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.

Masters degree or a Ph.D. in data science is required for professionals who need to place themselves in the top positions with some good years of experience. But it is not required for a beginner and also there is the number of possible ways available today to learn data science for working professionals even which are flexible yet valuable. The real Data science experience is gained by the real time experience gained in addition to the course. But still, expertise with a Ph.D. can always prove with their knowledge and be in the top positions. This scenario is very true with Google, as they recruit only the Ph.D. or Masters degree holders for Data scientist positions.

People from diverse backgrounds like economics, operations research, statistics, computer science, mathematics etc are practicing as the data scientist. This is because, being familiar with the basic concepts of statistics and maths with linear algebra, probability, calculus etc is essential to understand data science.

Programming and well versed with its terms is the basic requirement of a data scientist. Having a basic knowledge and programming skills of C, C++ or Java is required, as this will help in understanding the programming tools like R ad Python. Writing efficient code and analyze big data with those codes is all about data science. Though expertise programming skill is not required the basic knowledge is mandatory.

SQL language fundamentals in creating indexes, group by, joins etc is necessary to understand data science. Also, the basic SQL knowledge will help in understanding the hadoop cluster. The emerging technologies like MapReduce, Hive, and Pig etc will be easy for a SQL and HADOOP programmers to learn.

Though a data science degree is not required, it will help in better understanding the concepts. Today, technology is helping us to learn all these courses through MOOC from wherever we are. But still learning these courses will not be helpful in learning the soft skills and managing skills required for data scientist job.

Machine learning, being an integral part of data science, can be learned during the intermediate or advanced levels. But if you are skilled with machine learning, then you are half the way in your expert skills.

Introduction

• Why do we need Python?
• Program structure

Execution steps

• Interactive Shell
• Executable or script files
• User Interface or IDE

Memory management and Garbage collections

• Object creation and deletion
• Object properties

Data Types and Operations

• Numbers
• Strings
• List
• Tuple
• Dictionary
• Set
• Other Core Types
• Conversion between data types

Statements and Syntax

• Assignments, Expressions and prints
• If tests and Syntax Rules
• While and For Loops
• Break and continue

File Operations

• Opening a file
• Using Files – txt ,Csv, Xlsx
• How to connect MySQL
• Find and replace
• Appending to file
• Exporting file

Functions

• Function definition and call
• Function Scope
• Arguments
• Function Objects
• Anonymous Functions
• Packaging Importing

OOPS

• Classes and Objects
• Creating object
• Working with Class and Instance Variables Together
• Accessing Object Variables
• Accessing Object Functions
• Inheritance
• Multiple Inheritance
• Constructor
• Operator Overloading
• Polymorphism
• Encapsulation
• Abstract class and methods

Pandas Section

• Python Pandas – Introduction
• Introduction to Data Structures
• Python Pandas – Series
• Python Pandas – DataFrame
• Python Pandas – Basic Functionality
• Python Pandas – Descriptive Statistics
• Python Pandas – Indexing and Selecting Data
• Python Pandas – Function Application
• Python Pandas – Reindexing
• Python Pandas – Iteration
• Python Pandas – Sorting
• Python Pandas – Working with Text Data
• Python Pandas – Options and Customization
• Python Pandas – Missing Data
• Python Pandas – GroupBy
• Python Pandas – Merging/Joining
• Python Pandas – Concatenation
• Python Pandas – IO Tools
• Python Pandas – Comparison with SQL
• Python Pandas – Dates Conversion
• Plotting Data

Machine Learning Techniques

All algorithm will be explained by

• What is the mathematics behind it
• Which scenario want to use
• How it is different from other algorithms
• How to interpret with Python
• What insights getting out from the result
• Hypothesis Testing
• Correlation
• Outlier Detection
• T-test
• Anova
• Chi-square
• Linear regression
• Multiple regression
• Logistics Regression
• Naïve Bayes classifier
• K means clustering
• Decision tree
• SVM
• Time series forecasting Overview

For More Details About Data Science with SAS Course Click Here! →

Introduction to Data Analytics

Objectives

• This module introduces you to some of the important keywords in R like Business Intelligence, Business Analytics, Data and Information.
• You can also learn how R can play an important role in solving complex analytical problems.
• This module tells you what is R and how it is used by the giants like Google, Facebook, etc.
• Also, you will learnuseof ‘R’ in the industry, this module also helps you compare R with other software in analytics, install R and its packages.

Topics

• Business Analytics, Data, Information
• Understanding Business Analytics and R
• Compare R with other software in analytics
• Install R
• Perform basic operations in R usingcommandline
• Learn the use of IDE R Studio
• Use the ‘R help’ feature in R

Introduction to R programming

Objectives

• This module startsfromthe basics of R programming like datatypes and functions.
• In this module, we present a scenario and let you think about the options to resolve it, such as which datatype should one to store the variable or which R function that can help you in this scenario.
• You will also learn how to apply the ‘join’ function in SQL.

Topics

• Variables in R
• Scalars
• Vectors
• Matrices
• List
• Data frames
• Using c, Cbind, Rbind, attach and detach functions in R
• Factors

Data Manipulation in R

Objectives

• In this module, we start with a sample of a dirtydata setand perform Data Cleaning on it, resulting in a data set, which is ready for any analysis.
• Thus using and exploring the popular functions required to clean data in R.

Topics

• Data sorting
• Find and remove duplicates record
• Cleaning data
• Recoding data
• Merging data
• Slicing of Data
• Merging Data
• Apply functions

Data Import techniques in R

Objectives:

• This module tells you about the versatility and robustness of R which can take-up data in a variety of formats, be it from a CSV file to the data scraped from a website.
• This module teaches you various data importing techniques in R.

Topics

• Reading Data
• Writing Data
• Basic SQL queries in R
• Web Scraping

Exploratory Data Analysis

Objectives

• In this module, you will learn that exploratory data analysis is an important step in the analysis.
• EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis. You will also learn about the various tasks involved in a typical EDA process.

Topics

• Box plot
• Histogram
• Pareto charts
• Pie graph
• Line chart
• Scatterplot
• Developing Graphs

Basics of Statistics & Linear & Logistic Regression

Objectives

• This module touches the base of Descriptive and Inferential Statistics and Probabilities & ‘Regression Techniques’.
• Linear and logistic regression is explained from the basics with the examples and it is implemented in R using two case studies dedicated to each type of Regression discussed.

Topics

• Basics of Statistics
• Inferential statistics
• Probability
• Hypothesis
• Standard deviation
• Outliers
• Correlation
• Linear & Logistic Regression

Data Mining: Clustering techniques, Regression & Classification

Objectives

• Linear and logistic regression is explained from the basics with the examples and it is implemented in R using two case studies dedicated to each type of Regression discussed.
• The two Machine Learning types are Supervised Learning and Unsupervised Learning and the difference between the two types.
• We will also discuss the process involved in ‘K-means Clustering’, the various statistical measures you need to know to implement it in this module.

Topics

• Introduction to Data Mining
• Understanding Machine Learning
• Supervised and Unsupervised Machine Learning Algorithms
• K- means clustering

Project work

• 2 Real-time project

For More Details About Data Science with R Course Click Here! →

If there is any planned absence, always inform us. Absence should always be informed in prior to us and we will do the backup classes for the missed topics only after the course is completed.

Yes. Our trainers will help you throughout the interview process and guide you in each and every stage until you get the job. Our trainers motivate you in each and every stage of your job search. Our trainers also conduct mock interviews for you.

Our trainers are the Data experts who work in top companies in Data Science field and solve real-world data science problems. Our trainers are good at explaining the concepts and help students understand the subject better.

We connect with experts in each field who are mostly working professionals with real-time experience in the relevant field. Our trainers have in-depth knowledge and have more than eight years of experience in the industry. We screen the data professionals with good technical knowledge, training experience, good interpersonal and communication skills. We constantly monitor trainers’ performance and record feedback from each student.

You can keep in touch with our trainers who will help you clear your doubts and queries in the near future of course completion. Our trainers help you post course completion as well.

You will receive Certification for Data Science from Besant technologies after course completion.

We provide classroom and online modes: one to one and batch. We also provide corporate training.

We provide classroom training in both one to one and batch. Online training is also available in one to one and batch modes.

You can attend the free demo class. Kindly contact us directly for attending a demo class with our trainer at our institute. You can walk into any of our branches and we will arrange a demo for you with our trainer.

We are providing very good discounts for group enrolment and corporates training. We have been doing corporate training for a long time. Please contact us for group discounts and corporate training.

We accept cash and accept all the major credit and debit cards for payment. We accept Master, Visa cards. We also accept online payment.

Please contact us for more queries via live chat, call back or quick call inquiry. We will immediately help you with your all queries.