Data Science Training in Chennai

The field of data science is interdisciplinary, which encompasses a lot of areas including scientific methods, processes, systems, and algorithms. This field is deeply connected with big data and analytics. With proper training in data science, you make yourself eligible for recruitment in a lot of companies that have data handling jobs available with them. The world has been taken over by big data. And with the foothold of big data growth in the world, the need for its storage began to grow. Enterprise industries saw it as a challenge till somewhere around 2010. At that time, more emphasis was being laid on building solutions and frameworks for the storage of data. Then came frameworks like Hadoop that indeed solved the problem of data storage.

But then a new problem arose- processing of this stored data. Here comes the role of data science. In Hollywood sci-fi movies, you see a lot of seemingly unrealistic ideas. Believe it or not, all such ideas can be turned into reality with the help of data science. In another way, you can say that the future of artificial intelligence lies in data science. And that is probably the reason why data science and its courses are so much in demand. People want to learn it for lucrative IT career options. So, data science, which is a concoction of machine learning principles, algorithms, and various tools, is used to figure out concealed patterns in raw data. It is because of data science that data can be used to add value to any business. Predictive causal analytics, prescriptive analytics, and machine learning are the elements that together render data science helpful in making decisions and predictions. Here, it would be valuable to add that the future lies in the hands of data scientists. The need for data scientists that have expertise over the subject is growing with each passing day. What more? Well, data science is changing the way people look at the world flooded with data.

Combination of machine learning, Deep learning & AI.

• Data Science is the branch of science use the knowledge of Mathematics, Statistics, Computer Science and Business to analyze data insights and to take action based on these data. These scientific methods will help in making better decisions to solve critical business problems.
• Any company that makes data-driven decisions is dependent on its data scientists for its future.
• Data Scientist is someone who works with data and who has more expertise than that of a scientist, statistician, and a software engineer. Data Scientist should develop statistical algorithms and deal with big data.
• Data Engineer is someone who has a deeper understanding of data and its infrastructure and finds, transforms and cleans data.
• Data Analyst is someone who presents solutions to a business problem by analyzing data and using visualization techniques. Data analyst use Microsoft Excel, Tableau, SAS and work with queries. They don’t deal with Big data technologies.

• A data scientist should be expert in multiple skills such as Mathematics, statistics, Big Data.
• Data Scientist should have a deeper understanding of Mathematics, Statistics and should have good analytical skills to understand patterns in these data.
• Should be able to develop machine learning algorithms that are capable of doing predictive analysis and generate sample data based on given datasets.

Business Development & Analysis using Data.

• The fast increasing Global data, advancement of technologies, the growth of AI and machine learning – All these make big companies make data-driven decisions. Hence, Big companies depend on data to make important financial decisions. Data science is applied in Finance, Insurance, Gaming to make use of their structured and unstructured data and to make rational decisions.
• Data science has become inevitable in numerous fields and is undoubtedly the most-sought-after role nowadays.

• It could be a bit confusing to differentiate between Big data and Data science.
• Data Science is the branch of science that deals everything about data from the cleansing, preparation, analysis of data. This is applied primarily in internet searches, Fraud detection, Digital advertisements.
• Big data refers to complex and very large data sets which are unstructured and cannot be easily handled using traditional data processing tools. It involves data storage, analysis, visualization of these data and finds the insights from them to take strategic business decisions. This is applied in Financial services, Telecommunications, Commerce, Health and Sports..

• Data Scientist works at much a deeper level compared with the data analyst.
• Data analyst just works with analyzing and querying data using simple tools like SQL, Excel and provides solutions to the business problems presented to them. Data Scientist deals with Big data (Unstructured data).
• But, Data scientist creates business questions, analyses data patterns and creates a statistical model to find answers to those questions and use visualization techniques to help others understand their findings. They solve critical business problems and make companies take strategic decisions.

• Data Engineer deals with data warehouses. Data engineer is exposed to data architecture who designs, builds, works with data. Data engineer is responsible to maintain the accuracy and availability of data.
• Data Scientist, on the other hand, will develop deeper insights from the data maintained by the data engineer.

• Data engineers usually have a programming background whereas the data scientist comes from the mathematical background. According to Glassdoor, the number of jobs for Data engineers is 5 times more than the data scientist jobs.
• Data engineer develops, constructs, maintains data. Data engineer works with tools such as MongoDB, Redis, Hive, SAP, other database tools.
• Data Scientist develops insights and solves business needs. Data Scientist uses Tableau, SAS, SPSS, Rapidminer. Both of these roles use tools such as Spark, scale, etc.
• If you have good database background and strong technical skills, go for Data Engineer role.
• If you have good analytical skills, mathematical, statistical skills along with technical expertise, You can build your career as a Data Scientist.

Besant technologies provide the certificate for Data Science. It helps the candidates to prove their skills with industry standards.

Besant Technologies is a reputed institute that has trained over 10000 plus students. Besant technologies’ certificate for Data science will help you to kickstart your career as a data scientist and will help you to land in data science job. Besant Technologies certification will help you to gain real-time hands-on experience from industry guru’s.

The duration differs for weekday and weekend batches. For weekday batches, the estimate duration is between one to two months. For weekend batches, the estimate duration is about two months. For fees details, Kindly contact us.

You can take Exam untill you pass it. there is no limited attempts.

We conduct exams and provide certificates And if one fails, the exam can be retaken immediately. [

• You can start as a Data analyst.
• You can also get into Data scientist or
• Data engineer positions.
• There are still more roles to explore in Data science field.

Data scientist deals with big data and helps organizations take strategic business decisions in solving their critical business problems.

• Start-ups pay an average salary of about 10.8 lakhs to data scientists.
• Salary can go as high up to 25 lakhs per annum.
• Tier 1 companies pay the best in the industry for data scientists.

Data analyst start on an average of 3.5 lakhs per annum.

• Wipro,
• Infosys,
• Flipkart,
• Urban Ladder,
• Snapdeal,
• Amazon hires for various data science roles.

Data science is a very good career option to step up the ladder, as it is being one of the highest paid and niche skill of the decade. Data Science is the domain which has an evergreen future. A lot of companies invest good money in doing research and innovate in this field.

We will help you set up the software requirements in your personal laptops. Our trainers will guide you in setting up all the software requirements. It is highly recommended to carry your personal laptops in case of classroom training.

After setting up the software requirements, you can execute all classroom and hands-on labs in your personal laptop. We will give you practical assignments to do during class and at home. All exercises and course notes will be shared in softcopy only.

We will provide complete support for you to finish all your assignments. These assignments are designed based on industry problems and simulate the working experience in the relevant field.

Projects are live and are more closely related to real-time problems which help you to grasp the syllabus easily. Our projects will help you to have a deeper understanding of the subject.

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

Module 1: Introduction to Data Science (Duration-1hr)

• 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

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