visualize it to find interesting patterns.
Data analysis & visualizationRecommender LabUser Based Collaborative Filtering Model.
Project 7 : Capstone
Industry : AnalyticsProblem Statement : Predicting if the customer will churn or not.Topics : An end-to-end capstone project comprising:
Manipulating and envisioning the data for insights.Implementing the linear regression model to predict continuous values.Implementing classification models – decision tree, logistic regression, and random forest on “customer churn”.
An end-to-end capstone project covering all the modules. You’ll start off by manipulating and visualizing the data to get interesting insights. Then you’d have to implement the linear regression model to predict continuous values. Following which you’ll implement these classification models – logistic regression, decision tree & random forest on the “customer churn” data frame to find if the customer will churn or not.