” Over 1,50000 jobs available in Data science” 

                       Update yourself now to grab one of that opportunity!!!!!!!!!

Trainer Profile:

Trainer Profile: Masters from IISC bangalore , IIT Dhanbad, Author of a book on pyspark. Overall 11+ year IT working experience&4+ years of experience in corporate trainings. Delivered more than 30 corporate and online trainings.Worked as software developer and performance engineer in Oracle&Tata Steel and an author as well.A sophisticated communicator with good convincing, Presentation, and coordination skills.

Pre Requisite:

• Familiarity with Maths and statistics – One should be good at statistics and possess an analytical aptitude. The majors from which data science borrows are statistics, mathematics, economics, operations research, and computer science. Therefore, if you wish to become an adept Data Scientist, you should have studied some or one of these subjects and you should be able to design experiments and test hypotheses.
• Programming – It’s vital to know the basic concepts of object oriented programming like C, C++ or Java to ease the process of learning data science programming tools like Python and R.
• Basics of SQL – It’s important to know how to write a basic SQL query and having familiarity with joins, group by, having, creating indexes, etc. to get the data out, for analysis. Regardless of whether the data is to be retrieved from a database or a Hadoop cluster, there is SQL language layer always present on the top.
• Have passion to develop business acumen – Though Data Scientists work with high-end technologies, the beneficiaries are the business as a whole. The primary business expectation from a Data Scientist is a reliable IT system that delivers data-driven decisions for day-to-day business problems. So, when thinking of businesses, one has to keep in mind that the aim of a business solution is not to showcase technology, but to solve the identified business solution.
• Curious about playing with data

R-Programming

•  Introduction to R Basics
• Getting help
• Structure of program in R
• Packages
• Basic Data type
• Advance data type
• Loops and conditional
• Advanced loop
• String Manipulation in R
• Function in R
•  Mathematical functions
• Graphics in R 
• R connection with Database
• Debugging in R

Statistics In R

• Hypothesis Testing In R
• Test of mean
• Test of variance
• Chi- Square Test
• Non- Parametric Tests

Data Mining

• Machine Learning
• Linear Regression
• Classification 
• Logistic regression
• Classification Evaluation metrics 
• Decision tree 
• Ensemble learning 
• Bayes Classification 
• Neural Networks 
• Clustering
• Text Analysis in R
• Introduction to deep Learning

Python For Data science

• Python Basic
• Introduction to Python
• Running Python from eclipse IDLE
• Data type in Python
• Typecasting data
• Operators in python
• Collections in python:

• Conditionals and Looping
• Introduction to conditionals if
• Strings
• Introduction to Strings String Operations
• Functions modules and Package
• Exception handling in python
• IO in python
• Reading and writing excel files.

• Class and Objects :
• Class in Python Constructors Objects
• Debugging in Python
• Pandas
• Python and Markup language :
• Python and XML
• Python and HTML
• Python and JSON :
• Seaborn :
• Scikit-Learn
• Linear regression
• Classification
• Logistic Regression

• Clustering

Job Opportunities:

Course outcomes:

• Apply datascience techniques to your organisation’s data management challenges
• Identify and avoid common pitfalls in bigdata analytics
• deploy machine learning algorithms to mine data
• Interpret analytical models to make better business decisions
• understand the challenges associated with scaling bigdata algorithms
• Convert datasets to models through predictive analysis

Course Duration:   60 hours

Type of Training  : Classroom

Location                 : Bangalore