DATA SCIENCE

Data Science Training

Data Science training will help you prepare in various aspects of Data Science like Data Acquisition from various sources, Data preparation, Data transformation using Map Reduce, analyzing different methodologies in data science, Application of Machine Learning Techniques etc., We train you with our well-designed strategies and get you to job ready.

Data Science is an interdisciplinary system about processes and methods to extract expertise or observations from data in several forms, sometimes structured or unstructured, which is a continuation of a number of the data analysis systems, for example, data mining, analysis, and predictive analytics, much like Knowledge Discovery in Databases. Data science implements tactics and hypotheses drawn from many fields in the broad areas of mathematics, chemometrics, statistics, computer science and information science, including signal processing, machine learning, probability models, data mining, statistical learning, database, data engineering, learning and pattern recognition, predictive analytics, visualization, uncertainty modeling, data compression, computer programming, data warehousing, high performance computing and artificial intelligence.

What you’ll learn in Data Science Training Course?

• Deep understanding of the Roles of a Data Scientist
• How to use R, Hadoop, 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

Who can go for Data Science Training?

Data Science itself has many professions to enter into. One has good hands over programming and coding, any graduate or post-graduate with a mathematical or statistical background, a computer savvy well in analytics etc. can go for Data Science as their career option.

What are the pre-requisites to learn Data Science?

• No Pre-requisites are required for Data Science Online Course.

Why learn Data Science Online Training Course?

For the last decade, the data generated by companies and individuals has been a massive explosion. Data science plays a major role in handling and modulating all these data. It is flourishing rapidly across the globe and it will play a great role in the upcoming digital world. Data Science training will get you ready to part of this ever-growing technology. We have designed data science online training course in a way to give a boost to your career. By the completion of the training, you can confidently update your profile with hands-on experience.

Course Curriculum

MODULE 1: BASICS OF DATA SCIENCE     

• What is data science
• AI vs DS vs Machine learning
• Fields of data science
• Applications of Data Science
• Big Data

1. Definition of Big data
2. Applications of Big data
3. Hadoop and Spark
   1. Hadoop
      1. Map-reduce
      2. HDFS
   2. Spark
4. Tools and language

• Natural Language Processing
  1. Definition of NLP
  2. Application of NLP
  3. Tools and Language
• Machine learning
  1. Definition of Machine learning
  2. Types of Machine Learning
  3. Applications of Machine learning
  4. Tools and Languages
• NoSQL Databases
  1. Definition
  2. SQL vs NoSQL Databases
  3. NoSQL databases tools
  4. Search Engine technologies

MODULE 2: PYTHON BASICS

• Installation

1. Anaconda
2. Environment creation
3. Pycharm

• Interpreter
• Data types in Python
• String data types
• List
• Dictionary
• Tuple
• Set
• Functions
• Classes
• OOPS

1. Encapsulation
2. Inheritance
3. Abstraction

• Exceptional handling

MODULE 3: NUMPY, PANDAS AND SCIPY TUTORIAL

• Numpy Tutorial
• Pandas Tutorial
• Scipy Tutorial

MODULE 4: NATURAL LANGUAGE PROCESSING

• Basics of NLP
• Applications of NLP
• Tokenization
• Stopwords
• Stemming and lemmatization
• Part of Speech tagging
• Named entity recognition
• Custom NER system using OpenNLP (java)
• Phrase Handling Application
• Sentiment Analysis Application

1. Feature Extraction process
   1. True/False model
   2. Count Vectorizer
   3. TF-IDF Vectorizer
2. Creating Model using NLTK Naïve Bayes algorithm

• Recommendation System Application

MODULE 5: WEB CRAWLING

• Scrapy Introduction
• Xpath Introduction
• Crawling Application

MODULE 6: MACHINE LEARNING

• Basics of Machine Learning
• Types of Machine Learning Algorithms

1. Supervised
   1. Classification
      1. Logistic Regression
      2. K Nearest Neighbors
      3. SVM
      4. Decision Tree
      5. Random Forest
      6. Gradient Boosting
      7. Naïve Bayes
   2. Regression
      1. Linear Regression
      2. Polynomial Regression
      3. SVR
      4. Decision Tree Regressor
      5. Random Forest Regressor
   3. Unsupervised
      1. Clustering
         1. K Means Clustering
         2. Hierarchical Clustering

MODULE 7: MACHINE LEARNING MODEL EVALUATION

• Backward elimination Process
• P value
• R Squared
• Adjusted R Squared

MODULE 8: DEEP LEARNING    

• Basics of Deep Learning
• Neural Network

1. Application: Second Hand Bike Price Prediction using Dense Neural Network

1. CNN (Convolution Neural Network)
   1. Application: Image Classification Application
2. RNN (Recurrent Neural Network)
   1. LSTM (Long Short-Term Memory
   2. Application: Custom NER system using LSTM