Data Science Training in Delhi

• Data visualization
• linear algebra
• statistics
• probability
• Hypothesis and estimation
• Gradient descent method
• Acquisition of data
• Data manipulation
• Machine learning
• k neighborhood method
• Naive bays
• Simple linear regression
• Multiple regression analysis
• Logistic regression
• Decision tree
• neural network
• Clustering
• Natural language processing
• Network analysis
• Recommendation system
• Database and SQL

A Data science Certification Online Training makes students ready to take a certification exam in data science. In Data science Certification Online Courses, students are taught the necessary skills to get a job as data scientists. Data Science Course in Delhi is a suitable option for students who aspire to become certified data scientists.

Data science Certification Online Training study program emphasizes on imparting business intelligence. Data Science Course in Delhi helps the participants to formulate new business strategies and communicate the information mined from a large amount of relevant data.

Data science Certification Online Courses also enable the students to deal with product design, finance, and marketing. Data Science Course in Delhi also makes the participants well-versed in the different facets of data research. In the initial phases of Data science Certification, Online Training students will be introduced to data communication, sampling, and management. In the middle of the Data Science Online Course, data disputes, exploratory analysis, and prediction will be dealt with. Then students of Data Science Online Training will learn data regression and classification.

Data Science Course in Delhi, which gives Data science Certification Online Training, costs much less than an engineering degree course in a private college. Duration of Data science Online Training is also less, which is a maximum of one year.

Data science Certification Online Courses are valuable as it enables the participants to get a certificate for professional development. This certificate will help you to get a lucrative job as a data scientist in big companies in Bangalore, Chennai, Noida or Delhi. Many students who have passed the data science certification exams, after studying Data Science Course in Delhi, have joined good companies in Manyata tech park, outer ring road, and Embassy Golf Links, Domlur, in Bengaluru. Former students of Data science Certification Online Courses are now holding middle management roles at the executive level in offices in Southern Park, Saket, Delhi, and Statesman house, Connaught place, Delhi. Some institutes in this area provide Data Science Online Training.

This is because of Data Science some course in Delhi is job oriented training. After completing a Data science Online Course, the training institute gives you job support and placement assistance.

There are no special prerequisites to join the Data Science Online Course. But, to join Data Science Online Training, you must be familiar with the elementary concepts of Mathematics and Statistics like Calculus, Probability, and Linear Algebra. Fairly good knowledge in object-oriented programming such as C and Java is preferable when you join a Data science Online Course.

When you complete Data science Online Training, you’ll be able to do the following:

• Aggregate data: standardize them while maintaining their integrity.
• Interpret data: identify trends and sensitize recipients to the relevant use of the data.
• Assist in decision-making, with a view to improving the company’s strategy.
• Present your results: Summarize important conclusions with dashboard graphics or written reports.
• Collaborate with database administrators and managers: to suggest improvements in data collection and management.
• Correct potential problems in the processing chain, manage security aspects, and monitor technologies available.

• Capstone ProjecT; Domain: E-commerce
• Improving customer experience for BSNL; Domain: Telecom
• Attrition Analysis for IBM; Domain: Workforce Analytics
• Predict accurate sales for 25 stores of MORE; Domain: RetailLearn how cosmetics industry leaders make use of Data Science to leverage their business; Domain: Consumer products
• See how banks like SBI, IOB, and Bank of Baroda can make use of Data Science to stay ahead of the competition from new generation banks; Domain: banking

 Data science Online Training offers you many other projects as above.

The actual fees may differ from one Data science Online Course training institute to another. You can directly contact one which provides Online Data Science Training.

According to the payscale website, the average salary for a data scientist in India is Rs 707,155. Another site says it’s Rs 693,637 (IND) or $91,470 (US) per year.

To get this job, you must have completed Data science Online Training from a reputed institute through live online training or live classroom training.

You need to be able to use the statistical analysis software necessary to handle big data, and to understand “data mining” and “programming language”. Data Science Online Course will enable you to acquire such skills;

It often seems that marketing sense, communication skills, logical thinking skills, and marketing skills are also required to get a good position as a data scientist in a reputed firm. Data science Online Training from a good institute will enable you to rise in this field.

Many reputed companies having offices in Delhi, Delhi NCR and Noida hire data scientists. On June 14, 2019, the Naukri job site showed 562 vacancies of related jobs like principal data scientist,  data engineer, data scientist lead, etc. Data science Online Training with placement assistance will help you to grab one of these jobs. As the IT hub and silicon valley of India, Bangalore (Bengaluru)  also offers great opportunities for trained data scientists.

Overview of SAS

• Introduction and History of SAS
• Significance of SAS software solutions in various industries
• Demonstrate SAS Capabilities
• Job Profile / career opportunities with SAS worldwide?

Base SAS Fundamentals

• Explore SAS Windowing Environment
• SAS Tasks
• Working with SAS Syntax
• Create and submit a SAS sample program

Data Access & Data Transformation

• Accessing SAS Data libraries
• Getting familiar with SAS Data set

Reading SAS data set

• Introduction to reading data
• Examine structure of SAS data set
• Understanding of SAS works

Reading Excel worksheets

• Using Excel data as input
• Create as sample program to import and export excel sheets

Reading Raw data from External File

• Introduction to raw data
• Reading delimited raw data file (List Input)
• Using standard delimited data as input
• Using nonstandard delimited data as input
• Reading raw data aligned to columns (Fixed or column input)
• Reading raw data with special instructions (Formatted input)

Writing to an External file

• Write data values from SAS data set to an external file

Data transformations (Data step processing)

• Create multiple output datasets from single SAS dataset
• Writing observations to one or more SAS datasets
• Controlling which observations and variables to be written to output data

Creating subset of observations using

• Where condition
• Conditional processing using: IF statements

Processing Data Iteratively

• Iterative DO loop processing with END statement
• DO WHILE & DO UNTIL Statement
• SAS Array statement

Summarizing data

• Creating and Accumulating total variable (Retain)
• Using Assignment statement
• Accumulating totals for a group of data (BY group)

Manipulating Data

• Sorting SAS data sets
• Manipulating SAS data values
• Presentation of user defined values /data/currency values using FORMAT procedure
• SAS functions to manipulate char and num data
• Convert data type form char-to num and num-to-char
• SAS variables lists/ SAS variables lists range
• Debugging SAS program
• Accessing observations by creating index

Restructuring a SAS data set

• Rotating with the data step
• Using the transpose procedure

Combining SAS data sets

• Concatenation
• Interleaving
• One to one reading
• One to one merging (with non-matching)
• Match merging (Merging types with IN=option)

SAS Access & SAS Connect

• Validating and cleaning data
• Detect and correct syntax errors
• Examining data errors

Analysis & Presentation

• SAS/REPORTS SAS/GRAPH
• SAS/STATS SAS/ODS

Producing detailed /Summary Reports

• Freq Report
• Means Report
• Tabulate Report
• Proc report
• Summary report
• Univariate report
• Contents report
• Print report
• Compare proc
• Copy proc
• Datasets proc
• Proc append
• Proc delete

Generating Statistical Reports using

• Regression proc
• Uni/Multivariate proc
• Anova proc

Generating Graphical reports using

• Producing Bar and Pie charts (GCHART Proc)
• Producing plots (GPLOT Proc)
• Presenting Output Report result in:
• PDF
• Text files
• Excel
• HTML Files

SAS/SQL Programming

• Introduction and overview to SQL procedure
• Proc SQL and Data step comparisons

Basics Queries

• Proc SQL syntax overview
• Specifying columns/creating new columns
• Specifying rows/subsetting on rows
• Ordering or sorting data
• Formatting output results
• Presenting detailed data
• Presenting summarized data

Sub Queries

• Non correlated sub queries
• Correlated sub queries

SQL Joins (Combining SAS data sets using SQL Joins)

• Introduction to SQL joins
• Types of joins with examples
• Simple to complex joins
• Choosing between data step merges and SQL joins

SET Operators

• Introduction to set operations
• Except/Intersect/Union/Outer union operator

Additional SQL Procedures features

• Creating views with SQL procedure
• Dictionary tables and views
• Interfacing Proc SQL with the macro programming language
• Creating and maintaining indexes
• SQL Pass-Through facility

SAS Macro Language

• Introduction to macro facility
• Generate SAS code using macros
• Macro compilation
• Creating macro variables
• Scope or macro variables
• Global/Local Macro variables
• User defined /Automatic Macro variables
• Macro variables references
• Combing macro variables references with text
• Macro functions
• Quoting (Masking)
• Creating macro variables in Data step (Call SYMPUT Routine)
• Obtaining variable value during macro execution (SYMGET function)
• Creating macro variables during PROC SQL execution (INTO Clause)
• Creating a delimited list of values
• Macro parameters
• Strong Macro using Autocall Features
• Permanently storing and using stored compiled macro program
• SAS Macro debugging options to track problems

Basics Statistics

• Standard deviation
• Correlation Coefficients
• Outliers
• Linear regressions
• Clustering
• Chi Square

• What is Data Science?
• What is Machine Learning?
• What is Deep Learning?
• What is AI?
• Data Analytics & it’s types

Module 2: Introduction to R (Duration-1hr)

• What is R?
• Why R?
• Installing R
• R environment
• How to get help in R
• R Studio Overview

Module 3: R Basics (Duration-5hrs)

•  Environment setup
• Data Types
• Variables Vectors
• Lists
• Matrix
• Array
• Factors
• Data Frames
• Loops
• Packages
• Functions
• In-Built Data sets

Module 4: R Packages (Duration-2hrs)

• DMwR
• Dplyr/plyr
• Caret
• Lubridate
• E1071
• Cluster/fpc
• Data.table
• Stats/utils
• Ggplot/ggplot2
• Glmnet

Module 5: Importing Data (Duration-1hr)

• Reading CSV files
• Saving in Python data
• Loading Python data objects
• Writing data to csv file

Module 6: Manipulating Data (Duration-1hr)

• Selecting rows/observations
• Rounding Number
• Selecting columns/fields
• Merging data
• Data aggregation
• Data munging techniques

Module 7: Statistics Basics (Duration-11hrs)

• Central Tendency
  • Mean
  • Median
  • Mode
  • Skewness
  • Normal Distribution
•  Probability Basics
  • What does mean by probability?
  • Types of Probability
  • ODDS Ratio?
• Standard Deviation
  • Data deviation & distribution
  • Variance
• Bias variance Trade off
  • Underfitting
  • Overfitting
• Distance metrics
  • Euclidean Distance
  • Manhattan Distance
• Outlier analysis
  • What is an Outlier?
  • Inter Quartile Range
  • Box & whisker plot
  • Upper Whisker
  • Lower Whisker
  • Scatter plot
  • Cook’s Distance
• Missing Value treatments
  • What is a NA?
  • Central Imputation
  • KNN imputation
  • Dummification
• Correlation
  • Pearson correlation
  • Positive & Negative correlation

Module 8: Error Metrics (Duration-3hrs)

• Classification
  • Confusion Matrix
  • Precision
  • Recall
  • Specificity
  • F1 Score
• Regression
  • MSE
  • RMSE
  • MAPE

Module 9: Machine Learning

Module 10: Supervised Learning (Duration-6hrs)

• Linear Regression
  • Linear Equation
  • Slope
  • Intercept
  • R square value
•  Logistic regression
  • ODDS ratio
  • Probability of success
  • Probability of failure
  • ROC curve
  • Bias Variance Tradeoff

Module 11: Unsupervised Learning (Duration-4hrs)

• K-Means
• K-Means ++
• Hierarchical Clustering

Module 12: Machine Learning using R (Duration-10hrs)

• Linear Regression
• Logistic Regression
• K-Means
• K-Means++
• Hierarchical Clustering – Agglomerative
• CART
• 5.0
• Random forest
• Naïve Bayes