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DATA SCIENCE ONLINE TRAINING

USA – + 1-904-304-2519 IND - +91-9849038359 www.iqonlinetraining.com

IQ training facility offers Data Science Online Training. We handpicked highly experienced trainers after careful analysis on several

factors. Real time industry experts have designed our course material with the utmost care, covering every topic and secured it

online for your reference. Trainer is a real time Data Science Professional having a vast knowledge on all the concepts, which adds a

great value to the course during the training period. All our students were happy and able to find Jobs quickly in USA, UK, Singapore,

Canada, and Europe. Data Science Course is your one stop solution to learn at the comfort of your home with flexible schedules.

Data Science Course Content:



DATA SCIENCE ROADMAP



1. BASICS OF DATA SCIENCE

i. What is data scienceii. AI vs DS vs Machine learning

iii. Fields of data scienceiv. Applications of Data Sciencev. Big Data

a. Definition of Big datab. Applications of Big datac. Hadoop and Spark

i. Hadoop1. Map reduce2. HDFS

ii. Sparkd. Tools and language

vi. Natural Language Processinga. Definition of NLPb. Application of NLPc. Tools and Language



vii. Machine learninga. Definition of Machine learningb. Types of Machine Learningc. Applications of Machine learningd. Tools and Languages

viii. NoSQL Data basesa. Definitionb. SQL vs NoSQL Databasesc. NoSQL databases toolsd. Search Engine technologies

2. PYTHON BASICS

a. Installationi. Anaconda

ii. Environment creationiii. Pycharm

b. Interpreterc. Data types in Pythond. String data types



e. Listf. Dictionaryg. Tupleh. Seti. Functionsj. Classesk. OOPS

i. Inheritanceii. Encapsulationiii. Abstraction

l. Exceptional handling

3. NUMPY, PANDAS AND SCIPY TUTORIAL

a. Numpy Tutorialb. Pandas Tutorialc. Scipy Tutorial



4. NATURAL LANGUAGE PROCESSING

a. Basics of NLPb. Applications of NLPc. Tokenizationd. Stop words

e. Stemming and lemmatizationf. Part of Speech taggingg. Named entity recognition

h. Custom NER system using OpenNLP (java)i. Phrase Handling Applicationj. Sentiment Analysis Application

i. Feature Extraction process1. True/False model2. Count Vectorizer3. TF-IDF Vectorizer

ii. Creating Model using NLTK Naïve Bayes algorithmk. Recommendation System Application



5. WEB CRAWLING

a. Scrapy Introductionb. Xpath Introductionc. Crawling Application

6. MACHINE LEARNING

a. Basics of Machine Learningb. Types of Machine Learning Algorithms

i. Supervised

1. Classificationa. Logistic Regressionb. K Nearest Neighborsc. SVMd. Decision Treee. Random Forestf. Gradient Boostingg. Naïve Bayes



2. Regressiona. Linear Regressionb. Polynomial Regressionc. SVRd. Decision Tree Regressore. Random Forest Regressor

ii. Unsupervised1. Clustering

a. K Means Clusteringb. Hierarchical Clustering

7. MACHINE LEARNING MODEL EVALUATION

a. Backward elimination Processb. P valuec. R Squaredd. Adjusted R squared



8. DEEP LEARNING

a. Basics of Deep Learningb. Neural Network

i. Application: Second Hand Bike Price Prediction using Dense Neural Networkc. CNN (Convolution Neural Network)

i. Application: Image Classification Application

d. RNN (Recurrent Neural Network)i. LSTM (Long Short Term Memory

ii. Application: Custom NER system using LSTM

Note: The tutorial is application development tutorial where we cover all major applications that can be build using NLP, Machine Learning, Deep Learning



Additions:a. Search Engine Setupb. Semantic Search Application from scratchc. ChatBot Application from scratch

Chapter 1: Basics of Data ScienceWhen we say the word data science, what does it means? Data means raw bit of information on the other hand science means some scientific method, so when we apply some scientific method on raw bit of information, it called data science.Scientific methods can be applying some computer science techniques, statistics, machine learning, visualization, collecting, cleaning, analyzing and interaction on raw bit of information (data).

Data science applications:

You can create many applications like:a. Semantic searchb. Chatbotc. Parsers (resume parser, job parser... etc.)d. Recommendation systemse. Image classificationf. Speech conversiong. Character recognitionh. Price comparison websites Etc..,



Semantic Search: You can create search application using different tools that are available in the market like solr, lucene, elastic search …etc. But these search engines are just a normal keyword search; they are not that much intelligent or smart. You can use data science techniques to create smart search application like Semantic Search.Let me take an example of semantic search. Let’s suppose we are working on jobs datasets, where we have jobs data like,

Job 1Role: Java Developer Location: Delhi Industry: ITSkills: Java, spring, hibernate, j2ee

Job 2Title: Solr Developer Location: Noida Industry: ITSkills: Solr, Lucene, Search, Java

Job 3Title: Programmer Location: Delhi Industry: ITSkills: Spring, hibernate, J2ee



As given above we have 3 jobs in our database. If a user is searching for “java developer Delhi”. The system will break the sentences into words like: “java”, “developer”, “Delhi” and will search for job having the keywords. The result we will get job 1 only. The problem here even the job 3 was a java developer job, the system won’t show that job. So the goal of semantic search is to understand the query intent and some semantic terms to it.

The semantic will work in 3 parts:1. Find the phrases in the query. Here we have phrases “java developer” and “Delhi”.2. Categorize the keyword. Here “java developer” is a role and “Delhi” is a location.3. Find the semantic terms of the keyword. Here the semantic of “java developer” will be “spring”, “hibernate”

and “j2ee”.

Chatbot applications are very popular applications now a days. Industry don’t want to invest a sep



Trainer provides Assignments Every Week On The Topics Covered

Test Cases Will Be Provided By The End Of Each Scenario

Ready Reference Material Will Be Provided

Mode of Delivery

• Instructor – Led Live Training

• Sessions consists of Theory & Practical Demo.

• Small Exercises to Practice the topics covered during sessions.

• Insights from Certification Perspective.

• Daily Assignment: Incremental assignment evolving everyday

• Review of Assignment/ Queries



Outcome Deep understanding of the Roles of a Data Scientist

How to use R, and Machine Learning to analyze Big Data

Understand the Life Cycle of Data Analysis

Learn the techniques and tools for data transformation

Understand Data Mining techniques and their implementation

How to use machine learning algorithms in R to analyze data

Understanding data optimization and visualization techniques

Understand the parallel processing features in R

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