Best Data Science Using Julia Training in Delhi NCR
WHAT IS DATA SCIENCE USING JULIA?
CETPA Infotech offers data science with Julia training in Noida which will help you to understand Julia’s rich ecosystem and includes the essentials of data science, providing you with a high-level summary of advanced statistics and methods. The benefits of Julia for data science cannot be underestimated. Apart from speed and ease of use, there are already over 1,900 packages available and Julia can interface (either directly or with the help of packages) with libraries written in Python, R, MATLAB, C, FORTAN, or C++
The Julia programming language and environment were designed in 2009. They are free from various disadvantages that other solutions created for single-processor and low memory machines have. Data science using Julia course in Noida is designed for those who seek the in- depth data exploration. Even the basics of Julia language facilitate you to improve your skills on your own thanks to the rich documentation. The great efficiency of this environment makes the work with a huge set of data a pleasant and creative task.
Data science using Julia course in Delhi NCR is suitable for:
- Data analysts and budding data scientists who are new to Julia
- Those competent in Python and R and wish to adopt Julia to enhance their skills set in Data Science
- Data Scientists, data analysts, engineers, programmers, and other professionals
- Free Lancers
There is number of reasons which make CETPA best Data Science using Julia Training Institute in Noida. Some of them are:
- CETPA is certified by ISO 9001:2015 certification.
- Training Partner with brand names like Microsoft, PMKVY, Panasonic, Oracle & Autodesk many more.
- 100% Placement Guidance after completion of your training.
- Enhancement of Technical Skills along with the Soft Skills.
- Interview preparation
- Trainers with real-time Industry Experience
- Learn by working on live projects. need and convenience.
- Training is provided using the mixture of both academic and practical.
- Online training is also provided, apart from Classroom Training.
- A Hands-on methodology to help you master the domain.
Mode/Schedule of Training:
CETPA, The Best DATA SCIENCE USING JULIA Training Institute in Delhi NCR offers courses in following modes.
|Delivery Mode||Location||Course Duration||Schedule (New Batch Starting)|
|Classroom Training (Regular/ Weekend Batch)||*Noida/ Lucknow *Dehradun /Roorkee||4/6/12/24 weeks||New Batch Wednesday/ Saturday|
|*Instructor -Led Online Training||Online||40/60 Hours||Every Saturday or as per the need|
|*Virtual Online Training||Online||40/60 Hours||24×7 Anytime|
|College Campus Training||India or Abroad||40/60 Hours||As per Client’s need|
|Corporate Training (Fly a Trainer)||Training in India or Abroad||As per need||Customized Course Schedule|
Introduction to Data Science
- What is Data and it’s use in presentworld ?
- What are the sources of Data ?
- Exponential Growth in Data.
- Different types of Data.
- Structured, Semi-structured andUnstructured Data.
- What is Data Science?
- Need of Data Science in present world.
- What is Data Mining?
- What is Data Analytics?
Processes of Data Analytics
- Data collection from different sources.
- What is data sampling?
- Data Processing and preprocessing.
- Data processing based on differentkinds of data.
- Data Cleaning before processing.
- Handling the missing values in Data.
- Different algorithm implementation ondata
- Different tools available for dataanalytics
- Introduction to and hands on toGitHub.
Basic Concepts inJulia programming language
- Introduction to Julia language
- History of Julia language
- Why to Learn Julia ProgrammingLanguage?
- Datatypes in Julia language
- Operators in Julia language
- Introduction to loops
- Introduction to Conditions
- Mathematical Operators in Julia
- Arithmetic Operators
- Bitwise operators
- Numeric Comparisons
- Operator Precedence
- Numeric Conversions
- Strings in Julia
- String basics
- Trigonometric Plots in Julia
- Customizing the graphs with Julia
- Whiskers graph in Julia
- Line Graphs in Julia
- Pie Chart using Julia
- Funnel Chart with Julia
- Relative Frequency Histogram
- Segmented Bar Graph
Different Packages in Julia
- Introduction to basic plot package ‘Plots’
- Introduction to Gadfly
- Working with IJulia package
- Working in notebook with Julia
- Working with Clustering Package
- Working with Plotly package
- Working with Winston Package
- Introduction to Dataframe package
- Working with dataframes
- Introduction to GLM Package
- Implementing the Linux commands inJulia
- Implementing python and R packages inJulia
- Using Calculus package in Julia
Statistics used in Data Analytics
- What is statistics?
- Usefulness of statistics in Data Science
- Understanding the use of mean, medianand mode in real dataset
- Interpretation of variance and standarddeviation on dataset
- Understanding the meaning of covariance
- Understanding and Implementation ofBinomial Distribution
- Implementation of Linear Regression ondataset
- Implementation of Multiple Regressionon dataset
- Implementation of Logistic Regression
- Understanding and Implementation ofPoisson Distribution
- Understanding and Implementing TimeSeries
- Conducting the Chi- Square Test
- Meaning of R square
- Meaning of Z score
- Survival analysis using Julia
Machine Learning with Julia
- What is machine learning?
- Supervised Learning
- Unsupervised Learning
- Reinforced Learning
- K-Means Clustering
- KNN Algorithm
- Naïve Bayes Algorithm
- Support Vector Machine
- Principle Component Analysis
- Implementing Decision Tree
Introduction to Big Data
- What is Big Data ?
- Sources of Big Data in present world.
- Features of Big Data.
- Importance of Big Data in real world.
- What are the problems of Big Data?
- Why conventional methods can’t process the Big Data?
- Tools available to process the Big Data
- Data Handling using conventional tools(oracle, mysql, postgre)
- Hands on to the core Java
Introduction to Hadoop
- What is Hadoop?
- History of Hadoop
- Why Hadoop was needed to handlebigdata?
- Architecture of Hadoop
- Components of Hadoop and theirworking
- Algorithm behind the working of Hadoop
- Hadoop Distributed File System
- Map Reduce Algorithm in Hadoop andit’s dissection
- Map Reduce Programming hands on inHadoop
A mini Project on Data Science using learnt tools
- Classroom Training
- Online Training
- Corporate Training
- On campus Training